Re: comment: working draft on n-ary relations note

Dear Gian,

Thank you very much for your elaborate comments. It is interesting to 
see how you would represent it in a different language (NKRL, in your 
case). I would like to point out, however, that the mission of this 
Working Group is to propose solutions specifically for RDF and OWL 
languages -- the Semantic Web languages recommended by W3C.

It is also not clear what is the problem with the generality that you 
are referring to? Note that the goal of the note is not to present a 
general solution on how you would represent anything with the verb 
"purchase" in it, but rather how to express a certain knowledge 
structure in RDF/OWL. You may have misinterpreted our goals.

Thanks a lot again for your comments.
	
Natasha

On Oct 23, 2004, at 4:15 AM, Gian Piero Zarri wrote:

>     DEFINING N-ARY RELATIONS ON THE SEMANTIC WEB
>
>      Dear Natasha, Dear Alan,
>
>      I came across your W3C Working Draft on Defining N-ary Relations 
> on the Semantic Web.  I have, of course, really appreciated your 
> efforts to instruct languages like RDF and OWL to get out of the 
> binary relations trap. I'm not, however, totally convinced  about the 
> generality of the solutions you propose. For example, I found as 
> particularly  non natural and anti intuitive the introduction of the 
> individual Diagnosis_Relation_1 to represent the example "Christine 
> has breast tumor with high probability". The solution for "John buys a 
> 'Lenny the Lion' book from books.example.com for $15 as a birthday 
> gift" is more appealing; however,  it is quite similar to the 
> solutions proposed by Schubert in a well known 1976 paper in the 
> "Artificial Intelligence" journal on "Extending the expressive power 
> of semantic networks". To my knowledge, Schubert's proposal has never 
> been implemented in full, probably because of the difficulty to define 
> correctly the case system for each single English verb, "purchase" in 
> your case: see, in this context, Levin's book on "English Verb Classes 
> and Alternations", Chicago University Press, 1993.
>
>       I haven't resisted the temptation to represent your three 
> examples as instances ("predicative occurrences") of NKRL's templates. 
> NKRL (Narrative Knowledge Representation Language) is a formal 
> language explicitly created to represent in the best way the "meaning" 
> of "narratives" (in the widest meaning of this term) like those 
> represented by your examples. The main innovation of NKRL is the 
> addition of an "ontology of event" to the traditional (and 
> binary-constrained) "ontology of concepts" in the OWL or Protege-2000 
> style. Templates are n-ary structures constructed around a semantic 
> predicate (EXPERIENCE, OWN, PRODUCE, RECEIVE...), where the arguments 
> of the predicate ("traditional" concepts or combinations of concepts) 
> are introduced through "roles" like SUBJ(ect) , OBJ(ect), 
> BEN(e)F(iciary), MODAL(ity)... Predicates and roles are primitives; 
> concepts (about 2,5000 presently) are collected into a frame-like 
> hierarchical structure, HClass. Templates correspond, in short, to 
> formal definitions of classes of elementary narrative events like 
> "being affected by a positive/negative situation", "buying or selling 
> an object", "moving a physical object", "being present in a place", 
> "producing a service", "sending/receiving a message", etc. 150 
> pre-defined templates, very easy to extend and customize, are actually 
> associated with NKRL; they are structured into a second hierarchy, the 
> "hierarchy of templates", HTemp, which is the materialization of the 
> ontology of events evoked before. NKRL is fully implemented (Java2) in 
> both a file-oriented and an ORACLE-oriented version; information about 
> the NKRL's theory can be found on my Web site 
> (http://www.lalic.paris4.sorbonne.fr/Zarri/home.html).
>
>      Concretely, the NKRL representations of your two first examples, 
> "Christine has breast tumor..." and "Steve has temperature, which is 
> high, but falling" are obtained as instantiations of the template 
> "Experience:NegativeHuman/SocialSituation" (3.211) pertaining to the 
> "Experience:" branch of HTemp. The representation of the  "John 
> buys..." event is an instantiation of the template "Produce:Buy" 
> (6.361) pertaining to the "Produce:" branch of HTemp.
>
>
> c1) EXPERIENCE
>  SUBJ CHRISTINE_
>  OBJ breast_tumor
>  MODAL (SPECIF probability_ high_)
>
>
> c2) EXPERIENCE
>  SUBJ STEVE_
>  OBJ (COORD (SPECIF temperature_ (SPECIF magnitude_ high_)) (SPECIF 
> temperature_ (SPECIF trend_ falling_)))
>
>
> c3) PRODUCE
>  SUBJ JOHN_
>  OBJ (SPECIF purchase_ BOOK_1)
>  SOURCE BOOKS_EXAMPLE_COM
>  MODAL (SPECIF money_ USA_DOLLAR (SPECIF amount_ 15))
>  CONTEXT birthday_event
>
>
>     The above code is shortened, in that, e.g., the predicative 
> occurrences should always be accompanied by the formal representation 
> of the temporal characteristics of the corresponding events - NKRL is 
> endowed with a (relatively sophisticated) system for representing 
> narrative temporal information and using this for conceptual indexing 
> purposes. The fillers of roles like SUBJ, OBJ, MODAL etc. can be 
> simple ("breast_tumor") or complex. In this last case, they are built 
> up using the four AECS operators ALTERN(ative), ENUM(eration), 
> COORD(ination) and SPECIF(ication); AECS operators share some 
> similarities with the RDF "containers". The two AECS operators used in 
> the code above are the "collective operator", COORD, and the 
> "attributive operator", SPECIF. COORD means (roughly) that "the 
> corresponding arguments cannot be dissociated" - in c2, the 
> temperature is both high and falling. SPECIF is used (roughly) to 
> introduce further details about the first argument of the SPECIF list 
> - in c1, we specify that the probability is high. To built up 
> unambiguous and well-formed complex fillers, very precise rules are 
> used ("priority rule"); these are responsible, e.g., for the 
> duplication of the "temperature_" term in the OBJ filler of c2. As 
> already stated, the terms making up the fillers are elements of 
> HClass, the NKRL "traditional" ontology of concepts. More precisely, 
> terms in upper case are instances of concepts ("individuals"), see 
> CHRISTINE_, STEVE_ and JOHN_ that are all instances of the 
> "individual_person" concept. Terms in lower case are concepts, both 
> non-sortal (which cannot be directly instantiated), like "amount_" or 
> "high_", and sortal (which can directly give rise to individuals), 
> like "breast_tumor", "purchase_" or "birthday_event". For simplicity's 
> sake, I have used here the original terms of your examples to denote 
> concepts and instances; in reality, "temperature_" should be replaced 
> by something in the style of "human_temperature", the current concept 
> for trend  n HClass is "gradient_" - a non sortal concept, specific 
> term of "quantifying_property", etc.
>
>
>     A last point. Through reification operations on the symbolic 
> labels (like c1, c2 and c3 above) of the predicative occurrences, 
> these last can be associated in order to take into account those 
> "connectivity phenomena" typical of the narrative domain and that 
> derive from the presence of second order relationships like causality, 
> goal, indirect speech, co-ordination, subordination, etc. Thanks to 
> the use of second order operators like CAUSE and GOAL, it is then 
> possible to represent in NKRL situations like ""Christine has breast 
> tumor with high probability BECAUSE of her long exposure to DDT and 
> nonylphenol", or "John buys etc. IN ORDER TO sing his own praises to 
> Jane".
>
>
>     Best regards,
>
>
>
>      Gian Piero ZARRI
>      LaLICC, University of Paris4-Sorbonne
>      zarri@noos.fr, gpzarri@paris4.sorbonne.fr  
>     

Received on Monday, 25 October 2004 20:40:30 UTC