Weisen Guo, Steven B. Kraines



Over one million papers are published annually in life sciences. Bioinformatics and knowledge discovery fields aim to help researchers conduct scientific discovery using the existing published knowledge. Existing literature-based discovery methods and tools mainly use text-mining techniques to extract non-specified relationships between two concepts. We present an approach that uses semantic web techniques to measure the relevance between two relationships with specified types that involve a particular entity. We consider two highly relevant relationships as a relationship association. Relationship associations could help researchers generate scientific hypotheses or create computer-interpretable semantic descriptors for their papers. The relationship association extraction process is described and the results of experiments for extracting relationship associations from 392 semantic graphs representing MEDLINE papers are presented


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Paper Citation

in Harvard Style

Guo W. and Kraines S. (2009). DISCOVERING RELATIONSHIP ASSOCIATIONS IN LIFE SCIENCES USING ONTOLOGY AND INFERENCE . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 10-17. DOI: 10.5220/0002285300100017

in Bibtex Style

author={Weisen Guo and Steven B. Kraines},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
SN - 978-989-674-011-5
AU - Guo W.
AU - Kraines S.
PY - 2009
SP - 10
EP - 17
DO - 10.5220/0002285300100017