Unsupervised terminological ontology learning based on hierarchical topic modeling

Xiaofeng Zhu, Diego Klabjan, Patrick N. Bless

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present hierarchical relation-based latent Dirichlet allocation (hrLDA), a data-driven hierarchical topic model for extracting terminological ontologies from a large number of heterogeneous documents. In contrast to traditional topic models, hrLDA relies on noun phrases instead of unigrams, considers syntax and document structures, and enriches topic hierarchies with topic relations. Through a series of experiments, we demonstrate the superiority of hrLDA over existing topic models, especially for building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the settings of noisy data sets, which are likely to occur in many practical scenarios. Our ontology evaluation results show that ontologies extracted from hrLDA are very competitive with the ontologies created by domain experts.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
EditorsLatifur Khan, Balaji Palanisamy, Chengcui Zhang, Sahra Sedigh Sarvestani
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages32-41
Number of pages10
ISBN (Electronic)9781538615621
DOIs
StatePublished - Nov 8 2017
Event18th IEEE International Conference on Information Reuse and Integration, IRI 2017 - San Diego, United States
Duration: Aug 4 2017Aug 6 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
Volume2017-January

Other

Other18th IEEE International Conference on Information Reuse and Integration, IRI 2017
CountryUnited States
CitySan Diego
Period8/4/178/6/17

Keywords

  • Hierarchical topic modeling
  • Knowledge acquisition
  • Ontology learning
  • Terminological ontology

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems

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  • Cite this

    Zhu, X., Klabjan, D., & Bless, P. N. (2017). Unsupervised terminological ontology learning based on hierarchical topic modeling. In L. Khan, B. Palanisamy, C. Zhang, & S. S. Sarvestani (Eds.), Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017 (pp. 32-41). (Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRI.2017.18