RDF/OWL Representation of WordNet

Abstract

This document presents a standard conversion of Princeton WordNet to RDF/OWL. It describes how it was converted and gives examples of how it may be queried for use in Semantic Web applications.

Status of this Document

This section describes the status of this document at the time of its publication. Other documents may supersede this document. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at http://www.w3.org/TR/.

This document is a First Public Working Draft produced by the Semantic Web Best Practices and Deployment Working Group, part of the W3C Semantic Web Activity.

Comments on this document are encouraged and may be sent to [email protected]; please include the text "comment" in the subject line. All messages received at this address are viewable in a public archive.

At the time of publication the charter of the Semantic Web Best Practices and Deployment Working Group is expiring and no chartered group has been proposed to continue further work on this document. The Working Group does recognize that feedback on this document may lead to suggestions for further work. The current Working Group is not placing this work on the W3C Recommendation Track.

The URIs specified in this document for WordNet terms are served by W3C in accordance with its resource persistence policy. Refer to Appendix I Open Issues for expectations regarding Princeton URIs for these resources.

This document was produced by a group operating under the 5 February 2004 W3C Patent Policy. W3C maintains a public list of any patent disclosures made in connection with the deliverables of the group; that page also includes instructions for disclosing a patent. An individual who has actual knowledge of a patent which the individual believes contains Essential Claim(s) must disclose the information in accordance with section 6 of the W3C Patent Policy.

Publication as a Working Draft does not imply endorsement by the W3C Membership. This is a draft document and may be updated, replaced or obsoleted by other documents at any time. It is inappropriate to cite this document as other than work in progress.

Acknowledgements

The following people have reviewed this document and provided valuable advice: Jeremy Carroll, Brian McBride, John McClure, Benjamin Nguyen and Jacco van Ossenbruggen. The following people have provided valuable advice through the best practices mailing list and personal correspondence with the editors: David Booth, Dan Connolly Jeremy Carroll Kjetil Kjernsmo, Michel Klein, Peter Mika, Alistair Miles, Steve Pepper, Jacco van Ossenbruggen and Ralph Swick. Dan Brickley and Brian McBride have contributed to the WordNet conversion described in this note through their work in the WordNet Task Force and additional comments and suggestions. Special thanks to Ralph Swick for help in generating CBDs and setting up this conversion in W3C webspace. We also thank the MultimediaN e-Culture team, in particular Jan Wielemaker, for important usage comments.

Table of Contents

• 1. Introduction and guide to the reader
• 2. Introduction to WordNet in RDF/OWL
• 3. Selecting and Querying the appropriate WN version
• 4. Advanced options
• Appendix A: Requirements
• Appendix B: Versioning and redirection strategy
• Appendix C: Overview of the WordNet Prolog distribution
• Appendix D: Conversion details
• Appendix E: Possible mappings to SKOS
• Appendix F: Relation to previous versions
• Appendix G: Introducing URIs for Synsets, WordSenses, Words
• Appendix H: Internationalization
• Appendix I: Open Issues
• Appendix J: Change Log since 17 October
• Appendix K: References

1. Introduction and guide to the reader

WordNet [Fellbaum, 1998] is a heavily-used lexical resource in natural-language processing and information retrieval. More recently, it has also been adopted in Semantic Web research community. It is used mainly for annotation and retrieval in different domains such as cultural heritage [Hollink et al., 2003], product catalogs [Guarino et al., 1999] and photo metadata [Brickley, 2002]. It is also used to ground other vocabularies such as the FOAF schema [Brickley and Miller, 2005], as background knowledge in ontology alignment tools and other applications (see http://esw.w3.org/mt/esw/archives/cat_applications_and_demos.html for a list). Currently there exist several conversions of WordNet to RDF(S) or OWL.

The WordNet Task Force of the SWBPD WG aims at providing a standard conversion of WordNet for direct use by Semantic Web application developers. Some of the earlier conversions are incomplete and are incompatible with each other, for example because they provide different URIs for the same entity in the original source. By providing a standard conversion that is as complete as possible the TF aims to improve interoperability of SW applications that use WordNet and simplify the choice between the existing RDF/OWL versions. We have based this conversion on examining the commonalities of previous conversions, extending them where necessary and making choices to suit different needs of application developers. This conversion may be used directly in Semantic Web applications, or as a source for modified WordNet versions (e.g. turning WordNet into an ontology). We have focused on staying as close to the original source as possible, i.e. reflect the original data model without interpretation. For example, whether or not (parts of) WordNet actually constitute a proper subclass hierarchy is outside the scope. The W3C hosts the conversion of version 2.0 of Princeton WordNet at the following URI:

	http://www.w3.org/2006/03/wn/wn20/	
	

Guide to the reader

This document is composed of three parts. The first part (Section two) has three subsections. The first subsection provides a Primer to the usage of the WordNet RDF/OWL representation and is intended as a convenient starting point for users and developers that have already worked with Princeton WordNet and have basic knowledge of RDF(S) and OWL, or those who have already worked with another RDF/OWL representation of WordNet. The second subsection provides an Introduction to the WordNet RDF/OWL schema. Those who are not familiar with WordNet should read the third subsection: Introduction to the WordNet datamodel, before reading the Primer. The second part of this document consists of Sections three and four which give more background information for those who are not familiar with WordNet and describe advanced options. It also provides more background to the decisions taken during conversion. The third part (the Appendices) contains detailed information on the RDF/OWL representation, versioning strategy and open issues.

This document is intended to reflect the consensus of the community using WordNet on the Semantic Web and the opinion of the TF on how best to represent the Princeton WordNet datamodel in RDF/OWL.

This document uses the following namespace abbreviations in URIs:

• rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns
• rdfs: http://www.w3.org/2000/01/rdf-schema
• owl: http://www.w3.org/2002/07/owl
• xsd: http://www.w3.org/2001/XMLSchema
• wn20instances: http://www.w3.org/2006/03/wn/wn20/instances/
• wn20schema: http://www.w3.org/2006/03/wn/wn20/schema/

2. Introduction to WordNet in RDF/OWL

Primer to using RDF/OWL WordNet

Overview of classes, properties and URIs

The WordNet schema has three main classes: Synset, WordSense and Word. The first two classes have subclasses for the lexical groups present in WordNet, e.g. NounSynset and VerbWordSense (see Figure one). Each instance of Synset, WordSense and Word has its own URI. There is a pattern for the URIs so that (a) it is easy to determine from the URI the class to which the instance belongs; and (b) the URI provides some information on the meaning of the entity it represents. For example, the following URI

 http://www.w3.org/2006/03/wn/wn20/instances/synset-bank-noun-2

is a NounSynset. This NounSynset contains a WordSense which is the first sense of the word "bank". The pattern for instances of Synset is: wn20instances: synset- %lexform%- %type%- %sensenr%. The %lexform% is the lexical form of the first WordSense of the Synset (the first WordSense in the Princeton source as signified by its "wordnumber", see Overview of the WordNet Prolog distribution). The %type% is one of noun, verb, adjective, adjective satellite and adverb. The %sensenr% is the number of the WordSense that is contained in the synset. This pattern produces a unique URI because the WordSense uniquely identifies the synset (a WordSense belongs to exactly one Synset). The pattern for URIs of WordSenses is the same, except that "synset" is replaced for "wordsense". Example:

http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1

The pattern for Words is: wn20instances: word- %lexform%. The %lexform% is the actual lexical form of the Word. For example:

http://www.w3.org/2006/03/wn/wn20/instances/word-bank

http://www.w3.org/2006/03/wn/wn20/schema/participleOf 

	Synset
		AdjectiveSynset
			AdjectiveSatelliteSynset
		AdverbSynset
		NounSynset
		VerbSynset
	
	WordSense
		AdjectiveWordSense
			AdjectiveSatelliteWordSense
		AdverbWordSense
		NounWordSense
		VerbWordSense
	Word
		Collocation

Example queries

Here follow some typical queries that can be posed on the WordNet RDF/OWL once it is loaded into a triple store such as SWI Prolog's Semantic Web library [SWI Prolog, 2006] or Sesame [Broekstra et al., 2002]. The examples are given in SPARQL query language [SPARQL, 2005]. Which query language is available to a user depends on the chosen triple store.

The following queries for all Synsets that contain a Word with the lexical form "bank":

	PREFIX  wn20schema: <http://www.w3.org/2006/03/wn/wn20/schema/>

	SELECT  ?aSynset
	WHERE   { ?aSynset wn20schema:containsWordSense ?aWordSense .
		  ?aWordSense wn20schema:word ?aWord .
		  ?aWord wn20schema:lexicalForm  "bank"@en-US }	

Notice the addition of the language tag using "@en-US". This is necessary in all queries for strings. Queries without the correct language tag do not return results.

The following queries for all antonyms of a specific WordSense ("bank"):

	PREFIX  wn20instances: <http://www.w3.org/2006/03/wn/wn20/instances/>
	PREFIX  wn20schema: <http://www.w3.org/2006/03/wn/wn20/schema/>

	SELECT  ?aWordSense
	WHERE   { wn20instances:wordsense-bank-noun-1 wn20schema:antonymOf ?aWordSense }	

The following queries for all Synsets that have a hypernym that is similar to some other Synset:

	PREFIX  wn20schema: <http://www.w3.org/2006/03/wn/wn20/schema/>

	SELECT  ?aSynset
	WHERE   { ?aSynset wn20schema:hyponymOf ?bSynset .
	          ?bSynset wn20schema:similarTo ?cSynset}	

Advanced features

Although WordNet is not a strict class hierarchy, it is possible to interpret it as such for certain types of applications. This is also possible with this version, see Advanced options.

The above section describes the Full version of RDF/OWL WordNet. There is also a Basic version for users who only require the Synsets for their application and wish to reduce the size of their triple store. See WordNet Basic and WordNet Full for more information.

Introduction to the WordNet RDF/OWL schema

The schema of the conversion has three main classes: Synset, Word and WordSense. Synset and WordSense have subclasses based on the distinction of lexical groups. For Synset this means subclasses NounSynset, VerbSynset, AdjectiveSynset (in turn subclass AdjectiveSatelliteSynset) and AdverbSynset. For WordSense this means subclasses NounWordSense, VerbWordSense, etcetera. Word has a subclass Collocation used to represent words that have hyphens or underscores in them. Word does not have subclasses such as VerbWord, because a word like "bank" is separate from its function as e.g. a verb or a noun. There is no representation for "all noun word senses with the lexical form 'bank'" and "all verb word senses with the lexical form 'bank'" in the original source, so there is no such class in the class hierarchy.

There are three kinds of properties in the schema. A first set of properties connects instances of the main classes together. The class Synset is linked to its WordSenses with the property containsWordSense, and WordSense to its Word with the property word. A second set of properties represents the WordNet relations such as hyponymy and meronymy. There are three kinds of relations: those that relate two Synsets to each other (e.g. hyponymOf), those that relate two WordSenses to each other (e.g. antonymOf) and a miscellaneous set (gloss and frame). See Conversion details for an overview of all relations. A third set of properties gives information on entities (they have XML Schema datatypes as their range such as xsd:string). Examples are synsetId that records the original ID given in Princeton WordNet to a synset, and the tagCount of a wordsense. The actual lexical form of a Word is recorded with the property lexicalForm. Each synset has an rdfs:label that is filled with the lexical form of the first word sense in the synset.

Major differences with previous versions

This conversion builds on three previous WordNet conversions, namely by:

1. Dan Brickley
2. Stefan Decker & Sergey Melnik
3. University of Neuchatel

1. does not model the hyponym hierarchy as a subclass hierarchy;
2. it represents words and word senses as separate entities with their own URI which makes it possible to refer to them directly;
3. contains all relations that are in Princeton WordNet;
4. provides OWL semantics in the form of inverse properties, definition of property characteristics (e.g. Symmetry) and property restrictions on classes.
5. can be used by both RDFS and OWL infrastructure

Introduction to the WordNet datamodel

The core concept in WordNet is the synset. A synset groups words with a synonymous meaning, such as {car, auto, automobile, machine, motorcar}. Another sense of the word "car" is recorded in the synset {car, railcar, railway car, railroad car}. Although both synsets contain the word "car", they are different entities in WordNet because they have a different meaning. More precisely: a synset contains one or more word senses and each word sense belongs to exactly one synset. In turn, each word sense has exactly one word that represents it lexically, and one word can be related to one or more word senses.

There are four disjoint kinds of synset, containing either nouns, verbs, adjectives or adverbs. There is one more specific kind of adjective called an adjective satellite. Furthermore, WordNet defines seventeen relations, of which ten between synsets (hyponymy, entailment, similarity, member meronymy, substance meronymy, part meronymy, classification, cause, verb grouping, attribute) and five between word senses (derivational relatedness, antonymy, see also, participle, pertains to). The remaining relations are "gloss" (between a synset and a sentence), and "frame" (between a synset and a verb construction pattern). There is also a more specific kind of word. Collocations are indicated by hyphens or underscores (an underscore stands for a space character), e.g. mix-up and eye_contact.

3. Selecting and Querying the appropriate WN version

Querying WordNet: offline and online

There are two ways to query RDF/OWL WordNet. The first option is to download the appropriate WordNet version (see WordNet Basic and WordNet Full) and load it into local processing software such as Sesame [Broekstra et al., 2002] or SWI-Prolog's Semantic Web library [SWI Prolog, 2006]. Query languages such as SPARQL [SPARQL, 2005] and Prolog programs may then be written to query the data.

Example SPARQL query on version 2.0 of RDF/OWL WordNet for all WordSenses which have a Word with the lexical form "bank":

	PREFIX  wn20schema: <http://www.w3.org/2006/03/wn/wn20/schema/>

	SELECT  theWordSense
	WHERE   { theWordSense wn20schema:word theWord .
		  theWord wn20schema:lexicalForm  "bank"@en-US }	
	

The second option is to query the on-line version of WordNet by doing an HTTP GET on an already known WordNet URI such as http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1. This HTTP GET request returns the Concise Bounded Description of the requested URI, which is an RDF graph that includes all statements in the whole WordNet RDF/OWL which have that URI as its subject (see [CBD, 2004] for details). This is a far less flexible approach because it is not possible to pose queries (e.g. a query for all synsets which contain the word "bank"). However, it does give a sensible set of triples to answer the question "tell me about this resource" if the user has no prior knowledge of this resource.

In version 2.0 of WordNet RDF/OWL, the HTTP GET on http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1 returns the following triples (the Concise Bound Description):

	wn20instances:wordsense-bank-noun-1	rdf:type			wn20schema:NounWordSense
	wn20instances:wordsense-bank-noun-1	wn20schema:inSynset		wn20instances:synset-bank-noun-2
	wn20instances:wordsense-bank-noun-1	wn20schema:word			wn20instances:word-bank
	wn20instances:wordsense-bank-noun-1	wn20schema:derivationallyRelated wn20instances:wordsense-bank-verb-3
	wn20instances:wordsense-bank-noun-1	wn20schema:derivationallyRelated wn20instances:wordsense-bank-verb-5
	wn20instances:wordsense-bank-noun-1	wn20schema:derivationallyRelated wn20instances:wordsense-bank-verb-6
	wn20instances:wordsense-bank-noun-1	rdfs:label			"bank"@en-US
	wn20instances:wordsense-bank-noun-1	wn20schema:tagCount		"883"@en-US	
	

Because this WordNet version does not have blank nodes and reified triples, the Consice Bounded Description of a the URI http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1 is the same as the result of the following SPARQL query:

	SELECT http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1 ?p ?x
	WHERE {<http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1> ?p ?x}
	

WordNet Basic and WordNet Full

The complete WordNet in RDF/OWL version described here consists of different files and is over 150 MB uncompressed RDF/XML in size. The required memory footprint when loading all files into software such as SWI-Prolog's Semantic Web library may be double that amount (figures vary for different software). To mitigate memory shortage problems and/or improve query response times we have made a separate file for each WordNet relation. The required footprint can be dimished by loading only those files/relations that are required by the application at hand.

WordNet is often used for a task known as sense disambiguation: the annotation of lexical forms in texts with a synset's ID (or, on the Semantic Web, its URI) to record the meaning of the lexical form (cf. [Ide and Véronis, 1998]). The disambiguation process consists of selecting the appropriate synset. In the sense disambiguation task (and others in which only the Synsets are of interest) the WordSenses and Words add memory footprint which may not be used. To keep the footprint small for such applications we provide a WordNet Basic version. This version consists of the synset file of the WordNet Full, an additional data file and a separate schema file. This last file contains one additional property called senseLabel (domain Synset and range xsd:string). It also omits classes and properties that are not used in the Basic version (e.g. WordSense and containsWordSense). The data file contains instances of the new property for each Synset in the synset file. The property value is filled with the lexical forms that are attached to Words in the Full version. When selecting candidate Synsets for a lexical form in a text one queries for senseLabels matching the lexical forms.

Like for WordNet Full, the Basic users can also limit the relations to those that are required for their task, with the caveat that the following relations are defined between WordSenses and are therefore useless to Basic users: derivational relatedness, antonymy, see also, participle, pertains to.

Each version has a separate RDF/OWL schema file. Although the same schema could be used for both versions because the Basic schema is a subset of the Full schema (apart from the additional property senseLabel), it may be confusing to have classes in the Basic schema which do not have instances. For clarity two separate schema files were made.

See also WordNet versions for a list of downloadable files.

Downloadable files

Below the files to download are listed for version 2.0 of WordNet RDF/OWL. Alternatively, an archive file for both Full and Basic versions are available from the following location: http://www.w3.org/2006/03/wn/wn20/download/.

WordNet 2.0 Full consists of the following three files plus any of the files that contain relations that are listed below.

• WordNet Full Schema
• Synsets
• WordSenses, Words, instances of properties that connect Synsets, Words, WordSenses

WordNet 2.0 Basic consists of the following files plus any of the files that contain relations between Synsets

• WordNet Basic Schema
• Synsets
• senseLabels

Files that contain relations between Synsets:

• hyponymy
• entailment
• similarity
• member meronymy
• substance meronymy
• part meronymy
• classification
• cause
• verb grouping
• attribute

Files that contain relations between WordSenses:

• derivational relatedness
• antonymy
• see also
• participle
• pertains to

Files that provide information on Synsets:

• glosses
• frames

4. Advanced options

OWL features

• owl:disjointWith statements between classes (e.g. Word and WordSense);
• owl:allValuesFrom restrictions (e.g. to define that AdjectiveSynsets only containWordSenses from the class AdjectiveWordSense)
• owl:someValuesFrom restrictions (e.g. to define that each Word has at least one value for the property sense from the class WordSense);
• owl:TransitiveProperty statements (e.g. to define that entails is a transitive relation;
• owl:inverseOf statements (e.g. to define that hypernymOf is an inverse of hyponymOf.

1. for transitive properties RDFS users have to construct the transitive closure of the graph themselves or write software that deals with transitivity while querying the data;
2. the WordNet data does not explicitly contain the inverse of e.g. hyponymOf. The inverse statement is only implied with the OWL statement hyponymOf owl:inverseOf hypernymOf. In other words, querying the hypernymOf relation will return no results when using software that is not OWL-aware. Therefore, RDFS users should not use the inverse properties because they do not yield query results. Because querying for X hypernymOf Y is just a syntactic variant of querying for Y hyponymOf X RDFS users do not have less information than OWL users. See Conversion details for a list of the inverse properties.

Using WordNet as a class hierarchy

For some purposes it may be useful to treat WordNet as a class hierarchy, where each (Noun)Synset is an rdfs:Class and the hyponym relationship is interpreted as the rdfs:subClassOf relationship. Do note that this is not a correct interpretation, e.g. the synset denoting the city "Paris" is a hyponym of the synset denoting "capital", but "Paris" should be an instance of "capital" instead of a subclass. Therefore this interpretation was not added in this version. Users that wish to interpret WordNet as a class hierarchy may add the following triples to their local triple store:

	<rdf:Description rdf:about="&wn20schema;Synset">
		<rdfs:subClassOf rdf:resource="&rdfs;Class" />
	</rdf:Description>

	<rdf:Description rdf:about="&wn20schema;hyponymOf">
		<rdfs:subPropertyOf rdf:resource="&rdfs;subClassOf" />
	</rdf:Description>

Appendix A: Requirements

Requirements that were observed while designing the RDF/OWL conversion are:

1. it should be a full conversion (i.e. be as complete as possible);
2. it should be convenient to work with;
3. it should provide OWL semantics while still being intepretable by pure RDFS tools (i.e. OWL semantics are provided but can be ignored).

The requirement of completeness has been fullfilled by carefully analyzing the conceptual model of WordNet as well as its data. Existing versions and their documentation were used to compare with our analyses. Some results of the analyses are documented here in Introduction to the WordNet datamodel, Relation to previous versions and Conversion details.

To satisfy the requirement of convenience we have provided:

• URIs for all entities (in particular WordSenses and Words) so that they are directly accessible;
• human-readable URIs;
• a Basic and Full version;
• separate files so only the necessary data for the application at hand needs be loaded;
• an on-line service that returns the Concise Bounded Description of any WordNet URI.

To satisfy the requirement of providing OWL semantics while still being interpretable by pure RDFS tools we have:

• provided the appropriate owl:disjointFrom statements;
• provided relevant OWL restrictions for each class;
• defined each class as being both of rdf:type owl:Class as well as rdfs:Class;
• defined each property as an rdf:Property as well as either owl:DatatypeProperty or owl:ObjectProperty;
• defined an owl:inverse for each property;
• defined the relevant property characteristics (e.g. owl:TransitiveProperty) for each property.

Appendix B: Versioning and redirection strategy

Appendix C: Overview of the WordNet Prolog distribution

The Prolog distribution consists of eighteen files: one file that represents synsets and then one for each of the seventeen relationships. The file with synsets contains Prolog facts such as:

  s(100003009,1,"living_thing",n,1,1).
  s(100003009,2,"animate_thing",n,1,0).

Each fact denotes exactly one word sense. The word senses with the same synset ID together form a synset. The two facts above together form the synset with the ID 100003009. The arguments of the clause are the following:

1. Synset ID: unique number for a synset. If ID starts with 1: synset contains only nouns 2: verbs 3: adjectives 4: adverbs
2. Word number: provides a number for the word sense within the synset (not ordered)
3. Lexical form: a string, possibly containing a hyphen (connecting collocated words);
4. Sense type: value is one of the set {n, v, a, s, r} which stands for noun, verb, adjective, adjective satellite and adverb, respectively;
5. Sense number: gives a number to the sense in which the lexical form is used that is unique for the sense type (e.g. there are ten different nouns with the lexical form "bank" numbered 1 to 10; there are eight different verbs with the lexical form "bank" numbered 1 to 8;
6. Tag count: frequency of this word sense measured against a text corpus.

Relations are identified by lists of facts like the following:

  hyp(100002056,100001740).
  mp(100004824,100003226).
  ant(100017087,1,100019244,1).

The first identifies a hyponymy relation between two synsets, the second part meronymy between synsets, the third antonymy between two word senses (second and fourth argument are word numbers). The documentation defines characteristics for each relationship, such as (anti-)symmetry, inverseness and value restrictions on the lexical groups (e.g. nouns, verbs) that may appear in relations. Most of these informally stated requirements can be formalized in OWL and are present in the conversion.

Investigation of the source files and documentation revealed several conflicts between source and documentation. For example, the order of synset arguments of the member meronym relation seems to be different than the documentation asserts. For each conflict we have proposed a solution. Details of the conversion can be found in Conversion details.

Appendix D: Conversion details

The following lists the definition of each Prolog clause as stated in the Prolog distribution's documentation, followed by notes on the meaning of the clause, an example, the mapping to RDF/OWL, OWL characteristics defined for the property, its inverse property and possible conflicts between documentation and source files.

The quotes from the Prolog documentation of Princeton WordNet contain Prolog variables written in lower-case (should start with upper-case letter) and sometimes two variables in one clause that are spelled exactly the same (should have different spelling because two different variables are intended). This has been corrected in the quotes shown below from that documentation for improved clarity.

s(Synset_ID,W_num,Word,Ss_type,Sense_number,Tag_count).

A s operator is present for every word sense in WordNet. In wn_s.pl, W_num specifies the word number for word in the synset.

The arguments of the clause are the following:

1. Synset ID: unique number for a synset. If ID starts with 1: synset contains only nouns 2: verbs 3: adjectives 4: adverbs
2. Word number: provides a number for the word sense within the synset (not ordered)
3. Lexical form: a string, possibly containing a hyphen (connecting collocated words), an underscore (stands for a space between two collocated words), and escape sequences to encode diacritics;
4. Sense type: value is one of the set {n, v, a, s, r} which stands for noun, verb, adjective, adjective satellite and adverb, respectively;
5. Sense number: gives a number to the sense in which the lexical form is used that is unique for the sense type (e.g. there are ten different nouns with the lexical form "bank" numbered 1 to 10; there are eight different verbs with the lexical form "bank" numbered 1 to 8;
6. Tag count: frequency of this word sense measured against a text corpus.

Each s(...) represents one word sense. All s(...) with the same ID together form the whole synset.

Maps to:

• Synset's subclasses: NounSynset, VerbSynset, AdverbSynset, AdjectiveSynset, AdjectiveSatelliteSynset
• Word
• WordSense
• containsWordSense(Synset,WordSense) - inverse: inSynset
• synsetId(Synset, xsd:nonNegativeInteger)
• tagCount(WordSense, xsd:nonNegativeInteger)
• word(WordSense, Word) - inverse: sense
• lexicalForm(Word, xsd:string) - superproperty: rdfs:label

g(Synset_ID,Gloss).

The g operator specifies the gloss for a synset.

Gloss is a string.

Maps to: wn:gloss(Synset_ID, Gloss)

hyp(Synset_ID_A,Synset_ID_B).

The hyp operator specifies that the second synset is a hypernym of the first synset. This relation holds for nouns and verbs. The reflexive operator, hyponym, implies that the first synset is a hyponym of the second synset.

Examples: hyp(100003226,100003009), [organism, living_thing]. hyp(100018827,100017572), [food, substance]. With "reflexive" inverseness is meant.

Maps to: wn:hyponymOf(Synset_ID_A, Synset_ID_B)

Property characteristics: Transitive

Inverse property: wn:hypernymOf

Superproperty: rdfs:comment

ent(Synset_ID_A,Synset_ID_B).

The ent operator specifies that the second synset is an entailment of first synset. This relation only holds for verbs.

Example: ent(200001740,200004923) [breathe, inhale], ent(200004701,200004127) [sneeze, exhale]

Maps to: wn:entails(Synset_ID_A, Synset_ID_B)

Property characteristics: Transitive

Inverse property: wn:entailedBy

sim(Synset_ID_A,Synset_ID_B).

The sim operator specifies that the second synset is similar in meaning to the first synset. This means that the second synset is a satellite the first synset, which is the cluster head. This relation only holds for adjective synsets contained in adjective clusters.

Maps to: wn:similarTo(Synset_ID_A, Synset_ID_B)

mm(Synset_ID_A, Synset_ID_B).

The mm operator specifies that the second synset is a member meronym of the first synset. This relation only holds for nouns. The reflexive operator, member holonym, can be implied.

Example: mm(100006026,107463651). [Person, People]. With "reflexive" inverseness is meant.

Documentation seems to be wrong here. Arguments are the other way around in Prolog source.

Maps to: wn:memberMeronymOf(Synset_ID_A, Synset_ID_B)

Inverse property: wn:memberHolonymOf

Superproperty: wn:meronymOf

ms(Synset_ID_A, Synset_ID_B).

The ms operator specifies that the second synset is a substance meronym of the first synset. This relation only holds for nouns. The reflexive operator, substance holonym, can be implied.

Documentation seems to be wrong here. Arguments are the other way around in Prolog source.

Example: ms(102073849,107118730). [oxtail, oxtail soup]. With "reflexive" inverseness is meant.

Maps to: wn:substanceMeronymOf(Synset_ID_A, Synset_ID_B)

Inverse property: wn:substanceHolonymOf

Superproperty: wn:meronymOf

mp(Synset_ID_A, Synset_ID_B).

The mp operator specifies that the second synset is a part meronym of the first synset. This relation only holds for nouns. The reflexive operator, part holonym, can be implied.

Documentation seems to be wrong here. Arguments are the other way around in Prolog source.

Example: mp(100004824,100003226). [cell, organism]

Maps to: wn:partMeronymOf(Synset_ID_A, Synset_ID_B)

Inverse property: wn:partHolonymOf

Superproperty: wn:meronymOf

der(Synset_ID_A, Synset_ID_B).

The der operator specifies that there exists a reflexive lexical morphosemantic relation between the first and second synset terms representing derivational morphology.

Documentation seems to be wrong here. The pattern is der(Synset_ID_A,Nr1,Synset_ID_B,Nr2). It seems that the numbers refer to WordSenses within the synsets. "Reflexive" probably means symmetric. Not sure if there are "doubles" in the prolog source like for other predicates (can be excluded when creating triples, but it produces the same triple so does not matter - one could argue whether to create the triple or not when its symmetric counterpart is missing in the source).

Example: der(100002645,3,201420446,4). [unit, unify]

Maps to: wn:derivationallyRelated(WordSense_ID_A, WordSense_ID_B)

Property characteristics: Symmetric

cls(Synset_ID_A, Synset_ID_B,Class_type).

The cls operator specifies that the first synset has been classified as a member of the class represented by the second synset.

Class_type: t:topical, u:usage, r:regional

Examples: cls(100004824,105681603,t), [cell, biology]. cls(100033885,106668368,u), [blind_alley, figure_of_speech]. cls(302439442,108349657,r), [outcaste, India].

Maps to:

• t: wn:classifiedByTopic(Synset_ID_A,Synset_ID_B)
• u: wn:classifiedByUsage(Synset_ID_A,Synset_ID_B)
• r: wn:classifiedByRegion(Synset_ID_A,Synset_ID_B)

Inverse properties: memberInTopic, memberInUsage, memberInRegion

Superproperty: classifiedBy - inverse: memberIn

cs(Synset_ID_A, Synset_ID_B).

The cs operator specifies that the second synset is a cause of the first synset. This relation only holds for verbs.

Examples:
cs(200018968,200014429). [cause_to_sleep, sleep/catch_some_Z's]
cs(200020073,200019883). [keep_up, sit_up/stay_up]
cs(200020689,200014429). [anaestesize/put_to_sleep/... , slumber/sleep/catch_some_Z's]

Documentation seems to be wrong here. Arguments are the other way around in Prolog source (e.g. anaethesize causes to sleep).

Maps to: wn:causes(A,B)

Inverse property: wn:causedBy

vgp(Synset_ID_A, Synset_ID_B).

The vgp operator specifies verb synsets that are similar in meaning and should be grouped together when displayed in response to a grouped synset search.

Documentation is unclear. The actual format in the file is vgp(sidA, W_num1, sidB, W_num2). But in wn_vgp.pl the W_num's are always '0'. This seems to mean that the relation holds for all the words in the synset, i.e. the relation holds between synsets.

It seems that the file contains all the symmetric definitions, i.e. vgp(A,0,B,0) means that the file also contains vgp(B,0,A,0). One of the two can be ignored. No problem if the conversion code does not do this, because the asserted double triple is exactly the same. See comment under "der".

Maps to: wn:sameVerbGroupAs(A,B)

Property characteristics: Symmetric

at(Synset_ID_A, Synset_ID_B).

The at operator defines the attribute relation between noun and adjective synset pairs in which the adjective is a value of the noun. For each pair, both relations are listed (ie. each synset_id is both a source and target).

Example: at(101028287,300455926). [mercantilism, commercial]

The inverse version is also listed, so both at(A,B) and at(B,A) are in the source file.

Maps to:

• if synset A is a noun (so B is adjective): wn:attribute(Synset_ID_A,Synset_ID_B)
• if synset A is adjective: wn:attribute(Synset_ID_B,Synset_ID_A)

Inverse property: wn:attributeOf

ant(Synset_ID_A,W_num_1,Synset_ID_B,W_num_2).

The ant operator specifies antonymous words. This is a lexical relation that holds for all syntactic categories. For each antonymous pair, both relations are listed (ie. each Synset_ID,W_num pair is both a source and target word.)

The synset_id W_num identifies a word sense.

Maps to: wn:antonymOf(WordSense1, WordSense2)

Property characteristics: Symmetric

sa(Synset_ID,W_num,Synset_ID,W_num).

The sa operator specifies that additional information about the first word can be obtained by seeing the second word. This operator is only defined for verbs and adjectives. There is no reflexive relation (ie. it cannot be inferred that the additional information about the second word can be obtained from the first word).

The synset_id W_num identifies a word sense. The statement "no reflexive relation" probably means that the relation is not symmetrical.

Maps to: wn:seeAlso(WordSense1, WordSense2)

ppl(Synset_ID,W_num,Synset_ID,W_num).

The ppl operator specifies that the adjective first word is a participle of the verb second word.

The Synset_ID W_num identifies a word sense.

Maps to: wn:participleOf(WordSense1, WordSense2)

Inverse property: wn:participle

per(Synset_ID_A,W_num,Synset_ID_B,W_num).

The per operator specifies two different relations based on the parts of speech involved. If the first word is in an adjective synset, that word pertains to either the noun or adjective second word. If the first word is in an adverb synset, that word is derived from the adjective second word.

Documentation seems to be wrong here. The relation holds between wordsenses, not words. We also split the relation into two properties, as the documentation already indicates.

Maps to:

• A is adjective(satellite), B is noun or adjective(satellite): wn:adjectivePertainsTo(Synset_ID_A,Synset_ID_B)
• A is adverb, B is adjective(satellite): wn:adverbPertainsTo(Synset_ID_A,Synset_ID_B)

Inverse property: @@TODO

fr(Synset_ID,F_num,W_num).

The fr operator specifies a generic sentence frame for one or all words in a synset. The operator is defined only for verbs.

Example: fr(200610468,8,1).

Maps to: wn:frame(VerbWordSense, xsd:string)

The Synset_ID and W_num together identify a VerbWordSense that is associated with a particular sentence in which the verb can be filled in. If the W_num is zero, the sentence applies to all senses in the Synset. In that case we generate a wn:frame for each sense in the Synset.

A problem in conversion of this Prolog file is that the actual sentences are only identified by a number (F_num), and not present in the actual source. The actual sentences and their number (F_num) are present in the Unix version of Princeton WordNet, in a file called frames.vrb. Two example lines (there are 35 lines) from that file: "26 Somebody ----s that CLAUSE", "27 Somebody ----s to somebody". We have converted these lines into a Prolog clause sen(F_Num, String) and stored them in a file sen.pl, to be able to do the conversion.

Additional properties

meronymOf

Subproperties: partMeronymOf, memberMeronymOf, substanceMeronymOf

Inverse: wn:holonymOf

classifiedBy

Subproperties: classifiedByTopic, classifiedByUsage, classifiedByRegion

Inverse: classifies

Language tag

It is good practice to use the xml:lang attribute to specify the language in which literals are written. Currently all the RDF files are given a language tag on the document level (in RDF tag) for this purpose.

Use of rdfs:label

It is good practice to give labels to instances, in this case of Word, WordSense and Synset. For Word this is solved by adding wn:lexicalForm rdfs:subpropertyOf rdfs:label. For WordSense the contents for the rdfs:label is chosen by copying the contents of the wn:lexicalForm of the Word. For Synset the first word (according to W_num) is chosen as the label. As there is no preferred status of one WordSense within a Synset (the W_num does not seem to have a specific meaning) this is an arbitrary choice.

Conversion program

The conversion file can be found at: http://www.w3.org/2006/03/wn/wn20/convertwn20.pl. The source code contains instructions for use.

The conversion program makes use of the open-source SWI-Prolog programming language and its Semantic Web library.

The program can be used for conversion of new Princeton WordNet versions to RDF/OWL as long as the format and semantics of the Prolog source files are not changed.

Appendix E: Possible mappings to SKOS

"SKOS Core provides a model for expressing the basic structure and content of concept schemes such as thesauri, classification schemes, subject heading lists, taxonomies, 'folksonomies', other types of controlled vocabulary, and also concept schemes embedded in glossaries and terminologies." [SKOS Core Guide, 2005]. Because WordNet may be considered a complex kind of thesaurus, it is natural to try to represent it using SKOS. This version has not concentrated on such a representation, but we list some options for a future version in the SKOS schema.

The central class of SKOS is skos:Concept. Its instances are connected using the skos:broader/skos:narrower properties. To each concept one can attach exactly one skos:prefLabel and zero or more skos:altLabels.

The term "mapping" can have two meanings in this context. In the first meaning, the schema of WordNet (i.e. its classes and properties) is mapped to the SKOS classes and properties using rdfs:subClassOf, rdfs:subPropertyOf, owl:equivalentClass and owl:equivalentProperty. This is only possible without loss of information if the WordNet schema is equal to or is a strict specialization of SKOS. In the second meaning, a set of rules is specified that converts WordNet into instances of the SKOS schema. This is a more flexible approach and allows for more complex mappings (mappings other than property/class equalities and strict specialization).

A first choice concerns what WordNet class(es) to map to skos:Concept.

[@@TODO. See Appendix "Issues"]

Appendix F: Relation to previous versions

This conversion builds on three previous WordNet conversions, namely by:

1. Dan Brickley;
2. Stefan Decker & Sergey Melnik;
3. University of Neuchatel;

A fourth conversion by University of Chile was done in parallel with the activities of this TF.

In this document we have not tried to come up with a completely new conversion. Rather, we have studied these existing conversions, filled in some gaps and made a few different decisions. Below we discuss the differences per conversion.

The conversion by Brickley is a partial conversion, as only the noun-part of WordNet is converted. Of the relations only the hypernym relation is converted. Brickley converts the noun hierarchy into rdfs:Classes and the hyponym relationship into rdfs:subClassOf. This is an attractive interpretation, but we argue that not all hyponyms can be interpreted in that way. For example, the synset denoting the city "Paris" is a hyponym of the synset denoting "capital", but "Paris" should be an instance of "capital" instead of a subclass. An attempt to provide a consistent semantic translation of hyponymy has been done [Gangemi, 2003], but in this work we explicitly avoid semantic translation of the intended meaning of WordNet relations.

The conversion by Decker & Melnik is also a partial one. It does convert all synset types, but only three of the WordNet relations. Another difference is that it attaches word forms as labels to the Synset instances. Hence WordSenses and Words do not have a URI.

The two previous conversions are based on an older version of Princeton WordNet and are not updated as far as the TF can tell. Both provide RDFS semantics, but not OWL semantics.

The conversion of Neuchatel is close to the one in this document. It has roughly the same class hierarchy, with two exceptions. Firstly, it contains a class to represent word senses, but does not have a separate class for words. Secondly, it defines classes like "Nouns_and_Adjectives" (with subclasses Noun and Adjective). The "Nouns_and_Adjectives" classes are used in restriction definitions, where we have chosen to use owl:unionOf, because it better reflects the actual semantics. Aonther difference with this conversion is that Neuchatel is in pure OWL (e.g. all properties are either owl:ObjectProperty or owl:DatatypeProperty), while the conversion of the TF is both in RDFS and OWL (e.g. each OWL property is also defined to be an rdfs:Property). The conversion by the TF splits some relations into sub-relations, because their semantics warranted such a separation. For example, the Prolog relationship per denotes (a) a relation between an adjective and a noun or adjective or (b) a relation between an adverb and an adjective. We convert per into adjectivePertainsTo and adverbPertainsTo. The Neuchatel conversion does not provide sub-relations, and omits relations "derivation" and "classification". and also does not provide inverses for all relationships. The conversion uses hash URIs, while the TF's uses slash URIs (the benefits of the slash approach are described in Hash versus slash URIs). The main advantages of the conversion by the TF in comparison to the Neuchatel conversion is that it is more complete, uses slash URIs, is interpretable by both RDFS and OWL infrastructure, and represents Words as first-class citizens.

Representing words as first-class citizens allows fine-grained mappings to WordNets in other languages. Future integration of WordNet with WordNets in other languages can be done on three levels: relating Synsets, relating WordSenses and relating Words from the different WordNets to each other. However, as the other conversions do not provide URIs for words, these only allow integration on the first two levels. For future integration of WordNet with other multilingual resources it is essential that one can refer to two different words with the same lexical form, or two words with a different lexical form but similar meanings.

The conversion by University of Chile was made in parallel to the efforts of this TF (see e.g. this mail on the [email protected] mail archive). It has almost the same class hierarchy as this conversion; only the class Collocation is not present. The schema is modelled in RDFS, so it does not define restrictions, disjointness axioms, property characteristics and inverse properties. It does not have the superproperties for WN relations that we have introduced, and it uses hash URIs. The main technical advantages of the version by this TF is that it includes OWL semantics and that it uses slash URIs.

The previously mentioned conversions do not convert the frame sentences, while the TF's conversion and the conversion of University of Chile include them.

A practical advantage of the TF's conversion over the other conversion is the availability of a Basic and Full version and separate files for the WN relations.

In summary, the advantages of the TF's conversion over other versions are that it is complete, uses slash URIs, provides OWL semantics while still being interpretable by RDFS infrastructure, provides a Basic and Full version, and provides URIs for words.

Appendix G: Introducing URIs for Synsets, WordSenses, Words

We have chosen to introduce identifiers for the instances of classes Synset, WordSense and Word. We use the base uri a locally unique ID. Three kinds of entities need a URI: instances of the classes Synset, WordSense and Word. Instead of generating any unique ID we have tried to use IDs derived from information in the source and also tried to make them human-readable. Because the IDs have distinct syntactic patterns, it is easy to identify the type of the resource (Synset, WordSense or Word) by examining the URI. The patterns are described in Primer to using RDF/OWL WordNet.

We use two different namespaces: one for the schema and one for the instances. This makes it possible to manage the schema separately from the instances.

Some words contain characters that are not allowed in NCNames. In order to generate a correct URI we changed the following characters into underscores: '/', '\','(', ')' and ' ' (space). For example, the URI for the word "read/write_memory" becomes:

http://www.w3.org/2006/03/wn/wn20/instances/word-read_write_memory

The motivation for representing words as instances of a class with their own URIs instead of as labels or blank nodes is discussed in Relation to previous versions.

Hash versus slash URIs

	http://wordnet.princeton.edu/wn/wordsense-bank-noun-1
	
	http://wordnet.princeton.edu/wn#wordsense-bank-noun-1
	
	

• a graph that contains a pre-defined set of properties if the resource has values for them (e.g. rdf:type, rdfs:subClassOf);
• all statements connected to the resource with some offset, e.g. everything connected in at most two steps;
• the Concise Bounded Description of the URI [CBD, 2005];
• the Symmetric Concise Bounded Description of the URI [CBD, 2005].

The difference between the two last ones is that the Symmetric CBD not only includes statements for which the URI is the subject, but also those for which the URI is the object. We have chosen for the CBD of the URI because it "constitutes a reasonable default response to the request 'tell me about this resource'" [CBD, 2005].

Note that a variant of Recipe 5 in [Recipes, 2006] may be used to implement the HTTP GET on these WN URIs.

Appendix H: Internationalization

This section contains two language related topics. First of all, Princeton WordNet is a source that documents American English. To reflect this in the conversion, all RDF documents of this conversion are declared to be written in American English by adding the xml:lang='en-US' to the RDF tag of all WordNet files.

Secondly, it is desirable to be able to integrate other existing WordNets in other languages in the future (for a list of available WordNets see http://www.globalwordnet.org/gwa/wordnet_table.htm). Although this TF does not have the goal of performing such integration, it has the intention of making such integration possible with this RDF/OWL version of Princeton WordNet. Integration of WordNets implies creating mappings between entities in the WordNets to indicate lexico-semantic relationships between them, e.g. a property that signifies that the meanings of two Synsets overlap. The entities that represent language concepts that should be able to map are instances of the classes: Synset, WordSense and Word. To this end this conversion supplies URIs for instances of all three classes. We have not given the URIs in this conversion a part that encodes the language, such as http://www.w3.org/2006/03/wn/wn20/en/synset-bank-noun-2. The reason is that two WordNets in different languages require different base URIs. This alone guarantees uniqueness of e.g. the Word "chat" in an English WordNet and the word "chat" (cat) in a French WordNet. Identification of the language a particular word belongs to can also be done by using the xml:lang tag.

Appendix I: Open Issues

Princeton based URIs

This document was originally written as if there will be an RDF/OWL version of each Princeton WordNet edition. Is this feasible? There should be an institute who takes responsibility for not only creating new versions but also making them available for online use. Concerning creating new versions: when new versions by Princeton only differ from previous in its content, then this is just a matter of running the Prolog conversion program and putting the new version online. This document describes the convertion of Princeton version 2.0. In version 2.1 there is at least one structural difference, namely the introduction of a "instanceOf" relationship.

Maintanance / publishing newer versions

This version is based on the Princeton WordNet 2.0. Is it feasible and desirable to find an institute willing to commit to maintaining WordNet for a longer period, say two years? This also entails bringing out a new RDF/OWL version for each new Princeton version. Without such a commitment the RDF/OWL version presented in this document will be outdated within one or two years because of updates to the original source.

Serving old and new versions

An idea suggested in an earlier version of this Draft was to introduce a new base URI for each new version, and to use redirection from a stable URI which redirects to the newest available version of RDF/OWL WordNet. For example, the URI

http://wordnet.princeton.edu/wn/

http://wordnet.princeton.edu/wn20/

http://wordnet.princeton.edu/wn20/

http://wordnet.princeton.edu/wn/

http://wordnet.princeton.edu/wn20/

303 redirects

URIs as primitive queries

URIs can be used as a means of primitive queries. The following URI in WordNet refers to the first NounWordSense of the word "bank", which is an RDF node in WN:

	http://www.w3.org/2006/03/wn/wn20/instances/wordsense-bank-noun-1.
	

	http://www.w3.org/2006/03/wn/wn20/instances/wordsenseset-bank.
	

A full SPARQL service for WN can also address this need, but this is a nice alternative that does not require all agents to understand SPARQL. Another reason is that running a SPARQL service requires more resources from the hosting institute. However, this second (type of) URI does not refer to any RDF node or RDF arc. Is this use of URIs "accepted practice" (or could become such a thing) or "should be avoided at all costs" because the approach mixes the naming of nodes with naming sets of nodes? See also http://lists.w3.org/Archives/Public/public-swbp-wg/2006Mar/0076.html. If it is a good idea, how should the URIs be constructed?

W_num and sense_number

Each WordSense in a Synset has a "W_num" (starting from 1). It seems that this is not essential ordering information (i.e. only used to distinguish between word senses in the prolog source), so it has not been included in the conversion. Similar point for the sense_number in the prolog source.

Have to check with Princeton if indeed this information is not vital and also check with user community if they are not using these numbers.

Generating instances of symmetric properties

The Prolog source sometimes contains symmetrical pairs, e.g. the source file for antonyms should contain ant(A,B) but also ant(B,A) according to the documentation. However, the conversion program finds clauses where this is not the case. Currently the program does NOT add an antonym in the RDF for such cases.

Need to check with Princeton if these are either omissions or errors.

Frames

There seems to be additional semantics in the frame sentences, and hence could be alternative ways to convert the sentences. For example, the structure of the sentences seems to fall into two parts, plus an optional part. For example, the frame sentence "Somebody ----s that CLAUSE" has a prefix, a postfix and a lexical category. These could be extracted and added as properties to an instance of a class Frame. It should be checked with users if this information is useful and with Princeton about the meaning of the lexical categories.

Other

• Should wn:seeAlso be a subproperty of rdfs:seeAlso? If so, other properties could also be appropriate subproperties of rdfs:seeAlso, as the semantics of the triple S rdfs:seeAlso O is "... that the resource O may provide additional information about S." [RDF Primer, 2004]
• We assume wn:sameVerbGroupAs is between synsets, but have to check with Princeton if it should be between WordSenses.
• A strategy is required (test set?) to check whether the conversion program's output is correct.
• Should this document contain information on the relation to SKOS? There are problems in seeing WN as a strict specialization of SKOS: Should all WN classes be regarded as subclasses of skos:Concepts or should only Synsets or WordSenses be regarded as skos:Concepts? Also difficult choice regarding what to map to skos:prefLabel/skos:altLabel. WordSenses have equal status in WN, no one preferred over the other. If you choose not to make all classes of WN subclass of skos:Concept then you lose information. So it seems impossible to define WN as strict specialization of SKOS. The remaining solution is a rule-based mapping. But again the decision remains what is an appropriate mapping of the main classes to skos:Concept. It seems a choice between Synsets and WordSenses is necessary. In the former case, it seems logical to map the hyponym relation to skos:broader. It the latter case, a possibility is to put the WordSenses in a skos:Collection and hierarchically relate these collections based on the hyponymy relation. However, this would result
• Document now contains a set of downloadable files for version WordNet RDF/OWL version 2.0. Should this be moved to a separate WN download document?
• This version is not OWL DL, because rdfs:label is used on instances. Should the description of the benefits of the OWL definitions in the schema be changed?

Appendix J: Change Log

• Added reference to 'Best Practices Recipes for Publishing RDF Vocabularies'
• Moved all references to Princeton based URIs in favour of W3C based URIs because they will not return 404 (Princeton will be brought into the loop as late as possible)
• Moved all material on redirection to the Issues list, as there is no consensus on if and how redirection may be used
• Moved all material on management issues (who will maintain WN) to the Issues list
• Moved all material on versioning (new versions and how they are published and made available) to the Issues list
• Fixed error in description of the "per" operator; it is between wordsenses, not between synsets

• Added class AdjectiveSatelliteWordSense
• removed inverses from RDFS part
• Changed URI formatting to a more readable and versatile form
• Moved several "issues" as documented in the text itself to Appendix H (now called Appendix I)
• New Appendix on internationalization (new Appendix H)
• Added missing superproperties to Appendix D
• Added explanation on how frame sentences were converted to Appendix D
• Merged Sects. 2-4 into one
• Merged Sects. 5-7 into one
• Moved discussion on URIs in Sect. 5 to Appendix G; merged the material with the appendix
• Extended explanations in Appendix F (other conversions); indicated advantages of this conversion

• introduction rewritten; improved scope and added guide to the reader
• created new "Primer" section
• moved material in introduction on Prolog format to appendix
• moved material in introduction on requirements to appendix and edited material
• added new section on "querying wordnet online and offline"
• added new section on "wordnet basic and wordnet full"
• added new section on "wordnet versions"
• added new section on "advanced options"
• added new appendix on "versioning and redirection strategy"
• added new appendix on "possible mappings to SKOS"
• added new appendix on "relation to previous versions" which contains new comparison with a fourth wordnet version from university of chile
• resolved many open issues including "hash vs. slash issue"

Appendix K: References

[Brickley, 1999] D. Brickley. Message to RDF Interest Group: "WordNet in RDF/XML: 50,000 RDF class vocabulary". http://lists.w3.org/Archives/Public/www-rdf-interest/1999Dec/0002.html See also http://xmlns.com/2001/08/wordnet/.

[Brickley, 2002] Dan Brickley, RDFWeb: co-depiction photo metadata http://rdfweb.org/2002/01/photo/.

[Brickley and Miller, 2005] Dan Brickley and Libby Miller, FOAF Vocabulary Specification Namespace Document 27 July 2005 http://xmlns.com/foaf/0.1/

[Broekstra et al., 2002] Jeen Broekstra, Arjohn Kampman, Frank van Harmelen. Sesame: An Architecture for Storing and Querying RDF and RDF Schema [PDF] In Proceedings of the First International Semantic Web Conference (ISWC 2002), Sardinia, Italy, June 9-12 2002, pg. 54-68. Springer-Verlag Lecture Notes in Computer Science (LNCS) no. 2342.

[CBD, 2005] CBD - Concise Bounded Description W3C Member Submission 3 June 2005; http://www.w3.org/Submission/2005/SUBM-CBD-20050603/

[Decker & Melnik] S. Decker and S. Melnik. WordNet RDF representation. http://www.semanticweb.org/library/

[Fellbaum, 1998] C. Fellbaum. WordNet: An Electronic Lexical Database. MIT Press, 1998.

[Gangemi, 2003] A. Gangemi, N. Guarino, C. Masolo, and A. Oltramari. Sweetening WORDNET with DOLCE. AI Magazine, 24(3):13-24, 2003.

[Graves, 2005] Alvaro Graves http://www.dcc.uchile.cl/~agraves/wordnet/

[Guarino et al., 1999] Nicola Guarino, Claudio Masolo, and Guido Vetere. Ontoseek: Content-based access to the web. IEEE Intelligent Systems, 14(3):70--80, May/June.

[Hollink et al., 2003] L. Hollink, A. Th. Schreiber, J. Wielemaker, and B. J. Wielinga. Semantic annotation of image colletions. In S. Handschuh, M. Koivunen, R. Dieng, and S. Staab, (eds), Knowledge Capture 2003 - Proceedings Knowledge Markup and Semantic Annotation Workshop, pages 41--48.

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