Comparing of Term Clustering Frameworks for Modular Ontology Learning

Ziwei Xu (1, 2, 3) , Mounira Harzallah (4) , Fabrice Guillet (4)

1 EPUN - Ecole Polytechnique de l'Université de Nantes
2 LS2N - équipe DUKe - Data User Knowledge
3 LS2N - Laboratoire des Sciences du Numérique de Nantes
4 LINA - Laboratoire d'Informatique de Nantes Atlantique

This paper aims to use term clustering to build a modular ontology according to core ontology from domain-specific text. The acquisition of semantic knowledge focuses on noun phrase appearing with the same syntactic roles in relation to a verb or its preposition combination in a sentence. The construction of this co-occurrence matrix from context helps to build feature space of noun phrases, which is then transformed to several encoding representations including feature selection and dimensionality reduction. In addition, the content has also been presented with the construction of word vectors. These representations are clustered respectively with K-Means and Affinity Propagation (AP) methods, which differentiate into the term clustering frameworks. Due to the randomness of K-Means, iteration efforts are adopted to find the optimal parameter. The frameworks are evaluated extensively where AP shows dominant effectiveness for co-occurred terms and NMF encoding technique is salient by its promising facilities in feature compression.

Mots clés: Text Mining; Ontology Learning; Term Clustering; Feature Extraction

Domaines: Base de données [cs.DB]; Traitement du texte et du document; Intelligence artificielle [cs.AI]

Ziwei Xu, Mounira Harzallah, Fabrice Guillet. Comparing of Term Clustering Frameworks for Modular Ontology Learning. 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Sep 2018, Seville, Spain. pp.128-135, ⟨10.5220/0006960401280135⟩. ⟨hal-01982912⟩