Towards a Term Clustering Framework for Modular Ontology Learning

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

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

This paper aims to analyze and adopt the term clustering method for building a modular ontology according to its core ontology. 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, word embedding techniques are also presented as feature representation. These representations are clustered respectively with K-Means, K-Medoids, Affinity Propagation, DBscan and co-clustering algorithms. The feature representation and clustering methods constitute the major sections of term clustering frameworks. Due to the randomness of clustering approaches, iteration efforts are adopted to find the optimal parameter and provide convinced value for evaluation. The DBscan and affinity propagation show their outstanding effectiveness for term clustering and NMF encoding technique and word embedding representation are salient by its promising facilities in feature compression.

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

Domaines: Traitement du texte et du document; Intelligence artificielle [cs.AI]; Informatique et langage [cs.CL]; Recherche d'information [cs.IR]; Apprentissage [cs.LG]

Ziwei Xu, Mounira Harzallah, Fabrice Guillet, Ryutaro Ichise. Towards a Term Clustering Framework for Modular Ontology Learning. Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp.178-201, 2020, ⟨10.1007/978-3-030-49559-6_9⟩. ⟨hal-03063773⟩