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Towards a Term Clustering Framework for Modular Ontology Learning

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Book cover Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1222))

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Abstract

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.

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Author information

Authors and Affiliations

  1. LS2N, Ecole Polytechnique de l’Université de Nantes, 44300, Nantes, France

    Ziwei Xu, Mounira Harzallah & Fabrice Guillet

  2. National Institute of Informatics, Tokyo, 101-8430, Japan

    Ryutaro Ichise

Authors
  1. Ziwei Xu
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  2. Mounira Harzallah
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  3. Fabrice Guillet
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  4. Ryutaro Ichise
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Corresponding authors

Correspondence to Ziwei Xu , Mounira Harzallah , Fabrice Guillet or Ryutaro Ichise .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  2. Federal University of Pernambuco, Recife, Brazil

    Ana Salgado

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. Delft University of Technology, Delft, The Netherlands

    Jan Dietz

  5. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Polytechnic Institute of Setúbal/INSTIC, Setúbal, Portugal

    Joaquim Filipe

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Xu, Z., Harzallah, M., Guillet, F., Ichise, R. (2020). Towards a Term Clustering Framework for Modular Ontology Learning. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2018. Communications in Computer and Information Science, vol 1222. Springer, Cham. https://doi.org/10.1007/978-3-030-49559-6_9

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  • DOI: https://doi.org/10.1007/978-3-030-49559-6_9

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