Automated learning of domain taxonomies from text using background knowledge

Julia Hoxha, Guoqian Jiang, Chunhua Weng

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

In this paper, we present an automated method for taxonomy learning, focusing on concept formation and hierarchical relation learning. To infer such relations, we partition the extracted concepts and group them into closely-related clusters using Hierarchical Agglomerative Clustering, informed by syntactic matching and semantic relatedness functions. We introduce a novel, unsupervised method for cluster detection based on automated dendrogram pruning, which is dynamic to each partition. We evaluate our approach with two different types of textual corpora, clinical trials descriptions and MEDLINE publication abstracts. The results of several experiments indicate that our method is superior to existing dynamic pruning and the state-of-art taxonomy learning methods. It yields higher concept coverage (95.75%) and higher accuracy of learned taxonomic relations (up to 0.71 average precision and 0.96 average recall).

Original languageEnglish (US)
Pages (from-to)295-306
Number of pages12
JournalJournal of Biomedical Informatics
Volume63
DOIs
StatePublished - Oct 1 2016

Keywords

  • Concept discovery
  • Ontology learning
  • Semantic relation acquisition
  • Taxonomy extraction from text
  • Term recognition

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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