RDF Semantics

Abstract

This is a specification of a precise semantics for RDF and RDFS, with some basic results on entailment. It is intended to be readable by a general technical audience.

Status of this Document

This work is part of the W3C Semantic Web Activity. It has been produced by the RDF Core Working Group which is chartered to address a list of issues raised since RDF 1.0 was issued.

This document is a W3C Working Draft for review by W3C members and other interested parties. It is a draft document and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use W3C Working Drafts as reference material or to cite them as other than "work in progress". A list of current public W3C Working Drafts can be found as part of the W3C Technical Reports and Publications.

Table of Contents

0. Introduction
  0.1 Model-theoretic semantics
  0.2 Graph Syntax
  0.3 Definitions
1. Interpretations
  1.1 Technical notes
  1.2 Urirefs, resources and literals
  1.3 Interpretations
  1.4 Denotations of ground graphs
  1.5 Unlabeled nodes as existential assertions
  1.6 Comparison with formal logic
2. Simple Entailment between RDF graphs
  2.1 Criteria for non-entailment
3. Interpreting the RDF(S) vocabulary
  3.1 RDF interpretations
  3.2 Reification and containers
   3.2.1 Reification
   3.2.2 RDF Containers
  3.3 RDFS Interpretations
   3.3.1 A note on rdfs:Literal
4. Vocabulary entailment and closure rules
  4.1. Rdf-entailment and rdf closures
  4.2. Rdfs-entailment and rdfs closures
  4.3 A note on computing and entailment
Appendix A. Summary of model theory
Appendix B. Proofs of lemmas
Appendix C. Acknowledgements
References
Change Log

0. Introduction

0.1 Specifying a formal semantics

RDF is intended to be used to convey meanings. It is hard to give a precise characterization of what this means, and the semantics given here restricts itself to a formal notion of meaning which could be characterized as the part that is common to all other accounts of meaning. Exactly what is considered to be the 'meaning' of an assertion in RDF in some broad sense may depend on many factors, including social conventions, comments in natural language or links to other content-bearing documents. Most of this meaning will be inaccessible to machine processing and is mentioned here only to emphasize that the formal semantics described here is not intended to provide an analysis of 'meaning' in this broad sense. However, users should operate under the basic assumption that any such meaning is preserved by any formal inference processes which reserve truth in the formal sense, so that any terms in a formally sanctioned conclusion from a set of RDF graphs can be interpreted as carrying the same informal meanings that they had in the original graphs. We note in passing that this condition raises many complex questions about how such informal meanings derived from several sources should be combined, and that these questions are also not addressed by the formal semantics. See @@Jeremy for further discussion.

The chief utility of a formal semantic theory is not to provide any deep analysis of the nature of the things being described by the language or to suggest any particular processing model, but rather to provide a technical way to determine when inference processes are valid, i.e. when they preserve meanings.

We use a basic technique for specifying the formal meaning of a formal language called model-theoretic semantics. This assumes that the language refers to a 'world', and describes the minimal conditions that a world must satisfy in order to assign an appropriate meaning for every expression in the language. A particular world is called an interpretation, so that model theory might be better called 'interpretation theory'. The idea is to provide an abstract, mathematical account of the properties that any such interpretation must have, making as few assumptions as possible about its actual nature or intrinsic structure. Model theory tries to be metaphysically and ontologically neutral. It is typically couched in the language of set theory simply because that is the normal language of mathematics - for example, this semantics assumes that names denote things in a set IR called the 'universe' - but the use of set-theoretic language here is not supposed to imply that the things in the universe are set-theoretic in nature. Model theory is usually most relevant to implementation via the notion of entailment, described later, which makes it possible to define valid inference rules.

In this document we give two versions of the same semantic theory: directly, and also (in an appendix) an 'axiomatic semantics' in the form of a translation from RDF and RDFS into another formal language, L_base [@@Lbase ref?] which has a predefined model-theoretic semantics. The translation technique offers some advantages for machine processing and may be found easier to read by some readers, so is described here as a convenience. We believe that the two descriptions are in exact correspondence, but only the direct model theory in the main body of the document should be taken as normative.

There are several aspects of meaning in RDF which are ignored by this semantics; in particular, it treats URI references as simple names, ignoring aspects of meaning encoded in particular URI forms [RFC 2396] and does not provide any analysis of time-varying data or of changes to URI references.Also, it does not assign any particular meaning to some parts of the RDF and RDFS namespaces, and in some cases, notably the reification and container vocabularies, it assigns less meaning than one might expect. These cases are noted in the text and the limitations discussed in more detail. The semantics treats RDF(S) as a simple assertional language, in which each triple can be used to make a distinct assertion which is not changed by adding other triples. This imposes a fairly strict monotonic discipline on the language, so that it cannot express closed world assumptions, local default preferences, and several other commonly-used non-monotonic constructs.

Particular uses of RDF, including as a basis for more expressive languages such as DAML and OWL, may impose further semantic conditions in addition to those described here, and such extra semantic conditions can also be imposed on the meanings of terms in particular RDF namespaces. We use this convention in later parts of this document. All such extensions must however conform to the semantic conditions in this document. In more operational terms, any entailment which is valid according to the semantics described here must continue to be valid in any extended semantics imposed on an RDF namespace. We also recommend that entailment with respect to a more restricted notion of interpretation should be indicated by the use of a namespace entailment term, as introduced in section @@@ below.

0.2 Graph syntax

Any semantic theory must be attached to a syntax. Of the several syntactic forms for RDF, we have chosen the RDF graph as introduced in RDFMS as the primary syntax, largely for its simplicity. We understand linear RDF notations, whether normative (rdf/xml RDF/XML ) or not (such as N-Triples ) as lexical notations for specifying RDF graphs. There are well-formed graphs that cannot be described by these notations, however. Two RDF documents, in whatever lexical form, are syntactically equivalent if and only if they map to isomorphic RDF graphs. The model theory assigns interpretations directly to the graph; we will refer to this as the 'graph syntax' to avoid ambiguity, since the bare term 'syntax' is often assumed to refer to a lexicalization.

To describe RDF graphs it is first necessary to define the things that can act as nodes and arcs of the graph. There are three kinds of node in an RDF graph: urirefs, blank nodes and literals. A uriref is defined to be an absolute URI reference in the sense of RFC 2396. Blank (unlabeled) nodes are considered to be drawn from some set of 'anonymous' syntactic entities which have no label and are unique to the graph. Two graphs which differ only by having different blank nodes will be considered isomorphic; we will not bother to distinguish between isomorphic graphs. Literals come in several forms. Simple literals consist of a unicode character string or a unicode string paired with a language tag; datatyped literals consist of a unicode character string paired with a uriref which indicates a datatype; and a special class of XML typed literals is distinguished which can also have a lang tag. Finally, every arc in an RDF graph is labelled with a uriref. The same uriref may label several arcs and also be a node in the graph. An RDF graph can then be formally defined as a set of triples of the form <S, P, O>, where P is a uriref, S is either a uriref or a blank node, and O is either a uriref, a blank node, or a literal. The three components of the triple are called respectively the subject, predicate and object of the triple. It is convenient to adopt a familiar abuse of terminology and identify a single triple with the graph consisting of the singleton set containing that triple.

The convention that relates such a set of triples to a picture of an RDF graph can then be stated as follows. Draw one oval for each blank node and uriref, and one rectangle for each literal, which occur in either the S or O position in any triple in the set, and write each uriref or literal as the label of its shape. Then for each triple <S,P,O>, draw an arrowed line from the shape produced from S to the shape produced from O, and label it with P. Technically, this is a picture of a mathematical structure which can be described as a partially labelled directed pseudograph with unique node labels.

We refer to urirefs and literals, but not blank nodes, as names; but note that there is no distinction between the name of a node labelled with a uriref, and the node itself. A name will usually occur in more than one graph, but blank nodes are unique to each graph. This reflects the fact that literals and urirefs are considered to have a 'global' meaning but blank nodes do not. One guiding semantic principle is that each name is interpreted as referring to a thing, while a blank node indicates that something exists without naming any particular thing.

In this document we will use the N-triples syntax described in RDFTestCases to describe RDF graphs. This notation uses a nodeID convention to indicate blank nodes in the triples of a graph. Note that while node identifiers such as _:xxx serve to identify blank nodes in the surface syntax, these expressions are not considered to be the label of the graph node they identify; they are not names, and do not occur in the actual graph. In particular, two N-triples documents which differ only by re-naming their node identifiers will be understood to describe identical RDF graphs.

The N-triples syntax requires that urirefs be given in full, enclosed in angle brackets. In the interests of brevity, we use the imaginary URI prefix 'ex:' to provide "generic" examples. To obtain a more realistic view of the normal appearance of the N-triples syntax, the reader should imagine this replaced with something like 'http://example.org/rdf/mt/artificialExample/'. We will also make extensive use of the Qname prefixes rdf: and rdfs: defined as follows:

Prefix rdf: namespace URI: http://www.w3.org/1999/02/22-rdf-syntax-ns#

Prefix rdfs: namespace URI: http://www.w3.org/2000/01/rdf-schema#

Since Qname syntax is not legal in the N-triples syntax, and in the interests of brevity and readability, we will use the convention whereby a Qname is used without surrounding angle brackets to indicate the corresponding uriref enclosed in angle brackets, eg the triple

<ex:a> rdf:type rdfs:Property .

should be read as an abbreviation for the N-triples syntax

<ex:a> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2000/01/rdf-schema#Property> .

However, the reader is cautioned that this Qname convention is not correct N-triples syntax, will not be accepted by most software processors, and is used in this document only for editorial and orthographic convenience.

In giving generic examples later in the document, we will also use a convention where a simple string of characters without a colon is used to indicate that any uriref or literal can be used in that position. This is also not correct N-triples syntax.

0.3 Graph Definitions

Several definitions will be important in what follows.

A subgraph of an RDF graph is simply a subset of the triples in the graph. Each triple in a graph is considered to be a subgraph.

The result of taking the set-union of two or more RDF graphs (i.e. sets of triples) is another graph, which we will call the merge of the graphs. Each of the original graphs is a subgraph of the merged graph. Notice that when forming a merged graph, two occurrences of a given uriref or literal as nodes in two different graphs become a single node in the union graph (since by definition they are the same uriref or literal), but blank nodes are not 'merged' in this way; and arcs are of course never merged.

Notice that one does not, in general, obtain the merge of a set of graphs by concatenating their corresponding N-triples documents and constructing the graph described by the merged document, since if some of the documents use the same node identifiers, the merged document will describe a graph in which some of the blank nodes have been 'accidentally' merged. To merge Ntriples documents it is necessary to check if the same nodeID is used in two or more documents, and to replace it with a distinct nodeID in each of them, before merging the documents. Similar cautions apply to merging graphs described by RDF/XML documents which contain nodeIDs..

An RDF graph will be said to be ground if it has no blank nodes.

We will refer to a set of urirefs as a vocabulary. The vocabulary of a graph is the set of urirefs that it contains (either as nodes, on arcs or in typed literals). A name is a uriref or a typed literal. A name is from a vocabulary V if it is in V or is a typed literal containing a uriref in V. The names of a graph is the set of names which occur in the graph. This is the set of expressions that need to be assigned a meaning by an interpretation. We do not think of untyped literals as names because their interpretation is fixed by the RDF semantic rules.When giving examples, we will sometimes use a string of characters with no intervening colon to indicate 'some name'.

An instance of an RDF graph is, intuitively, a similar graph in which some blank nodes may have been replaced by urirefs or literals. However, it is technically convenient to also allow blank nodes to be replaced by other blank nodes, so we need to state this rather more precisely. Say that one triple is an instance of another if it can be obtained by substituting zero or more urirefs, literals or blank nodes for blank nodes in the original; and that a graph is an instance of another just when every triple in the first graph is an instance of a triple in the second graph, and every triple in the second graph has an instance in the first graph. Note that any graph is an instance of itself.

This allows blank nodes in the second graph to be replaced by names in the instance (which might cause some nodes to be identified that were previously distinct) but it also allows them to be replaced by other blank nodes. In particular, this means that the two graphs:

<ex:a> <ex:b> _:xxx .

<ex:a> <ex:b> _:yyy .

and

<ex:a> <ex:b> _:zzz .

with, respectively, three nodes and two arcs, and two nodes and one arc, are instances of each other. Similarly,

_:xxx <ex:b> _:xxx .

is an instance of

_:xxx <ex:b> _:yyy .

A proper instance of a graph is an instance in which at least one blank node has been replaced by a name. The above examples are not proper instances.

An instance with respect to a vocabulary V is an instance in which all the names in the instance that were substituted for blank nodes in the original are names from V.

1. Interpretations

1.1 Technical Notes

Throughout this document, the fact that two sets are given different names should not be taken to imply that they are disjoint. We will explicitly state any disjointness or containment conditions as they arise. In the same spirit, the fact that one set is stated to be a subset of another should not be interpreted as saying that these sets cannot be identical, unless this is stated explicitly.(These conventions are normal in mathematical discourse, but may violate the conventions expected by some readers.)

We do not impose any logical restrictions on the domains and ranges of properties; in particular, a property may be applied to itself. When classes are introduced in RDFS, we will allow them to contain themselves. This might seem to violate one of the axioms of standard (Zermelo-Fraenkel) set theory, the axiom of foundation, which forbids infinitely descending chains of membership. However, the semantic model given here distinguishes properties and classes considered as objects from their extensions - the sets of object-value pairs which satisfy the property, or things that are 'in' the class - thereby allowing the extension of a property or class to contain the property or class itself without violating the axiom of foundation. In particular, this use of a class extension mapping allows classes to contain themselves. For example, it is quite OK for (the extension of) a 'universal' class to contain the class itself as a member, a convention that is often adopted at the top of a classification hierarchy. (If an extension contained itself then the axiom would be violated, but that case never arises.) The technique is described more fully in Hayes&Menzel.

In this respect, RDFS differs from many conventional ontology frameworks such as UML which assume a more structured system of 'layers', or draw a distinction between data and meta-data. However, while RDFS does not assume the existence of such structure, it does not prohibit it. RDF allows such loops, but it does not mandate their use for all parts of a user vocabulary.If this aspect of RDFS is found worrying, then it is possible to restrict oneself to a subset of RDF graphs which do not contain any such 'loops' of class membership or property application, and still retain much of the expressive power of RDFS for many practical purposes.

The use of the explicit extension mapping also makes it possible for two properties to have exactly the same values, or two classes to contain the same instances, and still be considered distinct. This means that RDFS classes can be considered to be rather more than simple sets; they can be thought of as 'classifications' or 'concepts' which have a robust notion of identity which goes beyond a simple extensional correspondence. This property of the model theory has significant consequences in more expressive languages built on top of RDF, such as OWL, which are capable of expressing identity between properties and classes directly. This 'intensional' nature of classes and properties is sometimes claimed to be a useful property of a descriptive language, but a full discussion of this issue is beyond the scope of this document.

Notice that the question of whether or not a class contains itself as a member is quite different from the question of whether or not it is a subclass of itself. All classes are subclasses of themselves.

1.2 Urirefs, resources and literals.

RDF uses two kinds of referring expression; urirefs and literals. We make very simple and basic assumptions about these. Urirefs are treated as logical constants, i.e. as names which denote things (the things are called 'resources', following RFC 2396,but no assumptions are made here about the nature of resources.) The meaning of a literal is determined by its character string: it either refers to the value mapped from the string by the associated datatype, or if no datatype is provided then it refers to the string itself.

We do not take any position here on the way that urirefs may be composed from other expressions, e.g. from relative URIs or Qnames; the semantics simply assumes that such lexical issues have been resolved in some way that is globally coherent, so that a single uriref can be taken to have the same meaning wherever it occurs.

We do not make any assumptions about the relationship between the denotation of a uriref and a document or network resource which can be obtained by using that uriref in an HTTP transfer protocol. It has been argued that urirefs in the form of HTTP URIs should be required to denote the document that results from such a retrieval. Such a requirement could be added as an extra semantic condition, but this condition is not assumed in this document. In general, the formal semantics described here is designed to make as few assumptions as possible about the nature of the things referred to by any piece of RDF.

Similarly, the model theory given here has no special provision for tracking temporal changes. It assumes, implicitly, that urirefs have the same meaning whenever they occur. To provide an adequate semantics which would be sensitive to temporal changes is a research problem which is beyond the scope of this document.

1.3 Assertions, meanings and inference

Asserting an RDF graph amounts to claiming that it is true, which is another way of saying that the world it describes is, in fact, so arranged as to be an interpretation which makes it true. In other words, asserting a piece of RDF amounts to asserting a constraint on the possible ways the world might be. Notice that there is no presumption here that any RDF graph contains enough information to specify a single unique interpretation. It is usually impossible to assert enough in any language to completely constrain the interpretations to a single possible world, so there is no such thing as 'the' unique RDF interpretation. In general, the larger a graph is - the more it says about the world - then the smaller the set of interpretations that an assertion of the graph allows to be true - there are fewer ways the world could be, while making the asserted graph true of it.

The use of 'public' URIs in an RDF graph is often taken to imply that an assertion of the graph implicitly assents to the truth of other RDF graphs that define the meaning of that URI. To apply the semantics to this kind of situation, one should think of the assertion of such a graph as amounting to an assertion of the merge of that graph together with whatever RDF graphs are assumed to define the public vocabulary, in order to fully convey the intended meaning of the 'public' assertion.

It has been argued that certain sources of RDF assertions should be taken as more authoritative or more reliable than others, in particular that assertions made by the 'owner' of a uriref should be considered to be definitive in determining the meaning of those urirefs. The semantics given here takes no position on issues like this. Once one has decided what to trust, the semantics tells one what its valid consequences are, but that is all that it can do.

1.4 Interpretations

The following definition of an interpretation is couched in mathematical language, but what it amounts to intuitively is that an interpretation provides just enough information about a possible way the world might be - a 'possible world' - in order to fix the truth-value (true or false) of any ground RDF triple. It does this by specifying for each uriref, what it is supposed to be a name of; and also, if it is used to indicate a property, what values that property has for each thing in the universe; and if it used to indicate a datatype, we assume that the datatype defines a mapping between lexical forms and datatype values. This is just enough information to fix the truth-value of any ground triple, and hence any ground RDF graph.(We will show how to determine the truthvalues of non-ground graphs in the following section.) Notice that if we left any of this information out, it would be possible for some well-formed triple to be left without a determinate value; and also that any other information - such as the exact nature of the things in the universe - would, regardless of its intrinsic interest, be irrelevant to the actual truth-values of any triple.

All interpretations will be relative to a set of urirefs, called the vocabulary of the interpretation; so that one should speak, strictly, of an interpretation of an RDF vocabulary, rather than of RDF itself. Some interpretations may assign special meanings to the symbols in a particular namespace, which we will call a reserved vocabulary. Interpretations which share the special meaning of a particular reserved vocabulary will be named for that vocabulary, so that we will speak of 'rdf-interpretations' , 'rdfs-interpretations', etc.. An interpretation with no reserved vocabulary will be called a simple interpretation, or simply an interpretation. A simple interpretation can be viewed as having an empty reserved vocabulary.

RDF uses several forms of literal. The chief semantic characteristic of literals is that their meaning is determined by the form of the string they contain. In the case of typed literals, however, the full specification of the meaning depends on being able to access the datatype information which is external to RDF itself; for this reason we postpone a full discussion of the meaning of typed literals until later sections, where we introduce a special notion of datatype interpretation. For now, we will assume that each interpretation defines a mapping IL from typed literals to their interpretations, and will impose stronger conditions on IL as the notion of 'interpretation' is extended in later sections. Simple literals, without embedded datatypes, are always interpreted as referring to themselves: either a character string or a pair consisting of two character strings, the second of which is a language tag.

The set of all possible values of all literals is assumed to be a set called LV which is common to all RDF interpretations. Since the set of datatypes is not restricted by RDF syntax, it is impossible to give a sharp definition of LV, but it is required to contain all literal strings and also all pairs consisting of a literal string and a language tag.

We will assume that LV is a subset of IR. This assumption may seem controversial, since it amounts to saying that literal values are resources. We note however that this does not imply that literals should be identified with urirefs.

A simple interpretation I of a vocabulary V is defined by:

1. A non-empty set IR of resources, called the domain or universe of I, which is a superset of LV.

2. A mapping IEXT from a subset IP of IR into the powerset of IR x (IR union LV) i.e. the set of sets of pairs <x,y> with x in IR and y in IR or LV

3. A mapping IS from V into IR

4. A mapping IL from typed literals into LV.

IEXT(x) is a set of pairs which identify the arguments for which the property is true, i.e. a binary relational extension, called the extension of x. This trick of distinguishing a relation as an object from its relational extension allows a property to occur in its own extension, as noted earlier.

In the next sections we give the exact rules for how an interpretation of a vocabulary determines the truth-values of any RDF graph, by a recursive definition of the "denotation" - the semantic "value" - of any RDF expression in terms of those of its immediate subexpressions. RDF has two kinds of denotation: node labels denote things, and sets of triples denote truthvalues.

1.5 Denotations of ground graphs

The denotation of a ground RDF graph in I is given recursively by the following rules, which extend the interpretation mapping I from labels to ground graphs. These rules (and extensions of them given later) work by defining the denotation of any piece of RDF syntax E in terms of the denotations of the immediate syntactic constitutents of E, hence allowing the denotation of any piece of RDF to be determined by a kind of syntactic recursion.

Notice that if the vocabulary of an RDF graph contains urirefs that are not in the vocabulary of an interpretation I - that is, if I simply does not give a semantic value to some name that is used in the graph - then these truth-conditions will always yield the value false for some triple in the graph, and hence for the graph itself. Turned around, this means that any assertion of a graph implicitly asserts that all the names in the graph actually refer to something in the world.

As an illustrative example, the following is a small interpretation for the artificial vocabulary {ex:a, ex:b, ex:c}. We use integers to indicate the 'things' in the universe. This is not meant to imply that RDF interpretations should be interpreted as being about arithmetic, but more to emphasize that the exact nature of the things in the universe is irrelevant.(In this and subsequent examples we use the greater-than and less-than symbols in several ways: following mathematical usage to indicate abstract pairs and n-tuples; following Ntriples syntax to enclose urirefs, and also as arrowheads when indicating mappings. We apologize for any confusion.)

IR = LV union{1, 2};

IEXT: 1->{<1,2>,<2,1>}

IS: ex:a->1, ex:b->1, ex:c->2

IL: any typed literal -> 2

This interpretation makes these triples true:

  <ex:a> <ex:b> <ex:c> .

  <ex:c> <ex:a> <ex:a> .

  <ex:c> <ex:b> <ex:a> .

  <ex:a> <ex:b> "whatever"^^ex:b .

For example, I(<ex:a> <ex:b> <ex:c> .) = true if <I(ex:a),I(ex:c)> is in IEXT(I(<ex:b>)), i.e. if <1,2> is in IEXT(1), which is {<1,2>,<2,1>} and so does contain <1,2> and so I(<ex:a <ex:b> ex:c>) is true. The truth of the fourth literal is a consequence of the rather unusual interpretation chosen here for typed literals; this kind of oddity will be ruled out when we consider datatyped intepretations in section @@.

It makes these triples false:

  <ex:a> <ex:c> <ex:b> .

  <ex:a> <ex:b> <ex:b> .

  <ex:c> <ex:a> <ex:c> .

  <ex:a> <ex:b> "whatever" .

For example, I(<ex:a> <ex:c> <ex:b> .) = true if <I(ex:a),I(<ex:b>)>, i.e.<1,2>, is in IEXT(I(ex:c)); but I(ex:c)=2 and IEXT is not defined on 2, so the condition fails and I(<ex:a> <ex:c> <ex:b> .) = false.

It makes all literals containing an untyped literal false, since the property extension does not map anything to a string.

To emphasize; this is only one possible interpretation of this vocabulary; there are (infinitely) many others. For example, if we modified this interpretation by attaching the property extension to 2 instead of 1, none of the above six triples would be true.

1.6. Unlabeled nodes as existential assertions

Blank nodes are treated as simply indicating the existence of a thing, without using, or saying anything about, the name of that thing. (This is not the same as assuming that the blank node indicates an 'unknown' uriref; for example, it does not assume that there is any uriref which refers to the thing. See http://www.w3.org/2000/03/rdf-tracking/#rdfms-identity-anon-resources for a summary and pointers to further discussions on this issue. The discussion of skolemization in section 2.1 is also relevant.)

We now show how an interpretation can specify the truth-value of a graph containing blank nodes. This will require some definitions, as the theory so far provides no meaning for unlabeled nodes. Suppose I is an interpretation and A is a mapping from some set of unlabeled nodes to the universe IR of I, and define I+A to be an extended interpretation which is like I except that it uses A to give the interpretation of unlabeled nodes. Define anon(E) to be the set of unlabeled nodes in E. Then we can extend the above rules to include the two new cases that are introduced when unlabeled nodes occur in the graph:

Notice that we have not changed the definition of an interpretation; it still consists of the same values IR, IP, IEXT , IS and IL. We have simply extended the rules for defining denotations under an interpretation, so that the same interpretation that provides a truth-value for ground graphs also assigns truth-values to graphs with unlabeled nodes, even though it provides no denotation for the unlabeled nodes themselves. Notice also that the unlabeled nodes themselves are perfectly well-defined entities; they differ from other nodes only in not being assigned a denotation by an interpretation, reflecting the intuition that they have no 'global' meaning (i.e. outside the graph in which they occur).

This effectively treats all blank nodes as having the same meaning as existentially quantified variables in the RDF graph in which they occur. Notice however that that since two unlabeled nodes cannot have the same label, there is no need to specify the scope of the quantifier within a graph, and no need to use any explicit quantifier syntax.( If we were to apply the semantics directly to N-triples syntax, we would need to indicate the quantifier scope, since in this lexicalization syntax the same node identifier may occur several times corresponding to a single blank node in the graph. The above rule amounts to the convention that would place the quantifiers just outside, or at the outer edge of, the N-triple document corresponding to the graph.)

For example, with this convention, the graph defined by the following triples is false in the interpretation shown in figure 1:

  _:xxx <ex:a> <ex:b> .

  <ex:c> <ex:b> _:xxx .

since if A' maps the unlabeled node to 1 then the first triple is false in I+A', and if it maps it to 2 then the second triple is false.

Note that each of these triples, when thought of as a single graph, is true in I, but their conjunction is not; and that if a different nodeID were used in the two triples, indicating that the RDF graph had two blank nodes instead of one, then A' could map one node to 2 and the other to 1, and the resulting graph would be true under the interpretation I.

2. Simple entailment between RDF graphs.

Following conventional terminology, we say that I satisfies E if I(E)=true, and that a set S of expressions (simply) entails E if every interpretation which satisfies every member of S also satisfies E. If the singleton set {E} entails E' then we will simply say that E entails E'. In later sections these notions will be adapted to classes of interpretations with particular reserved vocabularies, but throughout this section entailment should be interpreted as simple RDF entailment.

Entailment is the key idea which connects model-theoretic semantics to real-world applications. As noted earlier, making an assertion amounts to claiming that the world is an interpretation which assigns the value true to the assertion. If A entails B, then any interpretation that makes A true also makes B true, so that an assertion of A already contains the same "meaning" as an assertion of B; we could say that the meaning of B is somehow contained in, or subsumed by, that of A. If A and B entail each other, then they both "mean" the same thing, in the sense that asserting either of them makes the same claim about the world. The interest of this observation arises most vividly when A and B are different expressions, since then the relation of entailment is exactly the appropriate semantic licence to justify an application inferring or generating one of them from the other. Through the notions of satisfaction, entailment and validity, formal semantics gives a rigorous definition to a notion of "meaning" that can be related directly to computable methods of determining whether or not meaning is preserved by some transformation on a representation of knowledge.

Any process or technique which constructs a graph E from some other graphs S is said to be (simply) valid if S entails E, otherwise invalid. Note that being an invalid process does not mean that the conclusion is false, and being valid does not guarantee truth. However, validity represents the best guarantee that any assertional language can offer: if given true inputs, it will never draw a false conclusion from them.

In this section we give a few basic results about simple entailment and valid inference. Simple entailment can be recognized by relatively simple syntactic comparisons. The two basic forms of simply valid proof step in RDF are, in logical terms, the inference from (P and Q) to P, and the inference from (foo baz) to (exists (?x) foo(?x)).

Note, these results apply only to simple entailment, not to the more subtle notions of entailment introduced in later sections. Proofs, all of which are straightforward, are given in appendix B, which also describes some other properties of entailment which may be of interest.

Subgraph Lemma. A graph entails all its subgraphs .

Instance Lemma. A graph is entailed by any of its instances.

The relationship between merging and entailment is simple, and obvious from the definitions:

Merging lemma. The merge of a set S of RDF graphs is entailed by S, and entails every member of S.

This means that a set of graphs can be treated as equivalent to a single graph as far as the model theory is concerned.

The main result for simple RDF inference is:

Interpolation Lemma. S entails a graph E if and only if a subgraph of the merge of S is an instance of E.

The interpolation lemma completely characterizes simple RDF entailment in syntactic terms. To tell whether a set of RDF graphs entails another, find a subgraph of their merge and replace names by unlabeled nodes to get the second. Of course, there is no need to actually construct the merge. If working backwards from the consequent E (the graph that may be entailed by the others), the most efficient technique would be to treat unlabeled nodes as variables in a process of subgraph-matching, allowing them to bind to 'matching' names in the antecedent graph(s) in S, i.e. those which may entail the consequent graph. The interpolation lemma shows that this process is valid, and is also complete if the subgraph-matching algorithm is. The existence of complete subgraph-checking algorithms also shows that RDF is decidable, i.e. there is a terminating algorithm which will determine for any finite set S and any graph E, whether or not S entails E.

Notice however that such a variable-binding process would only be appropriate when applied to the conclusion of a proposed entailment. This corresponds to using the document as a goal or a query, in contrast to asserting it, i.e. claiming it to be true. If an RDF document is asserted, then it would be invalid to bind new values to any of its unlabeled nodes, since the resulting graph would not be entailed by the assertion, as explained in the next section.

It might be thought that the operation of changing a bound variable would be an example of an inference which was valid but not covered by the interpolation lemma, e.g. the inference of

_:x <ex:a> <ex:b> .

from

_:y <ex:a> <ex:b> .

Recall however that by our conventions, these two expressions describe identical RDF graphs.

Finally, the following is a trival consequence of the definition of entailment, but it may be worth stating explicitly since many implemented systems fail to satisfy it:

Monotonicity Lemma. Suppose S is a subgraph of S' and S entails E. Then S' entails E.

2.1 Criteria for non-entailment

Notice that unlabeled nodes are not identified with other nodes in a merge, and indeed this reflects a basic principle of RDF graph inference: in contrast to names, which have a global identity which carries across all graphs, blank nodes should not be identified with other nodes or re-labeled with urirefs, in order to ensure that the resulting graph is entailed by what one starts with. To state this condition precisely, we need to first exclude a counterexample. It is possible for a graph to contain two triples one of which is an instance of the other, for example:

<ex:a> <ex:b> _:xxx .

<ex:a> <ex:b> <ex:c> .

Such an internally redundant graph is equivalent to one of its own instances, since replacing the blank node by <ex:c> would result in a single-triple graph which is a subgraph of the original. To rule out such cases of internal redundancy, we will say that an RDF graph is lean if none of its triples is a proper instance of any other. Then the above principle is made precise in the following two lemmas concerning criteria for non-entailment:

Anonymity lemma 1. Suppose E is a lean graph and E' is a proper instance of E. Then E does not entail E'.

Anonymity lemma 2. Suppose that E is a lean graph and that E' is like E except that two distinct unlabeled nodes in E have been identified in E'. Then E does not entail E'.

This means that there is no valid RDF inference process which can produce an RDF graph in which a single unlabeled node occurs in triples originating from several different graphs. (Of course, such a graph can be constructed, but it will not be entailed by the original documents. An assertion of such a graph would reflect the addition of new information about the identity of two unlabeled nodes.)

We emphasise again that these results apply only to simple entailment, not to the namespace entailment relationships defined in rest of the document.

3. Interpreting the RDF(S) vocabulary

So far we have considered only the model theory of what might be called the logical form of the RDF graph itself, without imposing any special interpretations on any reserved vocabulary. In the rest of the document we will extend the model theory to describe the semantic conditions reflecting the intended meanings of the rdf: and rdfs: namespaces. Some of these interpretations diverge in some ways from the meanings described in RDFMS and RDFSchema , and we will note these differences as they arise.

Although we will do this in stages, the same general technique is used throughout. First we describe a reserved vocabulary, i.e. a set of urirefs which will be given a special meaning; then we give the extra conditions on an interpretation which capture those meanings; then we restrict the notions of satisfiability and entailment to apply to these interpretations only. This essentially imposes an a priori restriction on the world being described that it satisfy the extra conditions. The new semantic conditions are automatically assumed to be true; an interpretation which would violate them is simply not allowed to count as a possible world.

Since there are now many distinct notions of interpretation, entailment and satisfiability, we use the Qname namespace prefixes to identify the various distinctions, eg an rdf-interpretation is an interpretation satisfying the rdf semantic conditions, rdf-entailment means entailment relative to such interpretations, and so on.We call this general idea vocabulary entailment , i.e. entailment relative to a set of interpretations which satisfy extra semantic conditions on a reserved vocabulary. Vocabulary entailment is more powerful than simple entailment, in the sense that a given set of premises entails more consequences. In general, as the reserved vocabulary is increased and extra semantic conditions imposed, the class of satisfying interpretations is restricted, and hence the corresponding notion of entailment becomes more powerful. For example, if S simply entails E then it also rdf-entails E, since every rdf-interpretation is also a simple interpretation; but S may rdf-entail E even though it does not simply entail it. Intuitively, a conclusion may follow from some of the extra assumptions incorporated in the semantic conditions imposed on the reserved vocabulary.

Another way of expressing this is that any restriction on interpretations decreases the number of possible ways that an interpretation might be a counterexample to E's following from S.

Simple entailment is the vocabulary entailment of the empty vocabulary. It is therefore the weakest form of RDF entailment, which holds for any reserved vocabulary; it is the entailment which depends only on the basic logical form of RDF graphs, without making any further assumptions about the meaning of any urirefs.

We will consider the syntactic criteria for recognizing vocabulary entailment in the next section.

3.1. RDF Interpretations

Consider the following (rather small) reserved vocabulary, which we will call rdfV:

An rdf-interpretation of a vocabulary V is an interpretation I on (V union rdfV) which satisfies the following extra conditions:

This forces every rdf interpretation to contain a thing which can be interpreted as the 'type' of properties. (The second condition could be regarded as defining IP to be the set of resources in the universe of the interpretation which have the value I(rdf:Property) of the property I(rdf:type). This way of construing subsets of the universe will be central in interpretations of RDFS.)

For example, the following rdf-interpretation extends the simple interpretation in figure 1:

IR = {1, 2, T }; IP = {1, T}

IEXT: 1->{<1,2>,<2,1>}, T->{<1,P>,<T,P>}

IS: ex:a -> 1, <ex:b> ->1, ex:c -> 2, rdf:type->T, rdf:Property->P

This is not the smallest rdf-interpretation which extends the earlier example, since we could have made I(rdf:Property) be 2 and IEXT(T) be {<1,2>,<T,2>}, and managed without having P in the universe. In general, a given entity in an interpretation may play several 'roles' at the same time, as long as this can be done without violating any of the required semantic conditions.

It is important to note that every rdf-interpretation is also a simple interpretation.The 'extra' structure does not prevent it acting in the simpler role.

3.2. Reification and containers

RDF provides vocabularies which are intended for use in describing containers and bounded collections, and a reification vocabulary to enable an RDF graph to describe, as well as exhibit, triples. Although these vocabularies have reasonably clear informally intended conventional meanings, we do not impose any extra formal semantic conditions on them, so the notions of rdf-entailment and rdf-interpretation apply to them without further change. They are discussed here in order to explain both the intuitive meanings intended and recommended, but also to note the intuitive consequences which are not supported by the formal model theory. When the RDFS vocabulary is added, this part of the RDF vocabulary will have nontrivial consequences; but until then, it can be added to the RDF vocabulary without needing any further semantic conditions.

The lack of a formal semantics for these vocabularies does not reflect any technical semantic problems, but rather is a design decision to make it easier to implement RDF reasoning engines which can check formal RDF entailment. Since no extra formal semantic conditions are imposed in these vocabularies, they are not considered to be restricted vocabularies, and RDF applications are free to impose their own restrictions. There are however clear recommendations on such extra restrictions, noted in each case..

3.2.1 Reification

The RDF reification vocabulary consists of a class name and three property names.

The recommended intended interpretation of these are that a triple of the form

aaa rdf:type rdf:Statement .

is true in I just when I(aaa) is a token of an RDF triple in some RDF document. The three properties, when applied to such a denoted triple, have the same values as the respective components of that triple.

This may be illustrated by considering the following two RDF graphs, the first of which consists of a single triple.

<ex:a> <ex:b> <ex:c> .

and

_:xxx rdf:type rdf:Statement .
_:xxx rdf:subject <ex:a> .
_:xxx rdf:predicate <ex:b> .
_:xxx rdf:object <ex:c> .

The second graph is called a reification of the triple in the first graph, and the node which is intended to refer to the first triple - the blank node in the second graph - is called, rather confusingly, a reified triple. (This can be a blank node or a uriref.) The intended interpretation of the reification vocabulary is that the reification graph would be made true in I by interpreting the reified triple to refer to the triple in the first graph, and using I to interpret that triple, so that the subject, predicate and object of that triple are interpreted in the same way in the reification as in the reified triple. Formally, <x,y> is in IEXT(I(rdf:subject)) just when x is an occurence of an RDF triple with the form

aaa bbb ccc .

and y is I(aaa); similarly for predicate and object. Notice that the value of the rdf:subject property is not the subject uriref itself but its interpretation, and so this condition involves a two-stage interpretation process: we have to interpret the reified node - the subject of the triples in the reification - to refer to another triple, then treat that triple as RDF syntax and apply the interpretation mapping again to get to the referent of its subject. This requires triple tokens to exist as first-class entities in the universe IR of an interpretation.

We emphasize that the intended interpretation here is that the reified triple that the reification describes - I(_:xxx) in the above example - is a particular token or instance of a triple in a particular RDF document, rather than an 'abstract' triple considered as a grammatical form. It is an object in a document rather than an object in an abstract graph. There could be several such entities which have the same subject, predicate and object properties. Although a graph is defined as a set of triples, several such tokens with the same triple structure might occur in different documents. Thus, it would be meaningful to claim that the blank node in the second graph above does not refer to the triple in the first graph, but to some other triple with the same structure. The decision to use this particular interpretation of reification was made on the basis of use cases where properties such as dates of composition or provenance information have been applied to the reified triple, which are meaningful only when thought of as referring to a particular instance or token of a triple.

Although RDF applications may use reification to refer to triples in RDF documents, the connection between the document and its reification must be maintained by some means external to RDF. RDF syntax provides no means to 'connect' an RDF triple to its reification. Since an assertion of a reification of a triple does not implicitly assert the triple itself, this means that there are no entailment relationships which hold between a triple and a reification of it. Thus the reification vocabulary has no effective semantic constraints on it, other than those that apply to an RDF interpretation. The chief facts that are worthy of note about RDF reification, in fact, are examples of non-entailments.

A reification of a triple does not entail the triple, and is not entailed by it. (The reason for first is clear, since the reification only asserts that the triple exists, not that it is true. The second non-entailment is a consequence of the fact that asserting a triple does not automatically assert that any triples exist in the universe being described by the triple. For example, the triple might be part of an ontology describing animals, which could be satisfied by an interpretation in which the universe contained only animals.)

Since the relation between triples and reifications of triples in any RDF graph or graphs need not be one-to-one, asserting a property about some entity described by a reification need not entail that the same property holds of another such entity, even if it has the same components. For example,

_:xxx rdf:type rdf:Statement .
_:xxx rdf:subject <ex:subject> .
_:xxx rdf:predicate <ex:predicate> .
_:xxx rdf:object <ex:object> .
_:yyy rdf:type rdf:Statement .
_:yyy rdf:subject <ex:subject> .
_:yyy rdf:predicate <ex:predicate> .
_:yyy rdf:object <ex:object> .
_:xxx <ex:property> <ex:foo> .

does not entail

_:yyy <ex:property> <ex:foo> .

3.2.2 RDF containers

RDF provides vocabularies for describing three classes of containers. A container is an entity whose 'members' are thought of as the values of properties, each of which relates a particular 'position' in the container to the entity, if there is one, which is 'at' that position. (The rdfs vocabulary, described below, adds a generic membership property which holds regardless of position, and a class containing all the membership properties.)

One should understand this RDF vocabulary as describing containers, rather than as a vocabulary for constructing them, as would typically be supplied by a programming language. On this view, the actual containers are entities in the semantic universe, and RDF graphs which use the vocabulary simply provide very basic information about these entities, enabling an RDF graph to characterize the container type and give partial information about the members of a container. Since the RDF container vocabulary is so limited, many 'natural' assumptions concerning RDF containers are not formally sanctioned by the RDF model theory. This should not be taken as meaning that these assumptions are false, but only that RDF does not formally entail that they must be true.

There are no special semantic conditions on the container vocabulary: the only 'structure' which RDF presumes its containers to have is what can be inferred from the use of this vocabulary and the semantic conditions on the rest of the RDF vocabulary. Since the membership properties rdf:_1, rdf:_2, ... are implicitly ordered by their very names, that order can be thought of as an ordering of the elements of the container. This implicit ordering of members of a container applies to all three kinds of container, even though bags are normally thought of as unordered. RDF does not support any entailments which could arise from re-ordering the elements of an rdf:Bag. For example,

_:xxx rdf:type rdf:Bag .
_:xxx rdf:_1 <ex:a> .
_:xxx rdf:_2 <ex:b> .

does not entail

_:xxx rdf:_1 <ex:b> .
_:xxx rdf:_2 <ex:a> .

Notice that if this conclusion were valid, then the result of conjoining it to the original graph would also be a valid entailment, which would assert that both elements were in both positions. (This is a consequence of the fact that RDF is a purely assertional language.)

There is no assumption that a property of a container applies to any of the elements of the container, or that if a property applies to a container then the property applies to any of the members of the container, or vice versa. There is no requirement that the three container classes are disjoint, so that for example something can be asserted to be both an rdf:Bag and an rdf:Seq. There is no assumption that containers are gap-free, so that for example

_:xxx rdf:type rdf:Seq.
_:xxx rdf:_1 <ex:a> .
_:xxx rdf:_3 <ex:c> .

does not entail

_:xxx rdf:_2 _:yyy .

There is no way in RDF to 'close' a container, i.e. to assert that it contains only a fixed number of members. This is a reflection of the fact that it is always consistent to add a triple to a graph asserting a membership property of any container. And finally, there is no built-in assumption that an RDF container has only finitely many members.

The informal purpose of the three container types is to allow applications to encode the various intentions or expectations about different kinds of containers. Sequences are thought of as totally ordered, bags as unordered (that is, equivalent under re-orderings) and rdf:Alt containers are intended to convey a series of alternative values of a property, which an application can choose from. However, these informal interpretations are not reflected in any RDF entailments. In particular, a triple with a rdf:Alt as a subject or object should not be thought of as an encoding of a logical disjunction.

3.2.3 Collections

RDF provides a vocabulary for describing collections, ie.'list structures' in terms of head-tail links. Collections differ from containers in allowing branching structure and in having an explicit terminator, allowing applications to determine the exact set of items in the collection.

As with containers, no special semantic conditions are imposed on this vocabulary. It is intended for use typically in a context where a 'well-formed' container is described using blank nodes to connect a sequence of items, each described by three triples of the form

_:c1 rdf:type rdf:List .
_:c1 rdf:first aaa .
_:c1 rdf:rest _:c2

where the final item is indicated by the use of rdf:nil as the value of the property rdf:rest. In a familiar convention, rdf:nil can be thought of as the empty collection. Clearly, any such graph amounts to an assertion that the collection, and all its sub-collections, exist, and since the members of the collection can be determined by inspection, this is often sufficient to enable applications to determine what is meant. Note however that the semantics does not require any collections to exist other than those mentioned explicitly in a graph (and the empty collection). For example, the existence of a collection containing two items does not automatically guarantee that the similar collection with the items permuted also exists:

_:c1 rdf:type rdf:List .
_:c1 rdf:first <ex:aaa> .
_:c1 rdf:rest _:c2
_:c2 rdf:type rdf:List .
_:c2 rdf:first <ex:bbb> .
_:c2 rdf:rest rdf:nil .

does not entail

_:c3 rdf:type rdf:List .
_:c3 rdf:first <ex:bbb> .
_:c3 rdf:rest _:c4
_:c4 rdf:type rdf:List .
_:c4 rdf:first <ex:aaa> .
_:c4 rdf:rest rdf:nil .

Also, RDF imposes no 'wellformedness' conditions on the use of this vocabulary, so that it is possible to write RDF graphs which assert the existence of highly peculiar objects such as lists with forked or non-list tails, or multiple heads:

_:666 rdf:type rdf:List .
_:666 rdf:first <ex:aaa> .
_:666 rdf:first <ex:bbb> .
_:666 rdf:rest <ex:ccc> .
_:666 rdf:rest _:777 .
_:777 rdf:type rdf:List .
_:666 rdf:rest rdf:nil .

As this example shows, it is also possible to write a set of triples which underspecify a collection (_:777 in the example) by failing to specify its rdf:rest property value. Extensions and applications of RDF can place their own well-formedness restrictions on the use of this vocabulary. We recommend that any semantic extension to RDF retains the convention that the subject of a 'linked' collection of three-triple items of the form described above, ending with an item ending with rdf:nil, should always describe a linear sequence whose members are the denotations of the rdf:first values of the items, in the sequence got by tracing the rdf:rest properties from the list to rdf:nil.

3.3. RDFS interpretations

RDF Schema RDFSchema extends RDF to include a larger reserved vocabulary rdfsV with more complex semantic constraints:

(rdfs:seeAlso, rdfs:isDefinedBy and rdfs:label are omitted, as the model theory places no constraints on their meanings.)

Although not strictly necessary, it is convenient to state the RDFS semantics in terms of a new semantic construct, a 'class', i.e. a resource which represents a set of things in the universe which have the same value of the rdf:type property. We will define a mapping ICEXT (for the Class Extension in I) from classes to their extensions, in terms of the relational extension of rdf:type, as follows:

ICEXT(x) = {y | <y,x> is in IEXT(I(rdf:type)) }

An rdfs-interpretation of V is a simple interpretation of (V union rdfsV) which satisfies the following semantic conditions, and satisfies all the triples in the subsequent table, which we will call axiomatic triples. The first condition can be understood as a definition of ICEXT and hence of IC, the set of classes.

The truth of the axiomatic triples could be stated as conditions on IEXT and ICEXT, but it is convenient to use the truth-of-triples formulation. Similarly, the conditions on IC and IP in the first table could be stated as axiomatic triples with property rdf:type and objects rdfs:Class and rdfs:Property respectively.

The semantics given here for rdfs:range and rdfs:domain do not entail that superclasses of domains or ranges of a property must also be domains and ranges of that property. For some purposes it may be more convenient to phrase the semantic conditions as 'iff' conditions, which would require imposing this as an extra condition on the meanings of rdfs:range and rdfs:domain. This extra condition will not effect any class-membership entailments on the elements of the domains and ranges of the property. The semantics given here was chosen because it is sufficient for all normal uses of these terms, allows some subtleties in class reasoning, and places a lesser burden on implementors.

The IEXT condition on rdf:Property in an rdf-interpretation is equivalent to the ICEXT condition on rdf:Property above; so these clearly imply all the conditions on an rdf-interpretation. It follows that any rdfs-interpretation is also an rdf-interpretation of the same vocabulary.

We will not attempt to give a pictorial diagram of an rdfs-interpretation.

3.3.1 A note on rdfs:Literal

The semantic conditions on rdfs-interpretations do not include the condition that ICEXT(I(rdfs:Literal)) must be a subset of LV. While this would seem to be required for conformance with RDFMS, there is no way to impose this condition by any RDF assertion or syntactic closure rule. This limitation is due to the fact that RDF does not allow literals to occur in the subject position of a triple, so there are severe restrictions on what can be said about literals in RDF. Similarly, while properties may be asserted of the the class rdfs:Literal, none of these can be validly transferred to literals themselves.

For example, a triple of the form

<ex:a> rdf:type rdfs:Literal .

is consistent even though 'ex:a' is a uriref rather than a literal. What it says is that I(ex:a) is a literal value, ie that the uriref 'ex:a' denotes a literal value. There is however no way in current RDF to specify exactly which literal value it denotes.

Note that the rules for simple entailment described earlier guarantee that any triple containing a literal object entails a similar triple with a bnode as object:

<ex:a> <ex:b> "10" .

entails

<ex:a> <ex:b> _:xxx .

This means that literal denotes 'something', which could therefore also be named, at least in principle, by a uriref.

3.4 Datatyped interpretations

We will assume that a datatype is identified by a uriref and itself defines a set of lexical forms and a mapping from that set to a set of values. Exactly how these are defined is a matter external to RDF, but this is the minimal structure required in order to state a semantics. In operational terms, a reasoning engine would require that the uriref of a datatype provides access to a process which can determine, for any character string, whether or not it is a valid lexical form for that datatype, and for any two such valid character strings, whether or not they map to the same value under the lexical-to-value mapping. It may also use information about the identity of datatype values from different datatypes, if that information is available.

RDF has one 'built-in' datatype rdfs:XMLLiteral which is provided in order to identify valid XML lexical forms.The valid lexical forms for rdfs:XMLLiteral are all character sequences which parse as valid XML, and the value space is the set of valid XML parse structures; with the extra provision that if the literal itself has a lang tag, then that tag is applied throughout the XML parse structure according to the rules for applying XML lang tags.

Since the set of possible datatypes is open-ended, we will assume that datatype interpretations are defined relative to a particular set of datatypes, and refer to D-interpretations where D is some set of datatypes, which we will call recognized datatypes. A 'datatype-aware' RDF engine should be competent to recognize the rdfs:XMLLiteral datatype and the set of all the XML Schema datatypes; we will call this set XSD. We will use the Qname prefix xsd: to refer to XML Schema datatypes in examples.

We will assume that there is a global mapping L2V from datatypes to their lexical-to-value mappings; the valid lexical forms of a datatype d constitute the domain of L2V(d), and the value spaces of d is the range of L2V(d).

A D-interpretation I of a graph G is an rdfs-interpretation of V, where V contains the vocabulary of G and which satisfies the following extra conditions:

The second condition says that the meaning of any typed literal which uses a 'recognized' datatype is the value of the literal character string under that datatype. For example, if I is an XSD-interpretation then I("15"^^xsd:Integer) must be the number fifteen. Notice that this applies only to datatypes in D; typed literals whose type is not a recognized datatype are treated as before, i.e. as denoting some unknown thing. This means that their meanings can be further restricted by adding a suitable extra datatype to the set of recognized datatypes.

The third condition makes explicit how to interpret an 'ill-formed' typed literal, i.e. one where the literal string is not in the lexical space of the datatype. Intuitively, such a name does not denote any value, but in order to avoid the semantic complexities which arise from empty names, we require such a typed literal to denote a special value. Thus for example, if D contains the XML schema datatypes, then I("arthur"^^xsd:integer) = <'badliteral', 'arthur', I(xsd:integer)>. Although this will usually mean that the containing triple is false when interpreted intuitively, it is unwise to draw conclusions from such a badly typed literal. Datatype-aware RDF reasoners should post an error condition when such literals are found. Similarly, such reasoners should post an error condition when they are unable to access the relevant datatype information by using a uriref which has been asserted to denote an instance of the class rdfs:Datatype.

The final condition indicates that RDF uses a datatype uriref in two ways: as a name for the datatype itself, and (when used as a class name) to indicate the class containing the elements of the value space of the datatype.

Users should take care not to identify the value space of a datatype with the class of its members. For example, the XML schema spec [XMLS] allows primitive datatypes whose elements are considered unequal as elements of the value space but which are identical when viewed as class members. RDF does not impose any identity conditions on elements in value spaces, nor assume any subclass relationships between datatype value classes. Information about such relationships should be obtained from the specifications of the datatypes themselves. Similarly, RDF does not assume that its literal strings are identical to elements of the class xsd:string, even though both are defined as sequences of unicode characters. Users may wish to make such identifications, but are cautioned that other users may disagree with any such claims or assumptions.

The treatment of unknown types provides a trivial proof of the following lemma:

Datatype monotonicity lemma. If D is a subset of D' and S D-entails E, then S D'-entails E.

This semantics for datatypes is minimal. It makes no provision for assigning a datatype to the range of a property, for example, and does not provide any way of explicitly asserting that a blank node denotes a particular value under a datatype mapping. There are several technical difficulties in extending this to a broader class of datatype usages while also preserving the simple 'core' of RDF. We expect that the datatyping machinery will be extended in later versions of RDF.

4. Vocabulary entailment and closure rules

We will say that S rdf-entails E (S rdfs-entails E, S D-entails E) when every rdf-interpretation (every rdfs-interpretation, every interpretation datatyped with respect to D) which satisfies every member of S also satisfies E. This follows the wording of the definition of simple entailment in section 2, but refers only to rdf- , rdfs- or D-interpretations instead of all simple interpretations. These are examples of vocabulary entailment, i.e. entailment relative to a set of interpretations which satisfy extra semantic conditions on a reserved vocabulary.

It is easy to see that the lemmas in section 2 do not hold for vocabulary entailment. For example, the triple

rdf:type rdf:type rdf:Property .

is true in every rdf-interpretation, and hence rdf-entailed by the empty set, which immediately contradicts the interpolation lemma for rdf-entailment.

Rather than develop a separate theory of the syntactic conditions for recognising entailment for each reserved vocabulary, we will use a general technique for reducing these broader notions of entailment to simple entailment, by defining the closure of an RDF graph relative to a set of semantic conditions. The basic idea is to rewrite the semantic conditions as a set of syntactic inference rules, and define the closure to be the result of applying those rules to exhaustion. The resulting graphs will contain RDF triples which explicitly state all the special meanings embodied in the extra semantic conditions, in effect axiomatizing them in RDF itself. A graph rdf-entails (rdfs-entails) another just when its rdf-closure (rdfs-closure) simply entails it. It is not possible to provide such a tight result for D-entailment closures since the relevant semantic conditions require identities which cannot be stated in RDF.

The notion of closure used here is purely a formal device to relate two notions of entailment. We do not mean to suggest that closure rules should be used as a computational technique, or that actually generating the full closure would be the best process to use in order to determine vocabulary entailment. Implementors who wish to check any kind of entailment should use a process which is optimised for the combinatorics of the particular set of use cases that are most likely to arise in a given application area. In many cases it may be more efficient to use a process of backchaining on the closure rules, for example.

Closure rules correspond directly to implication axioms in the L_base translation given in appendix @@.

4.1 Rdf-entailment and rdf closures

The rdf-closure of an RDF graph E is the graph gotten by adding triples to E according to the following (very simple) rules:

1. Add the following triple (which is true in any rdf-interpretation):

rdf:type rdf:type rdf:Property .

2. Apply the following rule recursively to generate all legal RDF triples (i.e. until none of the rules apply or the graph is unchanged.) Here xxx and yyy stand for any uriref, bNode or literal, aaa for any uriref.

(This rule will generate the triple mentioned in 1 in two steps from any RDF triple; nevertheless, we mention the triple explicitly since it is required to be in the closure of even an empty set of graphs.)

The following lemma is the basic result on rdf-entailment, and illustrates a general pattern of how to characterize vocabulary entailment syntactically.

RDF entailment lemma. S rdf-entails E if and only if the rdf-closure of the merge of S simply entails E.

For proof, see appendix B.

4.2 RDFS-entailment and RDFS closures

RDFS closures require more complex rules to reflect the consequences of the more elaborate semantic constraints on the rdfs reserved vocabulary. For example, the fact that the subset relationship is transitive means that two subClassOf assertions may entail a third, different, triple. Again, we emphasize that these closure rules are not being recommended as an efficient computational process.

The rdfs-closure of an RDF graph E is the graph gotten by adding triples to E according to the following rules:

1. Add the following triples, which are true in any rdfs-interpretation. These can be read off from the semantic conditions and axiomatic triples in section 3.3. (There are several other triples which are true in every rdfs-interpretation, but they will be generated from these by other rules.)

rdfs:Resource rdf:type rdfs:Class .
rdfs:Literal rdf:type rdfs:Class .
rdfs:Datatype rdf:type rdfs:Class .
rdfs:XMLLiteral rdf:type rdfs:Class .
rdfs:Class rdf:type rdfs:Class .
rdf:Property rdf:type rdfs:Class .
rdf:Seq rdf:type rdfs:Class .
rdf:Bag rdf:type rdfs:Class .
rdf:Alt rdf:type rdfs:Class .
rdf:Statement rdf:type rdfs:Class .
rdf:nil rdf:type rdf:List .

rdf:type rdf:type rdf:Property .
rdf:type rdfs:domain rdfs:Resource .
rdf:type rdfs:range rdfs:Class .

rdfs:domain rdf:type rdf:Property .
rdfs:domain rdfs:domain rdf:Property .
rdfs:domain rdfs:range rdfs:Class .

rdfs:range rdf:type rdf:Property .
rdfs:range rdfs:domain rdf:Property .
rdfs:range rdfs:range rdfs:Class .

rdfs:subPropertyOf rdf:type rdf:Property .
rdfs:subPropertyOf rdfs:domain rdf:Property .
rdfs:subPropertyOf rdfs:range rdf:Property .

rdfs:subClassOf rdf:type rdf:Property .
rdfs:subClassOf rdfs:domain rdfs:Class .
rdfs:subClassOf rdfs:range rdfs:Class .

rdf:subject rdf:type rdf:Property .
rdf:subject rdfs:domain rdf:Statement .

rdf:predicate rdf:type rdf:Property .
rdf:predicate rdfs:domain rdf:Statement .

rdf:object rdf:type rdf:Property .
rdf:object rdfs:domain rdf:Statement .

rdf:first rdf:type rdf:Property .
rdf:first rdfs:domain rdf:List .

rdf:rest rdf:type rdf:Property .
rdf:rest rdfs:domain rdf:List .
rdf:rest rdfs:range rdf:List .

rdf:ContainerMembershipProperty rdfs:subClassOf rdfs:Property .
rdfs:XMLLiteral rdfs:subClassOf rdfs:Literal .
rdfs:Datatype rdfs:subClassOf rdfs:Literal .
rdfs:Literal rdfs:subClassOf rdf:Resource .

2. Add all triples of the following forms, which are true in any rdfs-interpretation. This is an infinite set because the RDF container vocabulary is infinite. However, since none of these triples entail any of the others, it is only necessary, in practice, to add the triples which use those container properties which actually occur in any particular graph or set of graphs in order to check the rdfs-entailment relation between those graphs.

rdf:_1 rdf:type rdfs:ContainerMembershipProperty .
rdf:_2 rdf:type rdfs:ContainerMembershipProperty .
...

3. Apply the following rules recursively to generate all legal RDF triples (i.e. until none of the rules apply or the graph is unchanged.) Here, xxx, yyy and zzz stand for any uriref, bNode or literal, aaa for any uriref, and uuu for any uriref or bNode (but not a literal).

Unlike the simpler rdf closure rules, the outputs of some of these rules may trigger others. For example, these rules will generate the complete transitive closures of all subclass and subproperty heirarchies, together with all of the resulting type information about everything which can be inferred to be a member of any of the classes, and will propagate all assertions in the graph up the subproperty heirarchy, re-asserting them for all super-properties.They will generate all assertions of the form

xxx rdf:type rdfs:Resource .

for every xxx in V, and of the form

xxx rdfs:subClassOf rdfs:Resource .

for every class name; and several more 'universal' facts, such as

rdf:Property rdf:type rdfs:Class .

rdf:Property rdfs:subClassOf rdfs:Resource .

However, it is easy to see that (with the restriction noted of the infinite sets to those membership properties which occur in the graph) the rules will indeed terminate on any finite RDF graph, since there are only finitely many triples that can be formed from a given finite vocabulary.

A similar result applies here as in the case of rdf-entailment, though it takes considerably longer to prove:

RDFS Entailment Lemma. S rdfs-entails a graph E if and only if the rdfs-closure of the merge of S simply entails E.

We note in passing that the stronger 'iff' semantic conditions on rdfs:domain and rdfs:range mentioned in section 3.3 would be captured by adding the additional rules

and that these would provide a redundant inference path to the conclusions of rdfs 2 and 3.

4.3 Datatype entailments

In order to capture datatype entailment in terms of assertions and closure rules, the rules need to refer to information about identity supplied by the datatypes themselves; and to state the rules it is necessary to assume syntactic conditions which can only be checked by consulting the datatype sources. Since such questions are beyond the scope of RDF, it is impossible to prove an entailment lemma for datatype closures.

The following rules are valid in every D-interpretation, provided that ddd indicates a datatype in D, sss and ttt are character strings which are both valid lexical forms for that datatype, and for the second rule that sss and ttt are mapped into the same value by the datatype (or group of datatypes).

In each case, _:xxx is a new bnode, i.e. one that does not appear elsewhere in the graph.

These are as close as one can get in RDF to asserting that the typed literal denotes a literal value and that literals which map into the same values are equal. Notice that it would be invalid to make these inferences without checking that the literal string sss is in the lexical space of the datatype, so these cannot be considered valid rdfs entailments.

These rules do not support any entailments based on identity between values of different datatypes. An obvious generalization of the second rule would permit such conclusions, but questions of identity between items in value spaces of two different datatypes should be referred to the owners of the datatypes in D.

Appendix A: Summary of model theory@@not yet fixed

Appendix B: Proofs of lemmas.

Subgraph Lemma. A graph entails all its subgraphs.

Proof. Obvious, from definitions of subgraph and entailment. If the graph is true in I then for some A, all its triples are true in I+A, so every subset of triples is true in I. QED

Instance Lemma. A graph is entailed by all its instances.

Proof. Suppose I satisfies E' and E' is an instance of E. Then for some mapping A on the blank nodes of E', I+A satisfies every triple in E'. For each blank node b in E, define B(b)=I+A(c), where c is the blank node or name that is substituted for b in E', or c=b if nothing was substituted for it. Then I+B(E)=I+A(E')=true, so I satisfies E. But I was arbitrary; so E' entails E. QED.

If an instance of a graph E' is a subgraph of another graph E then E entails E'; this follows from the subgraph and instance lemmas. As we show below, this is in fact a necessary as well as sufficient condition for entailment, so it is useful to give a name to the syntactic condition that captures non-entailment. Say that a graph E' is separable from a graph E if no instance of E' is a subgraph of E. In particular, a ground graph is separable from E just when it is not a subgraph of E, and a ground triple is separable just in case it isn't in the graph. Graphs which are not separable from E are entailed by E; but for all others, there is a way to arrange the world so that they are false and E true.

For ground graphs, the subgraph lemma can be strengthened to provide simple necessary and sufficient conditions for entailment.

Conjunction Lemma.If E is ground, then I satisfies E if and only if it satisfies every triple in E.

Proof. Obvious, from definition of denotation for ground graphs. QED

Plain Subgraph Lemma. If E and E' are ground, then E entails E' if and only if E' is a subgraph of E.

Proof. 'If' follows directly from subgraph lemma; 'only if' follows from previous lemma and definition of entailment. QED

Herbrand Lemma. Any RDF graph has a satisfying interpretation.

Proof. We will construct the interpretation from the graph, by providing 'just enough' entities and extensions to make the graph true. Since the exact nature of the things in the universe is irrelevant, it is convenient to use the nodes of the graph themselves as their own denotations. (That was Herbrand's idea.)

The universe of I is defined to be the set of names and blank nodes in the graph.

Define IS to be the identity mapping on the vocabulary of the graph, and IEXT as follows: <x,y> is in IEXT(z) just when there is a triple in the graph of the form x z y . Define the mapping A to be the identity mapping on blank nodes of the graph.

Clearly I satisfies all ground triples in the graph, and I+A satisfies the entire graph; so I satisfies the graph. QED

An interpretation constructed in this way, so that the IS mapping is the identity mapping, is called a Herbrand interpretation. A Herbrand interpretation treats urirefs in the same way as literals, i.e. as denoting their own syntactic form. Of course this may not be what was intended by the writer of the RDF, but the lemma shows that any graph can be interpreted in this way.

If I satisfies E, then I may contain more information than is necessary to specify the truth of E; an interpretation - a world - can be larger than strictly needed to establish the truthvalues of a particular set of triples. It is therefore useful to define a notion of the minimal part of an interpretation which is just enough to make a given graph true.

Say that I' is a subinterpretation of I when vocab(I') is a subset of vocab(I), IR'is a subset of IR, I'(x)=I(x) wherever I'(x) is defined, and IEXT'(x) is a subset of IEXT(x) wherever IEXT'(x) is defined. Intuitively, I' defines a 'part' of the world defined by I. If a subinterpretation of I satisfies E, then I must also satisfy E.

Now define I to be a minimal interpretation of E if I satisfies E, but no other subinterpretation of I satisfies E.

Herbrand interpretations are minimal, and every minimal interpretation has a corresponding Herbrand interpretation which assigns the same truthvalues to every triple, and hence to every graph.

It is clear that if I satisfies E, then a minimal satisfying interpretation exists with a vocabulary precisely the vocabulary of E. The minimal interpretations can be characterized by the following lemma.

Minimality lemma. If I is a minimal satisfying interpretation of E, then I fails to satisfy every triple which has no instance in E.

Proof. We will argue by reductio. Suppose I satisfies some such triple S P O, i.e.. IEXT(I(P)) contains <I(S),I(O)>, and consider the subinterpretation I' which is like I except that IEXT(I'(P)) does not contain that pair. Since S P O has no instances in E, I'+A(x)=I+A(x) for any mapping A from blank nodes and any triple x in E, and I satisfies E, so I' satisfies E; so I is not minimal. QED

The property extensions in a minimal interpretation are 'shrink-wrapped' onto the assertions in the graph. Notice that every thing in the universe of a minimal interpretation of E must be the denotation of at least one node in E, and that every pair in any property extension must have at least one corresponding triple in E that it makes true; for if not, one could delete some of the interpretation and still satisfy E. We will make use of this property in later proofs.

Strong Herbrand Lemma. Any RDF graph E has a satisfying interpretation which does not satisfy any graph which is separable from E.

Proof.The construction in the proof of the Herbrand Lemma in fact establishes this result for arbitrary separable graphs. Consider the Herbrand interpretation I constructed in the proof of the Herbrand lemma, and let <S P O> be a triple which has no instances in E. Then either S is a name and there are no triples of the form S P O' in E, or O is a name and there are no triples of the form S' P O in E. Consider the first case (the other case is similar); then by the construction in the earlier proof, IEXT(I(P)) contains no pairs of the form <I(S), x>; so there is no mapping A from blank nodes to IR that could make the triple true in I+A; so the triple is false in I. Similarly for the other case. QED

Merging lemma. The merge of a set S of RDF graphs is entailed by S, and entails every member of S.

Proof. Obvious, from definitions of entailment and merge. All members of S are true iff all triples in the merge of S are true. QED.

Anonymity lemma 1. Suppose E is a lean graph and E' is a proper instance of E. Then E does not entail E'.

Proof. Since E' is a proper instance and E is lean, E' contains a triple which has no instances in E; otherwise the triple in E which it is a proper instance of would have had a proper instance in E. By the strong Herbrand lemma, there exists an interpretation which satisfies E but not E'. So E does not entail E'. QED

Anonymity lemma 2. Suppose that E is a lean graph and that E' is like E except that two distinct unlabeled nodes in E have been identified in E'. Then E does not entail E'.

Proof. First we assume that the blank nodes occur in two distinct triples in E. Suppose that E contains the triples

S1 P1 _:x1 .

S2 P2 _:x2 .

where E' contains the triples

S1 P1 _:x .

S2 P2 _:x .

(The arguments for the cases where the blank nodes occur in other positions in the triples are similar.) Since E is lean, it contains no other triples of the form S1 P1 O' or S2 P2 O'. Let I be a Herbrand interpretation of E; then I(S1) is distinct from I(S2) and IEXT(I(P1)) ={<I(S1), _:x1>}and IEXT(I(P2))={<I(S2), _:x2>}. Let A be any mapping from the blank nodes of E' to IR, then in order for both triples to be true in I+A, I+A(_:x) would have to equal both _:x1 and _:x2; but these are distinct; so I does not satisfy E'.

The only remaining case is where E contains a single triple with two blank nodes which are identified in E':

_:x1 P _:x2 .

where E' contains

_:x P _:x .

The argument here is similar; the Herbrand interpretation I now has IEXT(I(P)) = {<_:x1,_:x2>} and there is no mapping from the second triple that could satisfy this, so again I satisfies E but not E'. QED.

Note that the 'minimal' nature of the Herbrand construction provides an interpretation that is sufficient to make a graph true, but only just sufficient. This is a basic technique for showing that one graph does not entail another and for establishing a precise correspondence between syntactic relationships and entailment.

Interpolation Lemma. S entails E if and only if a subgraph of the merge of S is an instance of E.

Proof. 'if' is a direct consequence of the merging and instance lemmas.

To prove 'only if' we will show the converse. This is just a re-statement of the strong Herbrand lemma. Assume that no subgraph of the merge of S is an instance of E, i.e. that all subgraphs of the merge of S fail to be instances of E; i.e., that E is separable from the merge of S. Then by the strong Herbrand lemma the merge of S does not entail E. So, by the merging lemma, S does not entail E. QED.

Skolemization is a syntactic transformation routinely used in automatic inference systems in which existential variables are replaced by 'new' functions - function names not used elsewhere - applied to any enclosing universal variables. While not itself strictly a valid operation, skolemization adds no new content to an expression, in the sense that a skolemized expression has the same entailments as the original expression provided they do not contain the new skolem functions.

In RDF, skolemization simplifies to the special case where an existential variable is replaced by a 'new' name, i.e. a uriref which is guaranteed to not occur anywhere else.(Using a literal would not do. Literals are never 'new' in the required sense, since their meaning is fixed.) To be precise, a skolemization of E (with respect to V) is a ground instance of E with respect to a vocabulary V which is disjoint from the vocabulary of E.

The following lemma shows that skolemization has the same properties in RDF as it has in conventional logics. Intuitively, this lemma shows that asserting a skolemization expresses a similar content to asserting the original graph, in many respects. In effect, it simply gives 'arbitrary' names to the anonymous entities whose existence was asserted by the use of blank nodes. However, care is needed, since these 'arbitrary' names have the same status as any other urirefs once published. Also, skolemization would not be an appropriate operation when applied to anything other than the antecendent of an entailment. A skolemization of a query would represent a completely different query.

Skolemization Lemma. Suppose sk(E) is a skolemization of E with respect to V. Then sk(E) entails E; and if sk(E) entails F and the vocabulary of F is disjoint from V, then E entails F .

Proof. sk(E) entails E by the interpolation lemma.

Now, suppose that sk(E) entails F where F shares no vocabulary with V; and suppose I is some interpretation satisfying E. Then for some mapping A from the blank nodes of E, I+A satisfies E. Define an interpretation I' of the vocabulary of sk(E) by: IR'=IR, IEXT'=IEXT, I'(x)=I(x) for x in the vocabulary of E, and I'(x)=I+A(y) for x in V, where y is the blank node in E that is replaced by x in sk(E).Clearly I' satisfies sk(E), so I' satisfies F. But I'(F)=I+A(F) since the vocabulary of F is disjoint from that of V; so I satisfies F. So E entails F. QED.

RDF closure lemma. Any satisfying rdf-interpretation of E satisfies the rdf-closure of E; and any minimal simple satisfying interpretation of the rdf-closure of E is a satisfying rdf-interpretation of E.

Proof. This follows from a comparison of the rdf closure rules with the semantic conditions on an rdf-interpretation. Although the argument is very simple in this case, we give it here in full to illustrate the general technique.

The first part follows from the fact that the closure rules are all rdf-valid. To show this, suppose I is an rdf-interpretation; then for any aaa in the vocabulary of I, if a triple of the form xxx aaa yyy is true in I, then IEXT(I(aaa)) is nonempty then I(aaa) is in IP, so IEXT(I(rdf:type)) contains <I(aaa),I(rdf:Property)>, so the triple aaa rdf:type rdf:Property is true in I. Since I is an rdf-interpretation, its vocabulary contains rdf:type and IP contains I(rdf:type), so in particular the triple

rdf:type rdf:type rdf:Property .

is true in I. That establishes that the closure rules are rdf-valid.

To prove the other part of the lemma we must show that the closure rules are together sufficient to force any minimal interpretation to be an rdf-interpretation of E. The simplest way to argue this is to show the converse, viz. that any minimal simple interpretation of the rdf-closure that violates one of the semantic conditions for an rdf-interpretation of E would thereby fail to satisfy the closure. Suppose therefore that I is a minimal simple interpretation of the rdf-closure of E.

If I violates the first constraint then IP does not contain I(rdf:type); in that case, the added triple in the first closure rule is false in I. So assume that I violates the second constraint. Then there is some x in IP for which IEXT(I(rdf:type)) does not contain <x,I(rdf:Property)>. Since I is minimal, there is some node aaa in E with I(aaa)=x; and since I(aaa) is in IP, there is a pair <y,z> in IEXT(I(aaa)) and a triple

bbb aaa ccc .

in E with I(bbb)=y and I(ccc)=z. Then the closure of E contains the triple

aaa rdf:type rdf:Property .

which is false in I. So I fails to satisfy the rdf-closure. QED.

Notice the need for the minimality assumption, which 'forces' the semantic violation to be made explicit in the syntax of the graph itself. The second part of the lemma could be false for an arbitrary simple interpretation of the closure, which might fail to meet the required semantic conditions on some part of the universe that was not referred to in the graph itself. In general, one cannot infer, from the lack of an assertion in a graph, that what that assertion would say if it were in the graph must be false in a satisfying interpretation of the graph. Minimal interpretations, however, embody a 'closed world assumption' which would sanction such an inference. To prove an entailment we need to prove something about all interpretations; but to prove the converse, it is enough to show that a single interpretation exists with the right properties, and this is where the special properties of minimal interpretations are useful.

RDF entailment lemma. S rdf-entails E if and only if the rdf-closure of the merge of S simply entails E.

Proof. Follows from the merging lemma, the RDF closure lemma and the definition of entailment. By the merging lemma, we can identify S with the merge of S, i.e. we can treat a set of graphs as a single graph MS.

So suppose that MS rdf-entails E, and let I be a simple interpretation of the rdf-closure c(MS) of MS. Then there is a minimal simple subinterpretation I' of I which satisfies c(MS); so, by the previous lemma, I' is a satisfying rdfs-interpretation of E. Therefore I satisfies E, since I' is a subinterpretation of I.

Conversely, suppose that c(MS) simply entails E, and let I be an rdf-interpretation of MS; then by the previous lemma, I satisfies c(MS), so I satisfies E (since every rdf-interpretation is a simple interpretation). QED.

RDFS Closure Lemma. Any satisfying rdfs-interpretation of E satisfies the rdfs-closure of E; and any minimal simple satisfying interpretation of the rdf-closure of E is a satisfying rdfs-interpretation of E.

Proof.(Sketch) As in the proof of the RDF closure lemma, this follows from a point-by-point comparison of the rdfs closure rules with the semantic conditions on an rdfs-interpretation. A full proof would be long but tedious.We will illustrate the form of the argument by considering some typical cases in detail.

The first part follows from the fact that the rdfs closure rules are all rdfs-valid, which can be checked case by case. For example, consider the closure rule rdfs5, and suppose I is an rdfs-interpretation which satisfies

aaa rdfs:subPropertyOf bbb .

bbb rdfs:subPropertyOf ccc .

Then by the semantic conditions on an rdfs-interpretation, IEXT(I(aaa)) is a subset of IEXT(I(bbb)) and IEXT(I(bbb)) is a subset of IEXT(I(ccc)); so IEXT(I(aaa)) is a subset of IEXT(I(ccc)); so I satisfies

aaa rdfs:subPropertyOf ccc .

The other cases are similarly straightforward.

To demonstrate the other part of the lemma we must show that the closure rules are together sufficient to restrict any minimal interpretation to be an rdfs-interpretation. The simplest way to argue this is to show the converse, by demonstrating that any minimal simple interpretation of the rdfs-closure c(E) of E that violates one of the semantic conditions for an rdfs-interpretation of E would thereby make some triple in c(E) false. Again, this can be checked by a detailed examination of the cases that arise.

Suppose that I is a minimal simple interpretation of E. If I violates any of the conditions involving the rdfs-vocabulary then it is easy to check that one of the 'added' triples would be false, eg if IEXT(I(rdfs:domain) does not contain <I(rdfs:domain),I(rdf:Property)> then the triple

rdfs:domain rdfs:domain rdf:Property .

is false in I.

If I violates the condition on IEXT(I(rdfs:range)), then there exist x, y, u and v in IR with <x,y> in IEXT(I(rdfs:range)), <u,v> in IEXT(x) but v not in ICEXT(y). Since I is a minimal interpretation and satisfies c(E), the closure must contain two triples

aaa rdfs:range bbb .

ccc aaa ddd .

where I(aaa)=x, I(bbb)=y, I(ccc)=u and I(ddd)=v; but I makes the triple

ddd rdf:type bbb .

false, since I(ddd) is not in ICEXT(I(bbb)); but by the closure rule rdfs3, this triple is in c(E); so I fails to satisfy c(E). The IEXT(I(rdfs:domain)) case is similar.

Finally, suppose that I violates the condition on rdfs:subClassOf. Then for some x and y in IR, <x,y> is in IEXT(I(rdfs:subClassOf)) but there is some z in ICEXT(x) but not in ICEXT(y). Again, since I is minimal, these entities must occur in triples in c(E) of the form respectively

aaa rdfs:subClassOf bbb .

ccc rdf:type aaa .

where I(aaa)=x, I(bbb)=y and I(ccc)=z, and where the triple

ccc rdf:type bbb .

is false in I; so I does not satisfy c(E) by a similar argument, using the closure rule rdfs9. Again, the case for a violation of the the condition on rdfs:subPropertyOf is similar. QED.

RDFS Entailment Lemma. S rdfs-entails E iff the rdfs-closure of the merge of S simply entails E.

Proof. Exactly similar to proof of RDF entailment lemma. QED.

Appendix C: Translation into L_base

To translate an RDF graph into the semantic reference language L_base, apply the following rules to each expression noted. Each rule gives a translation TR[E] for the expression E, to be applied recursively. To achieve a translation which reflects a namespace entailment, add the axioms specified. Each namespace includes all axioms and rules for preceding namespaces, so that the RDFS translation of a graph should include the RDF translation as well as the RDFS axioms, and so on. The D-entailment rules and axioms refer to properties which depend on the particular datatypes. We have added comments to some of the axioms. The RDFS-D axioms given here use a predicate 'badLiteral' to flag cases of typed literals which are illegally formed according to their attached datatype.

This document reflects the joint effort of the members of the RDF Core Working Group. Dan Connolly clarified the relationship between RDF and RDFS and suggested the 'set of triples' definition of RDF graph. The idea of graph syntax is from Ora Lassila, who also helped clarify the distinction between rules and computation. Sergey Melnick suggested using a translation into logic. Jeremy Carroll noticed the need for the lean graph condition. Jos deRoo, Graham Klyne, Jeremy Carroll and Patrick Stickler found errors in earlier drafts and suggested many stylistic improvements.

The use of an explicit extension mapping to allow self-application without violating the axiom of foundation was suggested by Chris Menzel. Peter Patel-Schneider found several major errors in an earlier draft, and suggested several important technical improvements.

References

Normative

RDF/XML

Dave Beckett (editor), XML Syntax Specification (Revised), 18 December 2001 (W3C Working Draft).

RDFMS

Ora Lassila, Ralph R. Swick (editors), Resource Description Framework (RDF) Model and Syntax Specification, 22 February 1999.

RDFSchema

Dan Brickley, R.V. Guha (editors), Resource Description Framework (RDF) Schema Specification 1.0, 27 March 2000 (W3C Candidate Recommendation).

RDFTestCases

Art Barstow, Dave Beckett (editors), RDF Test Cases , 15 November 2001 (W3C Working Draft).

RFC 2396

T. Berners-Lee, Fielding and Masinter, RFC 2396 - Uniform Resource Identifiers (URI): Generic Syntax, August 1998.

Non-Normative

KIF

Michael R. Genesereth, Knowledge Interchange Format, 1998 (draft American National Standard).

Hayes&Menzel

Patrick Hayes, Christopher Menzel, A Semantics for the Knowledge Interchange Format , 6 August 2001 (Proceedings of 2001 Workshop on the IEEE Standard Upper Ontology)

DAML

Frank van Harmelen, Peter F. Patel-Schneider, Ian Horrocks (editors), Reference Description of the DAML+OIL (March 2001) ontology markup language

Marchiori&Saarela

Massimo Marchioi, Janne Saarela, Query + Metadata + Logic = Metalog, 1998

Fikes&McGuinness

Richard Fikes, Deborah L. McGuinness, An Axiomatic Semantics for RDF, RDF Schema, and DAML+OIL, KSL Technical Report KSL-01-01, 2001

Change Log from previous versions

Changes from Working Draft March 2002

• Overall presentation re-organized to separate the semantic and entailment issues more clearly.
• Many small changes to wording, extra explanatory sentences added, typos and errors corrected.
• Section 2 simplified, auxiliary lemmas now in appendix.
• Treatment of literals simplified and definition of graph syntax changed to reflect the simplification.
• "unasserted triples" terminology removed.
• Section on RDF containers and reification added, corresponding extensions to RDFS entailment conditions and closure rules added.