Title: Enhancing biomedical concept extraction using semantic relationship weights

Authors: Said Bleik; Wei Xiong; Min Song

Addresses: Department of Information Systems, New Jersey Institute of Technology, Newark, NJ 07102, USA ' Department of Information Systems, New Jersey Institute of Technology, Newark, NJ 07102, USA ' Department of Library and Information Science, Yonsei University, 134 Sinchon-dong, Seoul, Korea

Abstract: Scientific publications are often associated with a set of keywords to describe their content. Automating the process of keyword extraction and assignment could be useful in indexing electronic documents and building digital libraries. In this paper we propose a new approach to biomedical Concept Extraction (CE) using semantic features of concept graphs. We represent full-text documents by graphs and map biomedical terms to predefined ontology concepts. We adopt concept relation weights to improve the ranking process of potential key concepts. We perform both objective and human-based subjective evaluations. The results show that using relation weights significantly improves the performance of CE. The results also highlight the subjectivity of the CE procedure as well as of its evaluation.

Keywords: concept extraction; concept graphs; graph features; semantic relations; keyword extraction; biomedical concepts; SVM ranking; support vector machines; keyword assignment; document indexing; electronic documents; digital libraries; semantic features; biomedical terms; ontology concepts; scientific publications; concept relation weights; subjectivity.

DOI: 10.1504/IJDMB.2013.053307

International Journal of Data Mining and Bioinformatics, 2013 Vol.7 No.3, pp.303 - 321

Received: 12 May 2011
Accepted: 13 May 2011
Published online: 07 Jun 2013 *

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