This paper provides insight into how the core elements of the Ontolex-Lemon model are inte- grated in the Wikibase Ontology, the data model fundamental to any instance of the Wikibase software, including Wikidata lexemes, which today is probably the largest Ontolex-Lemon use case, a dataset collaboratively built by the community of Wikidata users. We describe how lexical entries are modeled on a Wikibase, including the linguistic description of lexemes, the linking of lexical entries, lexical senses and lexical forms across resources, and links across the domain of lexemes and the ontological part of a Wikibase knowledge graph. Our aim is to present Wikibase as a solution for storing and collaboratively editing lexical data follow- ing Semantic Web standards, and to identify relevant research questions to be addressed in future work.

We propose a method for automatic term extraction based on a statistical measure that ranks term candidates according to their semantic relevance to a specialised domain. As a measure of relevance we use term co-occurrence, defined as the repeated instantiation of two terms in the same sentences, in indifferent order and at variable distances. In this way, term candidates are ranked higher if they show a tendency to co-occur with a selected group of other units, as opposed to those showing more uniform distributions. No external resources are needed for the application of the method, but performance improves when provided with a pre-existing term list. We present results of the application of this method to a Spanish-English Linguistics corpus, and the evaluation compares favourably with a standard method based on reference corpora.

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.