Automatic identification of treatment relations for medical ontology learning : an exploratory study

• Main Authors: Khoo, Christopher S. G., Lee, Chew-Hung, Na, Jin-Cheon
• Other Authors: Wee Kim Wee School of Communication and Information
• Format: Conference or Workshop Item
• Language: English
• Published: 2014
• Subjects: Communication and Information
• Online Access: https://hdl.handle.net/10356/101249; http://hdl.handle.net/10220/20046; http://www.ergon-verlag.de/bibliotheks--informationswissenschaft/advances-in-knowledge-organization/band-9.php
• Institution: Nanyang Technological University

This study is part of a project to develop an automatic method to build ontologies, especially in a medical domain, from a document collection. An earlier study had investigated an approach to inferring semantic relations between medical concepts using the UMLS (Unified Medical Language System) semantic net. The study found that semantic relations between concepts could be inferred 68% of the time, although the method often could not distinguish between a few possible relation types. Our current research focuses on the use of natural language processing techniques to improve the identification of semantic relations. In particular, we explore both a semi-automatic and manual construction of linguistic patterns for identifying treatment relations in medical abstracts in the domain of colon cancer treatment. Association rule mining was applied to sample sentences containing both a disease concept and a reference to drug, to identify frequently occurring word patterns to see if these patterns could be used to identify treatment relations in sentences. This did not yield many useful patterns, suggesting that statistical association measures have to be complemented with syntactic and semantic constraints to identify useful patterns. In the second part of the study, linguistic patterns were manually constructed based on the same sentences. This yielded promising results. Work is ongoing to improve the manually constructed patterns as well as to identify the syntactic and semantic constraints that can be used to improve the automatic construction of linguistic patterns.