Aligning the B ulgarian BTB W ord N et with the B ulgarian W ikipedia (2019)

Abstract: The paper reports on an ongoing work that manually maps the Bulgarian WordNet BTB-WN with Bulgarian Wikipedia. The preparatory work of extracting the Wikipedia articles and provisionally relating them to the WordNet lemmas was done automatically. The manual work includes checking of the corresponding senses in both resources as well as the missing ones. The main cases of mapping are considered. The first experiments of mapping about 1000 synsets show the establishment of more than 78 % of exact correspondences and nearly 15 % of new synsets.

## introduction there is still lack of sufficient knowledge for solving many important nlp tasks, such as word sense disambiguation (wsd), relation extraction, named entity linking, event detection, etc. up to now a number of attempts have been provided in the community that integrate various linguistic and semantic resources in smart ways. these are, among others, semlink @xcite , predicate matrix (de @xcite , uby @xcite , babelnet @xcite . semlink combines propbank (kingsbury and @xcite , verbnet @xcite , and framenet @xcite . predicate matrix extends semlink with a mapping from its lexical units to wordnet synsets @xcite . uby was created for two languages -english and german. it combines wordnet and germanet with wiktionary, wikipedia, framenet and verb-net for english, and wiktionary and wikipedia for german. babelnet also combines many multilingual resources including wordnet and wikipedia. all these examples demonstrate two facts: (1) a single knowledge resource is not sufficient for the most of the nlp tasks; and (2) the automatic integration of the various distinct resources is error prone. this is especially true for low-resource languages that totally miss such resources or their existing resources are rather small in size. here we report on an effort to integrate bulgarian wordnet (btb-wn) @xcite with the bulgarian wikipedia. we are considering mapping of two semantic objects -concepts (meaning expressed by common words) and instances of such concepts called named entities. the integration is meant to be performed manually in order to ensure high quality of the result. the integrated knowledge graph will include the current version of btb-wn extended with: a) new senses and new synonyms for the existing synsets -all extracted from the articles in the bulgarian wikipedia; b) a controlled number of named entities that are specific to bulgaria and c) increasing the number of terminological concepts in various domains. thus the integrated resource will combine general lexica with encyclopedic knowledge (terminology). the expected result would be twofold: a) the mutual enrichment and improvement of both resources and b) handling of wsd in a more effective way by integrating the encyclopedic knowledge from wikipedia and the lexical information from wordnet. the structure of the paper is as follows: in the next section related work is presented. section 3 outlines the approach to the mapping as well as the results. the last section concludes the paper. ## related work needless to say, one of the most notable resources that link wordnet and wikipedia is babel-net -an automatically created very large, widecoverage multilingual semantic network @xcite . babelnet encodes knowl-edge as a labeled directed graph. it is created by linking the largest multilingual web encyclopedia -wikipedia, to the most popular computational lexicon --wordnet. babelnet has been built in 3 steps. the first step was to automatically combine wordnet and wikipedia by mapping the wordnet senses to wikipedia articles. the second step was to collect the multilingual lexicalizations of the babelnet synsets by using human-generated translations. these translations were provided by wikipedia as well as by a machine translation system for translating the occurrences of the concepts within sense-tagged corpora. the third step was to establish relations between the babel synsets through collecting all the relations found in word-net together with all wikipedias in the languages of interest. the integration was performed by an automatic mapping and by filling the lexical gaps in resource-poor languages with the aid of machine translation. the result is an "encyclopedic dictionary" that provides concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations. in spite of having at disposal such a resource as babelnet, our motivation to invest efforts in mapping the wordnet to the wikipedia was as follows: a) adding more locally important content into the existing mappings and b) enriching the resource that was constructed automatically with validated data. the babelfy service is very good at detecting concepts and names (given the availability of relevant data per language), but it still has problems with disambiguation among local people or places with the same name, or between a concept and a name. for example, the verb литва (litva, "start to fly") is identified only as the country литва (litva, "lithuania") whose graphical form coincides with the verb; similar for the adjective русия (rusiya, "blond") and the name of the country русия (rusiya, "russia"). in @xcite osenova and simov mention the initial attempt for annotating of named entities (ne) in bulgarian treebank (bul-treebank) with uris from dbpedia. this process was done with the same goal, namely to extend btb-wn in two directions: (1) the number of senses for lemmas that are already in btb-wn; and (2) the instances of the concepts. however, the bultreebank appeared to contain only a small number of named entities. thus the extension was insufficient and it required the use of the wikipedia uris and dbpedia classes for the missing nes. the authors also report on the automatic extension of btb-wn with automatically derived bulgarian synsets on the basis of the english ones through the usage of the english wiktionary. after manual checking of around 11000 suggestions, btb-wn was enriched by around 5000 synsets. a synset in pwn is viewed as a representation of a lexicalised concept, while in plword-net it is a set of lexical units sharing constitutive lexico-semantic relations and features. the synset includes such lexical units that share a set of lexico-semantic relations, called constitutive relations (hyper/hyponymy, holo/meronymy, type/instance, etc.). in some cases the constitutive relations might be irrelevant, so constitutive features are also used -stylistic register, aspect, semantic classes of verbs and semantic classes of adjectives. glosses, examples and substitution tests are also applied in the plwordnet. the mapping strategy refers to the synset level and includes looking for pairs of plwordnet and pwn synsets that are close in meaning. the stages of the mapping are as follows: an analysis of the sense and relation structure of a source synset, the selection of candidates for a target synset, the choice of a target synset and an inter-lingual relation that links the source and target synsets. having in mind the complex schema of mapping between the two wordnets we doubt that such a mapping could be successfully established between the wordnet and the wikipedia even for the one and the same language. expectedly, when named entities are highly predominant in the mapping, we might envisage also a high number of exact mappings, but for common words this is not so straightforward. for that reason, we decided to perform the mapping manually. for the first step our goal was to extend btb-wn with new synsets, synonyms and mappings to bulgarian wikipedia. another approach that could be taken into account when aiming to extend the wordnet is its alignment with a framenet (if such a resource has been constructed for the language). a recent and rather innovative example of the development of a framenet based on a corpus of written dutch, and annotated with propbank predicates and roles is the project of @xcite . in this project the creation of the framenet also exploits already manually classified data about real world events which specify frame constraints on the described situations. this data is manually related to texts describing the events. in future work we will consider this approach to extend the coverage of btb-wn as well as to add new constraints on the combinations of the senses within texts. ## btb-wn to wikipedia mapping in this section we present the correspondences between the synsets within btb-wn and the pages from the bulgarian wikipedia. ## conclusion the paper presents our initial attempts in enriching btb-wn with mappings to the bulgarian wikipedia. the first annotation results are promising in showing that wordnet profits well from this mapping -especially in adding synonyms, new senses and new instances. the importance of such a resource is envisaged at least in the following directions: enhancing named entity linking, relation extraction and word sense disambiguation of high quality for tasks, involving bulgarian data. the mapping also provides access to the whole wikipedia articles which could contribute valuable information for the usage of the corresponding concepts and named entities. the main source of enriching btb-wn appeared to be the named entities and the domain terms. we also noticed that wikipedia is a valuable resource for including mwes -predominantly terminological units, but not only. since the named entities are too many, as mentioned above, we focused on local ones because they are important for processing bulgarian data, and alsothey can be viewed as a valuable localized supplementary contribution to babelnet. another issue is that wikipedia contains mainly nouns. thus, the mappings enriched the noun network and the instances of names. for the verbs, adjectives and adverbs other enriching sources should be considered. through the derivation relations in wordnet, however, we still could incorporate the presented in wikipedia deverbal and adjectival nouns. in future work we envisage to map btb-wn also to other semantic resources such as wikidata. we have started with wikipedia because it provides more human oriented information which facilitates the mapping. in addition, wikidata is heavily extracted from wikipedia and we hope this to allow for an easy mapping. in the long run, we envisage also incorporating more bulgarian concepts and named entities with the idea to construct a bulgarian knowledge graph aligned to linguistic knowledge -senses and grammatical features.