Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
This addresses the need for automated ontology learning in domains with large, noisy document collections, though it is incremental as it builds on existing topic modeling techniques.
The paper tackles the problem of extracting terminological ontologies from heterogeneous documents by proposing hrLDA, a hierarchical topic model that uses noun phrases and considers syntax, and shows it outperforms existing models in building hierarchies and is robust to noisy data, with ontologies competitive to expert-created ones.
In this paper, we present hierarchical relationbased 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.