Verifying Integrity Constraints of a RDF -based W ord N et (2016)

Abstract: This paper presents our first attempt at verifying integrity constraints of our openWordnet-PT against the ontology for Wordnets encoding. Our wordnet is distributed in Resource Description Format (RDF) and we want to guarantee not only the syntax correctness but also its semantics soundness.

## introduction lexical databases are organized knowledge bases of information about words. these resources typically include information about the possible meanings of words, relations between these meanings, definitions and phrases that exemplify their use and maybe some numeric grades of confidence in the information provided. the princeton english wordnet @xcite , is probably the most popular model of a lexical knowledge base. our main goal is to provide good quality lexical resources for portuguese, making use, as much as possible, of the effort already spent creating similar resources for english. thus we are working towards a portuguese wordnet, based on the princeton model @xcite . in a previous paper @xcite we reported the new web interface 1 for searching, browsing and collaborating on the improvement of openwordnet-pt. correcting and improving linguistic data is a hard task, as the guidelines for what to aim for are not set in stone nor really known in advance. while the wordnet model has been paradigmatic in modern computational lexicography, this model is not without its failings and shortcomings, as far as specific tasks are concerned. also it is easy and somewhat satisfying to provide copious quantitative descriptions of numbers of synsets, for different parts-of-speech, of 1 http://wnpt.brlcloud.com/wn/ triples associated to these synsets and of intersections with different subsets of wordnet, etc. however, the whole community dedicated to creating wordnets in other languages, the global wordnet association foot_0 , has not come up with criteria for semantic evaluation of these resources nor has it produced, so far, ways of comparing their relative quality or accuracy. thus qualitative assessment of a new wordnet seems, presently, a matter of judgment and art, more than a commonly agreed practice. believing that this qualitative assessment is important, and so far rather elusive, we propose that having many eyes over the resource, with the ability to shape it in the directions wanted, is a main advantage. this notion of volunteer curated content, as first and foremost exemplified by wikipedia, needs adaptation to work for lexical resources. our openwordnet-pt was distributed since its beginning in rdf, following the semantic web standards proposed by tim berners-lee @xcite . nevertheless, so far, although we make available not only the data but also its model definition in owl foot_1 , we have not addressed the task to confront the data with its model to guarantee that data is compliance with the defined model. this is the main contribution of this paper. ## openwordnet-pt the openwordnet-pt @xcite , abbreviated as openwn-pt, is a wordnet originally developed as a projection of the universal wordnet (uwn) @xcite . its long term goal is to serve as the main lexicon for a system of natural language processing focused on logical reasoning, based on representation of knowledge, using an ontology, such as sumo @xcite . openwn-pt has been constantly improved through linguistically motivated additions and removals, either manually or by making use of large corpora. this is also the case for the lexicon of nominalizations, called nomlex-pt, that is integrated to the openwn-pt @xcite . one of the features of both resources is to try to incorporate different kinds of quality data already produced and made available for the portuguese language, independent of which variant of portuguese one considers. the philosophy of openwn-pt is to maintain a close connection with princeton's wordnet since this minimizes the impact of lexicographical decisions on the separation or grouping of senses in a given synset. such disambiguation decisions are inherently arbitrary @xcite , thus the multilingual alignment gives us a pragmatic and practical solution. it is practical because princeton wordnet remains the most used lexical resource in the world. it is also pragmatic, since those decisions will be more useful, if they are similar to what other wordnets say. of course this does not mean that all decisions will be sorted out for us. as part of our processing is automated and errorprone, we strive to remove the biggest mistakes created by automation, using linguistic skills and tools. in this endeavor we are much helped by the linked data philosophy and implementation, as keeping the alignment between synsets is facilitated by looking at the synsets in several different languages in parallel. for this we make use of the open multilingual wordnet's interface @xcite through links from our interface. this lexical enrichment process of openwn-pt reported in employs three language strategies: (1) translation; (2) corpus extraction; and (3) dictionaries. the interested reader will find more details in @xcite . the essential fact is that given the constant release of new versions of our openwn-pt, we must ensure the quality of the data that we make available. by quality here we mean not only the data content but its encoding consistency. ## openwordnet-pt in rdf as reported in @xcite , since its beginning openwn-pt is distributed using the resource description format (rdf) @xcite . we have being following the increasingly popular way of addressing the is-sue of interoperability by relying on linked data and semantic web standards such as rdf and owl @xcite , which have led to the emergence of a number of linked data projects for lexical resources (de @xcite . the adoption of such standards not only allows us to publish both the data model and the actual data in the same format, they also provide for instant compatibility with a vast range of existing data processing tools and storage systems, triple stores, providing query interfaces based on the sparql standard @xcite . to encode any data in rdf, one needs to decide which classes and properties (vocabulary) will be used. the adoption of already defined vocabularies helps on the data interoperability since these makes data easily integrate with other resources. we chose to use the vocabulary for wordnets encoding proposed by @xcite which is based on princeton wordnet 2.0. their work includes (1) a mapping of wordnet 2.0 concepts and data model to rdf/owl; (2) conversion scripts from the wordnet 2.0 prolog distribution to rdf/owl files; and (3) the actual word-net 2.0 data. the suggested representation stayed as close to the original source as possible, that is, it reflects the original wordnet data model without interpretation. the wordnet schema proposed by @xcite has three main classes: synset, wordsense and word. the first two classes have subclasses for each lexical group present in wordnet. each instance of synset, wordsense and word has its own uri. since @xcite is based on princeton wordnet 2.0, its use required few adaptations. our first decision was to adapt the word-net 2.0 vocabulary to version 3.0, having our own uris for all entities (classes and properties). we converted the wordnet 3.0 data to rdf in such a way that openwn-pt is an extension of word-net 3.0, with its instances, connected to princeton instances through owl:sameas relations. that is, for each princeton wordnet synset, we created an equivalent synset in openwn-pt synset, with no additional synsets or relations so far. given that openwn-pt's rdf is only useful together with an rdf version of princeton wordnet and we wanted to ensure that all information in the wordnet 3.0 distribution was transformed to rdf, we wrote our own script to translate the princeton wordnet 3.0 data files to rdf so they can be distributed alongside openwn-pt. 4 . for the uri schema, we adopted a similar approach of @xcite of pattern for the uris by classes. moreover, we created the domain https://w3id.org/own-pt/ under our control as suggested by the linked data principles. in given an ontology encoded in owl (lite or dl) one can use dl reasoners for different tasks such as: concepts consistency checking, query answering, classification, etc. in particular, classification amounts to placing a new concept expression in the proper place in a taxonomic hierarchy of concepts, it can be accomplished by verifying the subsumption relation between each defined concept in the hierarchy and the new concept expression. validating an ontology means to guarantee that all concepts are satisfiable, that is, the concepts definition do not contain contradictions. the basic reasoning task in an abox is instance checking, which verifies whether a given individual is an instance of (or belongs to) a specified concept. although other reasoning services are usually employed, they can be defined in terms of instance checking. among them we find knowledge base consistency, which amounts to verifying whether every concept in the knowledge base admits at least one individual; realization, which finds the most specific concept an individual object is an instance of; and retrieval, which finds the individuals in the knowledge base that are instances of a given concept (query answering). in some use cases, we need a method to validating the rdf data regarding a given model. in this case, owl users intend owl axioms to be interpreted as constraints on rdf data @xcite . for that, one has to define a semantics for owl based on the closed world assumption and a weak variant of the unique name assumption @xcite . owl default semantics adopts the open world assumption (owa) and does not adopt the unique name assumption (una). these design choices make it very difficult to treat these axioms as ics. on the one hand, due to owa, a statement must not be inferred to be false on the basis of failures to prove it; therefore, the fact that a piece of information has not been specified does not mean that such information does not exist. on the other hand, the absence of una allows two different constants to refer to the same individual. in the next section, we present some preliminary experiments with tbox and abox consistency check and integrity constraints (ic) validation in our rdf/owl data, reporting our experience with most well-know freely available tools. nevertheless, it is important to emphasize the capabilities that semantic web technologies that ex-ceed the currently mainstream technologies. most research groups that are still using xml for lexical resources distribution would argue that xml schema (fallside and walmsley, 2004) can ensure some constraints that we verify in the next section. relational database users would argue that sql is an already mature and declarative query language. we argue that owl/rdf brings much more expressivity allowing much more robust and semantics aware verification with queries such as: select ?w ?ws1 ?ws2 { ?ss1 wn30:containswordsense ?ws1 . ?ws1 wn30:word ?w . ?ss2 wn30:containswordsense ?ws2 . ?ws2 wn30:word ?w . ?ss1 wn30:hyponymof* ?ss2 . } in the sparql query above, we are asking for words that occur repeated in the same branch of the hierarchy of synsets formed by the wn30:hyponymof transitive closure. ## validating openwn-pt we were interested in checking our rdf and owl files against a wide variety of errors, both minor and major and to increase our coverage we opted to use a variety of reasoners. we started with protégé 7 , which is an ontology editor that among other features has interface with two well-know dl reasoners: fact++ @xcite and hermit @xcite . starting in version 4, protégé also gives us the opportunity to search for explanations that caused an inconsistency @xcite . @xcite and pellet @xcite are reasoners that have this feature builtin. in order to verify own-pt files we needed to combine all files in https://github. com/own-pt/openwordnet-pt and the simple knowledge organization system (skos) 8 ontology file. there are a number of tools available for this, we chose rdf pro @xcite , which was the fastest in our benchmarks. the errors found can be categorized in three different classes: datatype errors, domain and range errors, structural errors. ## conclusion the use of different systems, with different functionalities, give us more confidence in our validations. unfortunately, it required considerable ef-11 http://www.stardog.com . performance is still an issue. some of these experiment took hours to complete, in a relatively simple ontology. it looks like most of dl reasoners are not prepared to handle large aboxes. most dl reasoners are based on some variation of tableaux or other refutation based procedure @xcite . prove by refutation does not preserve information and tableaux proofs usually have exponential size. in the future, we hope to implement a proof-theoretical based reasoner for dl based on @xcite . it is also worthy to mention that the tools that we tested do not always have an user-friendly interface, making adoption for people outside the area difficult. reasoning with closed world assumption for icv is a future work given the problems that we faced with stardog. finally, dl learning @xcite and shapes constraint language @xcite are another possible interesting techniques to explorer. the former would allow us to extract the minimum required tbox for a given abox, the latter would be an alternative language for expressing constraints. aknowledgements this work used protégé resource, which is supported by grant gm10331601 from the national institute of general medical sciences of the united states national institutes of health.