Towards Mapping Thesauri onto pl W ord N et (2018)

Abstract: plWordNet, the wordnet of Polish, has become a very comprehensive description of the Polish lexical system. This paper presents a plan of its semi-automated integration with thesauri, terminological databases and ontologies, as a further necessary step in its development. This will improve linking of plWordNet into Linked Open Data, and facilitate applications in, e.g., WSD, keyword extraction or automated metadata generation. We present an overview of resources relevant to Polish and a plan for their linking to plWordNet.

## introduction after more than 12 years of continuous development plwordnet -the wordnet of polishwith the version 3.0 emo @xcite has become a very comprehensive description of the polish lexical system including: 197,721 synsets, 179,125 lemmas and 260,214 lexical units (henceforth lus 1 ) described by about 650,000 relation links. it provides also a very good coverage of large corpora of polish, cf @xcite . this is much more than it could have been expected at the beginning, especially if we take into account that plwordnet has been constructed from scratch on the basis of the corpusbased wordnet development method @xcite . moreover, plwordnet has been also manually mapped onto princeton wordnet on the synset level to a very large extent (>200k mapping relation instances) and onto wikipedia on the 1 a lexical unit is defined her technically as a triple: part of speech, lemma, sense id. the question is whether it is the final stage of the development of a wordnet of polish, or more generally, an example of the final stage of a wordnet in general? the immediate answer is no. a complete wordnet is a moving target that evolves along two dimensions: increasing understanding of the effective use of a wordnet as a tool in describing the lexical system of the natural language, and growing expectations of the wordnet applications developers. in this paper we are going to focus on the latter. wordnets have to compete with statistical models that are relatively easy to extract from very large corpora. however a wordnet is (or must be) a trustworthy language resource of high quality, providing description of the lexical meanings and the lexical system. its advantage is in description of infrequent lemmas and lus that is beyond the scope of distributional semantics methods (including word embeddings). next, an appropriate, high quality means of linking a wordnet with knowledge resources must be provided to facilitate its applications in wsd, keyword and semantic meta-data extraction from text, semantic text classification etc. our goal is to design a linking mechanism between plwordnet and a rich cloud of heterogeneous terminological and ontological resources, as well as linked open data (lod), and next to develop an efficient method for building this mechanism in a semi-automated way. in this paper, we focus on linking with terminological resources as a natural extension to the wordnet. ## lexical resources vs. plwordnet polish vocabulary outside plwordnet could be found in many electronic lexical resources. we describe them below in three groups: (1) subject headings systems, (2) controlled vocabulary thesauri (of the eu, un and us), and (3) wikipedia. ## linking potential all these lexical resources are interlinked, composing a quite complex resource net. we want to find a path through it in order to establish mappings between them and plwordnet. we will exercise two main formats: skos and marc 21. ## perspectives the presented overview and mapping method show a great potential in building a very large network of resources around plwordnet. the network can be even more expanded with lod utilising the existing high quality manual mapping of plwordnet onto wordnet. the primary application will be improvement of a wordnet-based wsd that works better with larger and denser network. next, it will be a basis for a method of the automated assignment of descriptive keywords to texts and will support extraction of keywords from texts. both methods will be first used in automated semantic indexing of digital research repositories, and next in different applications in digital humanities and social sciences. for such applications possibility of finding associations between texts and specialist terms is crucial and can be done via the created complex network.