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This document specifies usage scenarios, goals and requirements for a web ontology language. An ontology formally defines a common set of terms that are used to describe and represent a domain. Ontologies can be used by automated tools to power advanced services such as more accurate Web search, intelligent software agents and knowledge management.
This Working Draft is the second version of the requirements for the Ontology Web Language (OWL) 1.0 specification. Major changes from the first version include:
This document has been produced as part of the W3C Semantic Web Activity (Activity Statement) following the procedures set out for the W3C Process. The document has been written by the Web Ontology Working Group. The goals of the Web Ontology working group are discussed in the Web Ontology Working Group charter.
The Web Ontology Working Group will continue to update this document to reflect changes in requirements and in response to public comments until such time as OWL becomes a W3C Recommendation. The working group has not reached consensus on all topics, therefore particular features may be described as open issues that are still under discussion, such as the Objectives section. For information on the schedule of the working group and the release of related documents, please see the working group's home page.
Comments on this document should be sent to public-webont-comments@w3.org, a mailing list with a public archive. General discussion of related technology is welcome in the mailing list w3c-rdf-logic@w3.org, which also has a public archive.
This 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 materials or to cite them as other than "work in progress." A list of current W3C Recommendations and other technical documents can be found at http://www.w3.org/TR/.
The Semantic Web is a vision for the future of the Web in which information is given explicit meaning, making it easier for machines to automatically process and integrate information available on the Web. The Semantic Web will build on XML's ability to define customized tagging schemes and RDF's flexible approach to representing data. The next element required for the Semantic Web is a Web ontology language which can formally describe the semantics of classes and properties used in web documents. In order for machines to perform useful reasoning tasks on these documents, the language must go beyond the basic semantics of RDF Schema. This document will enumerate the current requirements of such a language. It is expected that future languages will extend this one, adding, among other things, greater logical capabilities and the ability to establish trust on the Semantic Web.
This document motivates the need for a Web ontology language by describing six use cases. Some of these use cases are based on efforts currently underway in industry and academia, others demonstrate more long-term possibilities. The use cases are followed by design goals that describe high-level objectives and guidelines for the development of the language. These design goals will be considered when evaluating proposed features. The section on Requirements presents a set of features that should be in the language and gives motivations for those features. The Objectives section describes a list of features that might be useful for many use cases but may not necessarily be addressed by the working group.
The Web Ontology Working Group charter tasks the group to produce this more expressive semantics and to specify mechanisms by which the language can provide "more complex relationships between entities including: means to limit the properties of classes with respect to number and type, means to infer that items with various properties are members of a particular class, a well-defined model of property inheritance, and similar semantic extensions to the base languages." The detailed specification of the Web Ontology language will take into consideration:
An ontology defines the terms used to describe and represent an area of knowledge. Ontologies are used by people, databases, and applications that need to share domain information (a domain is just a specific subject area or area of knowledge, like medicine, tool manufacturing, real estate, automobile repair, financial management, etc.). Ontologies include computer-usable definitions of basic concepts in the domain and the relationships among them (note that here and throughout this document, definition is not used in the technical sense understood by logicians). They encode knowledge in a domain and also knowledge that spans domains. In this way, they make that knowledge reusable.
The word ontology has been used to describe artifacts with different degrees of structure. These range from simple taxonomies (such as the Yahoo hierarchy), to metadata schemes (such as the Dublin Core), to logical theories. The Semantic Web needs ontologies with a significant degree of structure. These need to specify descriptions for the following kinds of concepts:
Ontologies are usually expressed in a logic-based language, so that detailed, accurate, consistent, sound, and meaningful distinctions can be made among the classes, properties, and relations. Some ontology tools can perform automated reasoning using the ontologies, and thus provide advanced services to intelligent applications such as: conceptual/semantic search and retrieval, software agents, decision support, speech and natural language understanding, knowledge management, intelligent databases, and electronic commerce.
Ontologies figure prominently in the emerging Semantic Web as a way of representing the semantics of documents and enabling the semantics to be used by web applications and intelligent agents. Ontologies can prove very useful for a community as a way of structuring and defining the meaning of the metadata terms that are currently being collected and standardized. Using ontologies, tomorrow's applications can be "intelligent," in the sense that they can more accurately work at the human conceptual level.
Ontologies are critical for applications that want to search across or merge information from diverse communities. Although XML DTDs and XML Schemas are sufficient for exchanging data between parties who have agreed to definitions beforehand, their lack of semantics prevent machines from reliably performing this task given new XML vocabularies. The same term may be used with (sometimes subtle) different meaning in different contexts, and different terms may be used for items that have the same meaning. RDF and RDF Schema begin to approach this problem by allowing simple semantics to be associated with terms. With RDF Schema, one can define classes that may have multiple subclasses and super classes, and can define properties, which may have sub properties, domains, and ranges. In this sense, RDF Schema is a simple ontology language. However, in order to achieve interoperation between numerous, autonomously developed and managed schemas, richer semantics are needed. For example, RDF Schema cannot specify that the Person and Car classes are disjoint, or that a string quartet has exactly four musicians as members.
One of the goals of this document is to specify what is needed in a Web Ontology language. These requirements will be motivated by potential use cases and general design objectives that take into account the difficulties in applying the standard notion of ontologies to the unique environment of the Web.
Ontologies can be used to improve existing Web-based applications and may enable new uses of the Web. In this section we describe six representative use cases of Web ontologies. Note that this is not an exhaustive list, but instead a cross-section of interesting use cases.
A Web portal is a web site that provides information content on a common topic, for example a specific city or domain of interest. A web portal allows individuals that are interested in the topic to receive news, find and talk to one another, build a community, and find links to other web resources of common interest.
In order for a portal to be successful, it must be a starting place for locating interesting content. Typically this content is submitted by members of the community, who often index it under some subtopic. Another means of collecting content relies on the content providers tagging the content with information that can be used in syndicating it. Typically, this takes the form of simple metatags that identify the topic of the content, etc.
However, a simple index of subject areas may not provide the community with sufficient ability to search for the content that its members require. In order to allow more intelligent syndication, web portals can define an ontology for the community. This ontology can provide a terminology for describing content and axioms that define terms using other terms from the ontology. For example, an ontology might include terminology such as "journal paper," "publication," "person," and "author." This ontology could include definitions that state things such as "all journal papers are publications" or "the authors of all publications are people." When combined with facts, these definitions allow other facts that are necessarily true to be inferred. These inferences can, in turn, allow users to obtain search results from the portal that are impossible to obtain from conventional retrieval systems. Of course, such a technique relies on content providers annotating their pages with the web ontology language, but if we assume that these owners will try to distribute their content as widely as possible, then we can expect that they would be willing to do this.
One example of an ontology based portal is OntoWeb. This portal serves the academic and industry community that is interested in ontology research. Another example of a portal that uses Semantic Web technologies and could benefit from an ontology language is The Open Directory Project; a large, comprehensive human-edited directory of the Web. It is constructed and maintained by a vast, global community of volunteer editors. RDF dumps of the Open Directory database are available for download.
Ontologies can be used to provide semantic annotations for collections of images, audio, or other non-textual objects. It is even more difficult for machines to extract meaningful semantics from multimedia than it is to extract semantics from natural language text. Thus, these types of resources are typically indexed by captions or metatags. However, since different people can describe these non-textual objects in different ways, it is important that the search facilities go beyond simple keyword matching. Ideally, the ontologies would capture additional knowledge about the domain that can be used to improve retrieval of images.
Multimedia ontologies can be of two types: media-specific and content-specific. Media specific ontologies could have taxonomies of different media types and describe properties of different media. For example, video may include properties to identify length of the clip and scene breaks. Content-specific ontologies could describe the subject of the resource, such as the setting or participants. Since such ontologies are not specific to the media, they could be reused by other documents that deal with the same domain. Such reuse would enhance search that was simply looking for information on a particular subject, regardless of the format of the resource. Searches where media type was important could combine the media-specific and content-specific ontologies.
As an example of a multimedia collection, consider an archive of images of antique furniture. An ontology of antique furniture would be of great use in searching such an archive. A taxonomy can be used to classify the different types of furniture. It would also be useful if the ontology could express definitional knowledge. For example, if an indexer selects the value "Late Georgian" for the style/period of (say) an antique chest of drawers, it should be possible to infer that the data element "date.created" should have a value between 1760 and 1811 A.D. and that the "culture" is British. Availability of this type of background knowledge significantly increases the support that can be given for indexing as well as for search. Another feature that could be useful is support for the representation of default knowledge. An example of such knowledge would be that a "Late Georgian chest of drawers" is typically made of mahogany. This knowledge is crucial for real semantic queries, e.g. a user query for "antique mahogany storage furniture" could match with images of Late Georgian chests of drawers, even if nothing is said about wood type in the image annotation.
Large corporations typically have numerous web pages concerning things like press releases, product offerings and case studies, corporate procedures, internal product briefings and comparisons, white papers, and process descriptions. Ontologies can be used to index these documents and provide better means of retrieval. Although many large organizations have a taxonomy for organizing their information, this is often insufficient. A single taxonomy is often limiting because many things can fall under multiple categories. Furthermore, the ability to search on values for different parameters is often more useful than a keyword search with taxonomies.
An ontology-enabled web site may be used by:
A typical problem for each of these types of users is that they may not share terminology with the authors of the desired content. The salesperson may not know the technical name for a desired feature or technical people in different fields might use different terms for the same concept. For such problems, it would be useful for each class of user to have different ontologies of terms, but have each ontology interrelated so translations can be performed automatically.
Another problem is framing queries at the right level of abstraction. A project leader looking for someone with expertise in operating systems should be able to locate an employee who is an expert with both Unix and Windows.
One aspect of a large service organization is that it may have a very broad set of capabilities. But when pursuing large contracts these capabilities sometimes need to be assembled in new ways. There will often be no previous single matching project. A challenge is to reason about how past templates and documents can be reassembled in new configurations, while satisfying a diverse set of preconditions.
This use case is for a large body of engineering documentation, such as that used by the aerospace industry. This documentation can be of several different types, including design documentation, manufacturing documentation, and testing documentation. These document sets each have a hierarchical structure, but the structures differ between the sets. There is also a set of implied axes which cross-link the documentation sets: for example, in aerospace design documents, an item such as a wing spar might appear in each.
Ontologies can be used to build an information model which allows the exploration of the information space in terms of the items which are represented, the associations between the items, the properties of the items, and the links to documentation which describes and defines them (i.e., the external justification for the existence of the item in the model). That is to say that the ontology and taxonomy are not independent of the physical items they represent, but may be developed/explored in tandem.
A concrete example of this use case is design documentation for the aerospace domain, where typical users include:
To support this kind of usage, it is important that constraints can be defined. These constraints may be used to enhance search or check consistency. An example of a constraint might be:
biplane(X) => CardinalityOf(wing(X)) = 2 wingspar(X) AND wing(Y) AND isComponentOf(X,Y) => length(X) < length(X)
Another common use of this kind of ontology is to support the visualization and editing of charts which show snapshots of the information space centered on a particular object (class or instance). These are typically activity/rule diagrams or entity-relationship diagrams.
The Semantic Web can provide agents with the capability to understand and integrate diverse information resources. A specific example is that of a social activities planner, which can take the preferences of a user (such as what kinds of films they like, what kind of food they like to eat, etc.) and use this information to plan the user's activities for an evening. The task of planning these activities will depend upon the richness of the service environment being offered and the needs of the user. During the service determination / matching process, ratings and review services may also be consulted to find closer matches to user preferences (for example, consulting reviews and rating of films and restaurants to find the "best").
This type of agent requires domain ontologies that represent the terms for restaurants, hotels, etc. and service ontologies to represent the terms used in the actual services. When building the actual services, the information may come from a number of sources, such as portals, service-specific sites, reservation sites and the general Web.
Agentcities is an example of an initiative that is exploring the use of agents in a distributed service environment across the Internet. This will involve building a network of agent platforms that represent real or virtual cities, such as San Francisco or the Bay Area, and populating them with the services of those cities. Initially, these services will be oriented towards business to consumer services, such as hotels, restaurants, entertainment, etc., but eventually, they will be expanded to include business to business services, such as payroll, and business to enterprise services.This will require a number of different domain and service ontologies: Key issues include:
Ubiquitous computing is an emerging paradigm of personal computing, characterized by the shift from dedicated computing machinery to pervasive computing capabilities embedded in our everyday environments. Characteristic to ubiquitous computing are small, handheld, wireless computing devices. The pervasiveness and the wireless nature of devices require network architectures to support automatic, ad hoc configuration. An additional reason for development of automatic configuration is that this technology is aimed at ordinary consumers.
A key technology of true ad hoc networks is service discovery, functionality by which "services" (i.e., functions offered by various devices such as cell phones, printers, sensors, etc.) can be described, advertised, and discovered by others. All of the current service discovery and capability description mechanisms (e.g., Sun's JINI, Microsoft's UPnP) are based on ad hoc representation schemes and rely heavily on standardization (i.e., on a priori identification of all those things one would want to communicate or discuss).
The key issue (and goal) of ubiquitous computing is "serendipitous interoperability," interoperability under "unchoreographed" conditions, i.e., devices which weren't necessarily designed to work together (such as ones built for different purposes, by different manufacturers, at a different time, etc.) should be able to discover each others' functionality and be able to take advantage of it. Being able to "understand" other devices, and reason about their services/functionality is necessary, since full-blown ubiquitous computing scenarios will involve dozens if not hundreds of devices, and a priori standardizing the usage scenarios is an unmanageable task.
The interoperation scenarios are dynamic in nature (i.e., devices appear and disappear at any moment as their owners carry them from one room or building to another) and do not involve humans in the loop as far as (re-)configuration is concerned.
Given that device functionality can be modeled as web services, the needs for this use case are somewhat similar to the needs for DAML-S (particularly the issues surrounding the expressiveness of the language).
The tasks involved in the utilization of services involve discovery, contracting, and composition. The contracting of services may involve representing information about security, privacy and trust, as well as about compensation-related details (the provider of a service may have to be compensated for services rendered). In particular, it is a goal that corporate or organizational security policies be expressed in application-neutral form, thus enabling constraint representation across the diversity of enforcement mechanisms (e.g., firewalls, filters/scanners, traffic monitors, application-level routers and load-balancers).
Given that RDF-based schemes for representing information about device characteristics have started to be adopted (namely, W3C's Composite Capability/Preference Profile (CC/PP) and WAP Forum's User Agent Profile or UAProf), an additional need is compatibility with RDF at some level.
Design goals describe general motivations for the language that do not necessarily result from any single use case. In this section, we describe eight design goals for the Web ontology language. For each goal, we describe the tasks it supports and explain the rationale for the goal. We also describe the degree to which RDF and RDF Schema supports the goal.
Ontologies should be publicly available and different data sources should be able to commit to the same ontology for shared meaning. Also, ontologies should be able to extend other ontologies in order to provide additional definitions.
Supported Tasks:
Any use case in which distributed data sources use shared
terminology.
Justification:
Interoperability requires agreements on the definitions of
terms. Ontologies can provide standard sets of terms and formal
descriptions of those terms. Data sources that commit to the same
ontology explicitly agree to use the same terms with the same
meanings.
Often, shared ontologies are not sufficient. An organization may find that an existing ontology provides 90% of what it needs, but the remaining 10% is critical. In such cases, the organization should not have to create a new ontology from scratch, but instead be able to create an ontology which extends an existing ontology and adds any desired terms and definitions.
RDF(S) Support:
In RDF, each schema has its own namespace identified by a URI.
Each term in the schema is identified by combining the schema's
URI with the term's ID. Any resource that uses this URI
references the term as defined in that schema. However, RDF is
unclear on the definition of a term that has partial definitions
in multiple schemas. The specification appears to assume that the
definition is the union of all descriptions that use the same
identifier, regardless of source. However, this may lead to
problems in a distributed environment, where some schemas may
contain incorrect or false definitions. There is no way in RDF
for a resource to indicate which set of definitions it agrees
to.
An ontology may change during its lifetime. A data source should specify the version of an ontology to which it commits.
An important issue is whether or not documents that commit to one version of an ontology are compatible with those that commit to another. Both compatible and incompatible revisions should be allowed, but it should be possible to distinguish between the two. Note that since formal descriptions only provide approximations for the meanings of most terms, it is possible for a revision to change the intended meaning of a term without changing its formal description. Thus determining semantic backwards-compatibility requires more than a simple comparison of term descriptions. As such, the ontology author needs to be able to indicate such changes explicitly.
Supported Tasks:
Any use case in which the ontology could potentially change,
and in particular those in which the owner of the ontology is
different from the data providers.
Justification:
Since the web is constantly growing and changing, we must
expect ontologies to change as well. Ontologies may need to
change because there were errors in prior versions, because a new
way of modeling the domain is preferred, or because
new terminology has been created (e.g.,
as the result of the invention of new technology). A web ontology
language must be able to accommodate ontology revision. Note that
ontology evolution is different from ontology extension, which
does not change the original ontology.
RDF(S) Support:
The RDF Schema Specification recommends that each version of a
schema should be a separate resource with its own URI. The
rdfs:subClassOf and rdfs:subPropertyOf properties can be used to
relate new versions of classes and properties to older versions.
However, this has the drawback that incorrect definitions cannot
be retracted. For example, assume that in schema v1, v1:Dolphin
is a rdfs:subClassOf v1:Fish. When this mistake is noticed, the
new version of the schema, v2, says that v2:Dolphin is a
rdfs:subClassOf v2:Mammal. However, if we make v2:Dolphin a
rdfs:subClassOf v1:Dolphin, then we also say that v2:Dolphin is
an rdfs:subClassOf v1:Fish which perpetuates the error.
Different ontologies may model the same concepts in different ways. The language should provide primitives for relating different representations, thus allowing data to be converted to different ontologies and enabling a "web of ontologies."
Supported Tasks:
Any use case in which data from different providers with
different terminologies must be integrated.
Justification:
Although shared ontologies and ontology extension allow a
certain degree of interoperability between different
organizations and domains, there are often cases where there are
multiple ways to model the same information. This may be due to
differences in the perspectives of different organizations,
different professions, different nationalities, etc. In order for
machines to be able to integrate information that commits to
heterogeneous ontologies, there need to be primitives that allow
ontologies to map terms to their equivalents in other ontologies.
RDF(S) Support:
RDF provides minimal support for interoperability by means of
the rdfs:subClassOf and rdfs:subPropertyOf properties.
Different ontologies or data sources may be contradictory. It should be possible to detect these inconsistencies.
Supported Tasks:
Any use cases in which decentralization of data and lack of
controlling authority can lead to conflicts in the data. Any
ontology extension task that may result in incoherent
descriptions (possibly by extending an ontology in a way that
generated an over constrained term).
Justification:
The Web is decentralized, allowing anyone to say anything. As
a result, different viewpoints may be contradictory, or even
false information may be provided. In order to prevent agents
from combining incompatible data or from taking consistent data
and evolving it into an inconsistent state, it is important that
inconsistencies can be detected automatically.
RDF(S) Support:
RDF and RDFS do not allow inconsistencies to be expressed.
The language should be able to express a wide variety of knowledge, but should also provide for efficient means to reason with it. Since these two requirements are typically at odds, the goal of the web ontology language is to find a balance that supports the ability to express the most important kinds of knowledge.
Supported Tasks:
Any use case that uses large ontologies or large data sets and
requires the representation of diverse knowledge.
Justification:
There are over one billion pages on the Web, and the potential
application of the Semantic Web to embedded devices and agents
poses even larger amounts of information that must be handled.
The web ontology language should support reasoning systems that scale
well. However, the language should also be as expressive
as possible, so that users can state the kinds of knowledge that
is important to their applications.
Expressivity determines what can be said in the language, and thus determines its inferential power and what reasoning capabilities should be expected in systems that fully implement it. An expressive language contains a rich set of primitives that allow a wide variety of knowledge to be formalized. A language with too little expressivity will provide too few reasoning opportunities to be of much use and may not provide any contribution over existing languages.
RDF(S) Support:
RDF is very scalable (with the exception of the XML syntax
being extremely verbose) but is not very expressive.
The language should provide a low learning barrier and have clear concepts and meaning. The concepts should be independent from syntax.
Supported Tasks:
Markup and querying of semantic web documents by users, either
directly or indirectly.
Justification:
Although ideally most users will be isolated from the language
by front end tools, the basic philosophy of the language must be
natural and easy to learn. Furthermore, early adopters, tool
developers, and power users may work directly with the syntax,
meaning human readable (and writable) syntax is desirable.
RDF(S) Support:
RDF is fairly easy to use, but RDF Schema is more complex. The
syntax appears to be a major barrier for many.
The language should be compatible with other other commonly used Web and industry standards. In particular, this includes XML and related standards (such as XML Schema and RDF), and possibly other modeling standards such as UML.
Supported Tasks:
Exchange of ontologies and data in a standard format.
Justification:
Compatibility with other standards eases tool
development and deployment of the language.
RDF(S) Support:
RDF has an XML serialization syntax.
The language should support the development of multilingual ontologies, and potentially provide different views of ontologies that are appropriate for different cultures.
Supported Tasks:
Tasks where the same ontology is used by multiple countries or
cultures.
Justification:
The Web is an international tool. The Semantic Web should aid
in the exchange of ideas and information between different
cultures, and should thus make it easy for members of different
nations to use the same ontologies.
RDF(S) Support:
To the extent that XML supports internationalization, so does
RDF. The RDF Specification states that the xml:lang attribute can
be used to support the internationalization of labels, but does
not accommodate it in the data model.
The use cases and design goals motivate a number of requirements for a Web Ontology language. The Working Group currently feels that the requirements described below are essential to the language. Each requirement includes a short description and is motivated by one or more design goals from the previous section.
Ontologies must be objects that have their own unique identifiers, such as a URI reference.
Motivation: Shared ontologies
Two terms in different ontologies must have distinct absolute identifiers (although they may have identical relative identifiers). It must be possible to uniquely identify a term in an ontology using a URI reference.
Motivation: Shared ontologies, Ontology interoperability
Ontologies must be able to explicitly extend other ontologies in order to reuse terms while adding new classes and properties. Ontology extension must be a transitive relation; if ontology A extends ontology B, and ontology B extends ontology C, then ontology A implicitly extends ontology C as well.
Motivation: Shared ontologies
Resources must be able to explicitly commit to specific ontologies, indicating precisely which set of definitions and assumptions are made.
Motivation: Shared ontologies
It must be possible to provide meta-data for each ontology, such as author, publish-date, etc. The language should provide a standard set of common metadata properties. These properties may or may not be borrowed from the Dublin Core element set.
Motivation: Shared ontologies
The language must provide features for comparing and relating different versions of the same ontology. This should include features for relating revisions to prior versions, explicit statements of backwards-compatibility, and the ability to deprecate terms (i.e., to state they are available for backwards-compatibility only, and should not be used in new applications/documents.)
Motivation: Ontology evolution
The language must be able to express complex definitions of classes. This includes, but is not limited to, sub classing and Boolean combinations of class expressions (i.e., intersection, union, and complement).
Motivation: Balance of expressivity and scalability, Ontology interoperability, Inconsistency detection
The language must be able to express the definitions of properties. This includes, but is not limited to, sub properties, domain and range constraints, transitivity, and inverse properties.
Motivation: Balance of expressivity and scalability, Ontology interoperability, Inconsistency detection
The language must provide a set of standard data types. These data types may be based on XML Schema data types.
Motivation: Compatibility with other standards, Ease of use
The language must include features for stating that two classes or properties are equivalent.
Motivation: Ontology interoperability
The language must include features for stating that pairs of identifiers represent the same individual. Due to the distributed nature of the Web, it is likely that different identifiers will be assigned to the same individual. The use of a standard URL does not solve this problem, because some individuals may have multiple URLs, such as a person who has home and work web pages or e-mail addresses.
Motivation: Ontology interoperability
In general, the language will not make a unique names assumption. That is, distinct identifiers are not assumed to refer to different objects (see the previous requirement). However, there are many applications where the unique names assumption would be useful. Users should have the option of specifying that all of the names in a particular namespace or document refer to distinct objects.
Motivation: Inconsistency detection
The language must provide a way to allow statements to be "tagged" with additional information such as source, timestamp, confidence level, etc. The language need not provide a standard set of properties that can be used in this way, but should instead provide a general mechanism for users to attach such information. RDF reification may be one possible way to accommodate the requirement, although reification is a somewhat controversial feature.
Motivation: Shared ontologies, Ontology interoperability
The language must support the ability to treat classes as instances. This is because the same concept can often be seen as a class or an individual, depending on the perspective of the user. For example, in a biological ontology, the class Orangutan may have individual animals as its instances. However, the class Orangutan may itself be an instance of the class Species. Note, that Orangutan is not a subclass of Species, because then that would say that each instance of Orangutan (an animal) is an instance of Species.
Motivation: Ontology interoperability
The language must support the definition and use of complex/ structured data types. These may be used to specify dates, coordinate pairs, addresses, etc.
Motivation: Compatibility with other standards, Ease of use
The language must the support the specification of cardinality restrictions on properties. These restrictions set minimum and maximum numbers of object that any single object can be related to via the specified property.
Motivation: Balance of expressivity and scalability goal,, Inconsistency detection
The language should have an XML serialization syntax. XML has become widely accepted by industry and numerous tools for processing XML have been developed. If the web ontology language has an XML syntax, then these tools can be extended and reused.
Motivation: Compatibility with other standards
The language should support the specification of multiple alternative user-displayable labels for the objects within an ontology. This can be used, for example, to view the ontology in different natural languages.
Motivation: Internationalization, Ease of use
The language should support the use of multilingual character sets.
Motivation: Internationalization, Compatibility with other standards
In some character encodings, e.g. Unicode based encodings, there are some cases where two different character sequences look the same and are expected, by most users, to compare equal. An example is one using a pre-composed form (just one c-cedilla character) and another using a decomposed form (a 'c' character followed by a cedilla accent character). Given that the W3C I18N WG has decided that early uniform normalization (to Unicode Normal Form C) as the usual approach to solving this problem, any other solution needs to be justified.
Motivation: Internationalization, Compatibility with other standards
In addition to the set of features that should be in the language (as defined in the previous section), there are other features that would be useful for many use cases. These features will be addressed by the working group if possible, but the group may decide that there are good reasons for excluding them from the language or for leaving them to be implemented by a later working group. Some of these objectives are not fully defined, and as such need further clarification if they are to be addressed by the language.
The language may support different levels of complexity for defining ontologies. Applications can conform to a particular layer without supporting the entire language. A guideline for identifying layers may be based on functionality found in different types of database and knowledge base systems.
Motivation: Balance of expressivity and scalability
The language should support the specification of default values for properties. Such values are useful in making inferences about typical members of classes. However, true default values are nonmonotonic, which can be problematic on the Web where new information is constantly being discovered or added. Furthermore, there is no commonly accepted method for dealing with defaults. An alternative is for the language specification to recommend to users how they can create their own default mechanisms.
Motivation: Balance of expressivity and scalability
Due to the size and rate of change on the Web, the closed-world assumption (which states that anything that cannot not be inferred is assumed to be false) is inappropriate. However, there are many situations where closed-world information would be useful. Therefore, the language must be able to state that a given ontology can be regarded as complete. This would then sanction additional inferences to be drawn from that ontology. The precise semantics of such a statement (and the corresponding set of inferences) remains to be defined, but examples might include assuming complete property information about individuals, assuming completeness of class-membership, and assuming exhaustiveness of subclasses.
Motivation: Shared ontologies, Inconsistency detection
The language should support the ability to specify ranges of values for properties. This mechanism may borrow from XML Schema data types.
Motivation: Inconsistency detection
The language may support the composition of properties in statements about classes and properties. An example of the use of property composition would be the assertion that a property called uncleOf is the same as the composition of the fatherOf and brotherOf properties.
Motivation: Balance of expressivity and scalability
The language should be decidable.
Motivation: Balance of expressivity and scalability
The language should support the ability to commit to portions of an ontology, as well as committing to an entire ontology. However, it is unclear what granularity should be used here. Possible choices are to choose a subset of terms and all definitions they include, or to choose individual pieces of definitions. Note that borrowing partial definitions of terms will lead to interoperability problems because different applications will be using the same term to mean different things.
Motivation: Shared ontologies
The language should support the ability to create ontology views, in which subsets of an ontology can be specified or terms can be assigned alternate names. This is particularly useful in developing multicultural versions of an ontology. Note that this requirement may be satisfied by having multiple ontologies and using an ontology mapping mechanism.
Motivation: Internationalization, Ontology interoperability
The W3C XML Digital Signature specification is an important building block for communication among untrusted properties, which is important for many ontology applications. The web ontology language should be designed in a way that makes it straightforward to use XML Signatures.
Motivation: Compatibility with other standards
The language should support the use of arithmetic functions. These can be used in translating between different units of measure.
Motivation: Ontology interoperability
The language should support string concatenation and simple pattern matching. These features can be used to establish interoperability between ontologies that treat complex information as a formatted string and those that have separate properties for each component. For example, one ontology may represent a person's name as a single string "lastname, firstname," while another may have a property for each.
Motivation: Ontology interoperability
The language should support the ability to aggregate information in a way similar to SQL's GROUP BY clause. It should allow counts, sums, and other operations to be computed for each group. This would allow interoperability between ontologies that represented information at different levels of granularity, and could relate things such as budget category totals to budget line item amounts, or the number of personnel to individual data on employees.
Motivation: Ontology interoperability
The language should support the use of executable code to evaluate complex criteria. Procedural attachments greatly extend the expressivity of the language, but are not well-suited to formal semantics. A procedural attachment mechanism for web ontologies should specify how to locate and execute the procedure. One potential candidate language would be Java, which is already well-suited to intra-platform sharing on the Web.
Motivation: Ontology interoperability