LOV-ES: Guiding the Ontology Selection to Structure Textual Data using Topic Modeling

Damien Graux , Anaïs Ollagnier (1, 2)

1 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis
2 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics

On-line availability of text corpora nowadays allow data practitioners to build complex knowledge combining various sources. One common shared challenge lays in the modelisation of intermediate knowledge structures able to gather at once the various topics present in the texts. Practically, practitioners often go through the creation of vocabularies. In order to help these domain experts, we design LOVES: a solution able to help them in this creative process, guiding them in the selection and the combination of already existing vocabularies available online. Technically, our solution relies on LDA to detect topics and on the LOV to then propose candidate vocabularies.

Mots clés: Topic Modeling; Textual source; LDA; LOV; Vocabulary Recommender

Domaines: Informatique [cs]

Damien Graux, Anaïs Ollagnier. LOV-ES: Guiding the Ontology Selection to Structure Textual Data using Topic Modeling. International Semantic Web Conference, Oct 2022, Hangzou, China. ⟨hal-03765857⟩