Towards a Linking between W ord N et and W ikidata (2021)

Abstract: WordNet is the most widely used lexical resource for English, while Wikidata is one of the largest knowledge graphs of entity and concepts available. While, there is a clear difference in the focus of these two resources, there is also a significant overlap and as such a complete linking of these resources would have many uses. We propose the development of such a linking, first by means of the hapax legomenon links and secondly by the use of natural language processing techniques. We show that these can be done with high accuracy but that human validation is still necessary. This has resulted in over 9,000 links being added between these two resources.

## introduction english wordnet @xcite , derived from princeton wordnet @xcite foot_0 , is the most complete wordnet for english, while wikidata foot_1 provides one of the largest collection of encyclopedic facts in machine readable form. moreover, as wikidata is an open resource to which anyone can contribute and data is published without any license, it is quickly becoming a central database to which knowledge graphs can link. as such, a linking between word-net and wikipedia would provide value to users of both resources, and potentially make it easier to extend wordnet in the future with new synsets. however, there are significant differences between the scope of the two projects, with wordnet specialising on providing information about the use of words in english, including verbs, adjectives and adverbs, whereas wikidata describes entities, mostly by means of proper nouns, although lexical information is currently being added to wiki-data @xcite . still, there is a significant overlap in terms of the proper and common nouns in wordnet and providing links to wikidata would help to improve and extend the usage of wordnet. a linking between the proper nouns in word-net and wikipedia was constructed by @xcite and as a side part of this work we updated and contributed this list to wikidata including manually remapping 156 links that had become stale. however, we also see that for most common nouns it is still possible to match most of the senses to a concept in wikidata, for example of the eight senses of 'work' in wordnet, six of them can easily be mapped to a concept in wikidata and only two abstract definitions 'activity directed toward making or doing something' and 'applying the mind to learning and understanding a subject (especially by reading)' are not obviously available in wikidata. in fact, out of 122,147 noun lemmas in english wordnet 67,569 (55.3%) are represented by an entry in wikidata and as such we believe that the majority of noun senses in wordnet should have a counterpart in wikidata. given the size of the task of this linking, it is obvious that we should have some automatic help to improve the linking process; however, neither resource would accept fully automatic linking as has been applied in other resources such as babelnet @xcite and uby @xcite . as such, in this paper we start the process of using automatic tools to construct the links between the datasets and manually validating. for the purpose of this paper, our first focus is on what we refer to as hapax links, that is links for which there is only a single sense for the lemma in wordnet and for which only one page in wikidata has this lemma as the english title. we then consider how we could extend this further to the links where there is ambiguity in the lemma. finally, we consider how this linking could be used to contribute back to english wordnet and extend the existing categories there. ## related work most of the focus to date has been on the development of automatic linking between word-net and encyclopedic knowledge graphs based on wikipedia such as dbpedia @xcite or wikidata. one of the most prominent examples of this is babelnet @xcite , which mapped wordnet to wikipedia using a word sense disambiguation algorithm, in which the surrounding elements in the synset graph and article text were used as context for disambiguation. in their work @xcite , the authors report an f-measure of 82.7% in their linking, and while this is strong it cannot be considered to be a gold standard. another approach has been through the use of personalised pagerank (ppr) @xcite , which was first attempted by toral et al. @xcite and later improved by meyer and @xcite to create the uby resource @xcite . a similar resource, yago @xcite , has also been constructed by means of automatic linking and while they report very high accuracy (97.7%) this referred to only a limited number of concepts that are linked. there have also been attempts to link wordnet to other resources including the semlink @xcite that have provided links to other lexical resources and ontologies. in contrast to these works, this work is developing a manual linking that aims to be usable as a gold standard. ## towards a complete linking the hapax linking above, while it has a very high accuracy is also not sufficient in order to create a complete linking between two resources, as such we have attempted to evaluate how easily this can be extended to a complete linking of the two re- q7366 song q7889 video game q11424 film q101352 family name q134556 single q207628 musical composition q215380 musical group q222910 compilation album q386724 work q482994 album q3305213 painting q5398426 television series q5741069 rock band ## conclusion in this work we have analysed the task of linking the noun hierarchy of wordnet with wikidata. we found that the approach relying on hapax linking can be achieved with very high accuracy, although this does still produce occasional errors. however, for ambiguous senses the task of linking is still much harder and the automatic methods need to be further refined to produce high quality results. as a result of this we have increased the amount of links between wikidata and wordnet to nearly 10,000 and have made them available in wikidata and english wordnet foot_3 . we hope that this can be a seed to further the integration of the two projects and close the gap between the lexical and encyclopedic information in the two resources.