Mapping W ord N et Instances to W ikipedia (2018)

Abstract: Lexical resource differ from encyclopaedic resources and represent two distinct types of resource covering general language and named entities respectively. However, many lexical resources, including Princeton WordNet, contain many proper nouns, referring to named entities in the world yet it is not possible or desirable for a lexical resource to cover all named entities that may reasonably occur in a text. In this paper, we propose that instead of including synsets for instance concepts PWN should instead provide links to Wikipedia articles describing the concept. In order to enable this we have created a gold-quality mapping between all of the 7,742 instances in PWN and Wikipedia (where such a mapping is possible). As such, this resource aims to provide a gold standard for link discovery, while also allowing PWN to distinguish itself from other resources such as DBpedia or BabelNet. Moreover, this linking connects PWN to the Linguistic Linked Open Data cloud, thus creating a richer, more usable resource for natural language processing.

## introduction princeton wordnet @xcite and wikipedia, especially in machine readable form such as dbpedia @xcite , are two of the most widely used resources in natural language processing. the nature of these resources is distinct, with wordnet constituting a lexicon of words in the english language and wikipedia being an encyclopedia describing entities in the world. this means that wordnet should contain all the common nouns, verbs, adjectives and adverbs and wikipedia should contain the proper nouns referring to notable entities in a text. however, in fact there is a significant overlap between these two resources as wikipedia contains pages for abstract general concepts, such as "play" foot_0 , while pwn contains many proper nouns for concepts such as paris, for which pwn has four synsets for the city in france (i83645), the city in texas (i84698), the mythological prince (i86545) and a plant (i102495). in the case of wordnet, the choice of which proper nouns to include has had certain biases, for example there are many synsets for cities in the united states, e.g, paterson, new jersey (i84527), but not for kawasaki, a city in japan that is ten times larger. if however, pwn were to expand to include more proper nouns, it would lead to a much larger resource that would overlap significantly in its coverage with dbpedia. in fact, there have been several attempts to automatically create such a resource, most notably babelnet @xcite and uby @xcite , however these resources have to rely on automatic alignment of the concepts. instead, we propose that the concepts for named entities can be mapped to wikipedia and that these concepts can thus be removed or replaced with links in future versions of pwn. since pwn is created by careful manual effort, it is clear that an automatic mapping would not be compatible with the nature of pwn. instead, as a principal contribution of this paper, we present the first manually created mapping between pwn instances and wikipedia articles. this could be further used to link pwn to other resources including wikidata and geonames as well as help in the automatic translation of parts of wordnet. in this paper, we first define the scope of the problem, in particular in terms of the number of instances and proper nouns that exist in pwn and their distribution. we then review some existing work on mapping pwn and wikipedia instances. we present our method of linking, that uses wikipedia categories to propose an alignment between sets of concepts simultaneously and the tool we created based on this that allows our annotators to quickly map the concepts between one resource and another. finally, we present the results of our annotation, in particular in terms of the total effort and work required to create this mapping and conclude with some discussion and analysis of the results. ## on proper nouns in wordnet princeton wordnet is a lexicon, that consists of a graph of synsets, which are collections of words that are synonymous, linked by a number of properties. all words in a synset have the same partof-speech, however unfortunately there is only a single category for nouns and in fact synsets may contain a mixture of proper and common nouns, e.g., caterpillar,cat (i51642). the links in the graph are of different types and the link instance hypernym links a synset to a concept that is an instance of @xcite , giving a limited set of proper nouns that we can systematically identify. there are in total 7,742 synsets in pwn which are instance hypernyms of 946 synsets and these will be the main focus of our work. of these nearly all contain words starting with a capital letter, and of the 16 that don't, can be explained as follows: 7 are not capitalized for orthographic reasons, e.g., al-muhajiroun, 6 should be capitalized but are not in wordnet, e.g., pampas, 2 should not be instance hypernyms but instead normal hypernyms isle,islet (i85598) and sierra (i86184) and 1 church mouse (i48540) is likely erroneous. as such, we can say that the set of synsets that are marked as instance hypernyms of a concept are all named entities in the world. however, there are many other synsets that contain one or more capitalized word as an entry and it is clear that we are not capturing all the proper nouns in pwn. in particular, there are a large number of capitalized words that refer to names of species or other terms in the linnaean taxonomy, e.g., felis catus or genus hydrangea and these are not instances of another synset and often share a synset with common nouns, e.g., domestic cat,house cat,felis domestics,felis catus (i46594). in addition, there are several other large categories of proper nouns that are not captured by this approach especially beliefs, e.g., buddhism (i79765) and languages, e.g., german,high german,german language (i73125). however, simply using the capitalization to detect proper nouns produces a lot of false positives, including acronyms and terms including a proper noun such as scotch terrier, scottish terrier, scottie (i46443). as such, for this work we have focussed only on the synsets which are instances of synsets, as these are the terms that seem to be most encyclopedic in their content. a breakdown of the major synsets is given in figure 1 , and as we can see the major categories are (i35562), which is named people, (i35580), which is named places. a few other categories that have large number of entities include rivers and other geological features ((i85104),( i85439 ) and ( i85674 )), gods (i86570), events, especially wars (i35586), social groups, such as terrorist organizations (i79103) and books (i69848). ## related work the goal of mapping wordnet to wikipedia has been recognized as an important one, however most of the focus has so far been on the automatic creation of mappings between the two resources, and this has led to the creation of widecoverage lexicons that are useful for nlp applications but cannot act as a gold standard for nlp in the same way that wordnet does. the most notable such resources is babelnet @xcite , whose mapping of wordnet to wikipedia is based on the use of a word-sense disambiguation algorithm, where contexts are created for the wikipedia and wordnet entities by means of using the surrounding synsets and the article texts. a second step then selects the highest scoring mapping based on structuring the wikipedia page content using wordnet relations. the authors report a maximum f-measure of 82.7% with a precision of 81.2%, showing that while babelnet is a high-quality resource, it cannot be considered a gold standard. this method improved on a previous approach by these authors @xcite , which used the taxonomic structure of the resources. another method to link word-net and wikipedia has been through personalized page rank (agirre and soroa, 2009), which was first attempted as a method for linking these resources in @xcite and then was further improved by @xcite , by the introduction of "thresholds". niemann and gurevych's methodology forms the basis of the uby resource @xcite . @xcite proposed using semantic textual similarity methods and showed results that obtained an f-measure of 84.1% outperforming ponzetto and navigli's approach. notably, this work also created a gold standard of wikipedia-wordnet mappings that can be used for evaluation of further approaches to linking. however, this mapping is only of 200 words and as such is not on the same scale as the resource introduced in this paper. another large-scale resource that has been constructed by combining wordnet and wikipedia is yago @xcite , which created an ontology of concepts created from wikipedia categories. this showed a very high accuracy in the mapping of concepts (97.7%), however this does not deal with the actual entities as in this work. wordnet has also been linked to a number of other lexical resource by a variety approaches, including semcor @xcite , where texts were annotated with word-net synset identifiers and this was used as a basis to create links to other resources including framenet @xcite and verb-net @xcite , which were linked in @xcite . another linking was created by the semlink @xcite , also based on the annotation of a corpus with pwn, fn and vn. finally, mappings have also been proposed between wordnet and wiktionary foot_1 , a free dictionary from the wikimedia foundation, in works such as @xcite and @xcite . ## mapping wordnet to wikipedia our goal is to create a large manual mapping between a subset of princeton wordnet and wikipedia, however simply identifying this subset and starting annotation is not a suitable approach as looking up each wordnet synset in wikipedia and recording the results would be a slow and dull process. we could try to improve this by matching the lemmas of wordnet entries to the titles of wikipedia articles, but this would have a very low coverage as the article title for a wikipedia article must be unique so often includes specific disambiguating terms. to expand the coverage of this we consider a wordnet lemma to match a wikipedia article if it matches the title ignoring case before the first comma or parentheses or any page that redirects to this article. thus, we would match the lemma "paris" to the page titles "paris", "paris, texas" and "paris (mythology)". in addition, we also included information from disambiguation pages, as collected by dbpedia @xcite 3 . this method captures most of the mappings as only 77 wordnet synsets have no candidates in wikipedia, however it also creates significant ambiguity with an average of 21.6 candidates for each synset. for these reasons, we try to resolve these differences by suggesting category mappings, inspired by @xcite . ## resource and evaluation we used the above described methodology to annotate the vast majority of the mappings (7,582 mappings), while the remaining 239 synsets had no good candidates in wikipedia, principally due to spelling variants and this includes the 77 synsers with no candidates and other synsets for which the category approach did not work. these remaining 239 synsets were then mapped directly (on a spreadsheet). we also used this pass to sort the links into the following types: exact the wordnet synset and wikipedia article exactly describe the same entity. broad the wikipedia article describes several things, of which the entity described by the wordnet synset is only one of. an example of this is the wikipedia article for the "wright brothers" 5 , which is linked broader to two wordnet synsets for each brother. in this case, wikipedia redirects "orville wright" and "wilbur wright" to this article. narrow the opposite of 'broad', i.e., the word-net synset describes multiple wikipedia articles. an example is rameses, ramesses, ramses (i96663) defined as "any of 12 kings of ancient egypt between 1315 and 1090 bc" 6 , while each is a separate wikipedia article. related the wikipedia article does not describe the wordnet synset but something intrinsically linked to it, and the lemmas of the wordnet synset have redirects to this article. for example hoover, william hoover, william henry hoover (i95579) is mapped to "the hoover company" describing the company he founded. wikipedia also redirect "william hoover" to this article. unmapped a small number of entities in word-net were not possible to map to wikipedia, either because the synset was not in wikipedia (this was the case for many terrorist organizations), the description and name did not match anything in wikipedia (for a exact broad narrow related unmapped 7,582 54 21 30 59 we used the following heuristic to help with this mapping. if the wikipedia page title exactly matched one of the lemmas or the wikipedia article was of the form "x, y" or "x (y)" and x was one of the lemmas and y occurred in the definition of the synset, we accepted it as an exact match foot_2 . for example, this allowed us to easily validate the mappings for the wikipedia articles "paris" (the capital of france), "paris, texas" and "paris (mythology)". all other mappings (1,733) were manually assigned one of the above cate-gories. as a result of this mapping process we also detected 56 errors (0.7%) and improved 11 mappings, by which we mean that we changed a broader/narrower link to an exact link. for example, the synset downing street (i83390), was moved from "10 downing street" to "downing street". the complete size of each of these categories is given in ## conclusion we have presented a new mapping of all the instances in wordnet to wikipedia articles. this represents the largest gold standard mapping for tasks such as link discovery @xcite and is likely to be a basic resource for many tasks in natural language processing. for the future development of princeton wordnet as a resource, this mapping can form the basis by which pwn can distinguish itself from an encyclopedia, by replacing the instance links with direct links to wikipedia. moreover, by linking to wikipedia articles, we can further link to many other resources, for example it is only a matter of changing the url to find a dbpedia entity that can be used to find machine readable information about the data. furthermore, all wikipedia articles are now linked to wikidata entities, so we can easily find that paris, city of light, french capital, capital of france (i83645) is linked to wiki-data entity q90 foot_7 and then this can give us identities in many other databases including geonames (2968815), openstreetmap (71525) and even the official twitter account (@paris). finally, it is worth noting that wikipedia and wikidata also contains links to these concepts in other languages, and as such, this linking can create a partial translation of a section of wordnet. as such, this transforms wordnet into a richer linked resource that can be part of the web of linguistic linked open data @xcite .