Biomedical concept extraction using concept graphs and ontology-based mapping

Said Bleik; Wei Xiong; Yiran Wang; Min Song

doi:10.1109/BIBM.2010.5706627

Biomedical concept extraction using concept graphs and ontology-based mapping

Said Bleik, Wei Xiong, Yiran Wang, Min Song

Department of Library and Information Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Assigning keywords to articles can be extremely costly. In this paper we propose a new approach to biomedical concept extraction using semantic features of concept graphs to help in automatic labeling of scientific publications. The proposed system extracts key concepts similar to author-provided keywords. We represent full-text documents by graphs and map biomedical terms to predefined ontology concepts. In addition to occurrence frequency weights, we use concept relation weights to rank potential key concepts. We compare our technique to that of KEA's, a state-of-the-art keyphrase extraction software. The results show that using the relations weight significantly improves the performance of concept extraction. The results also highlight the subjectivity of the concept extraction procedure as well as of its evaluation.

Original language	English
Title of host publication	Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Pages	553-556
Number of pages	4
DOIs	https://doi.org/10.1109/BIBM.2010.5706627
Publication status	Published - 2010
Event	2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 - Hong Kong, China Duration: 2010 Dec 18 → 2010 Dec 21

Publication series

Name	Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

Other

Other	2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Country/Territory	China
City	Hong Kong
Period	10/12/18 → 10/12/21

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics

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10.1109/BIBM.2010.5706627

Cite this

Bleik, S., Xiong, W., Wang, Y., & Song, M. (2010). Biomedical concept extraction using concept graphs and ontology-based mapping. In Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 (pp. 553-556). [5706627] (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010). https://doi.org/10.1109/BIBM.2010.5706627

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title = "Biomedical concept extraction using concept graphs and ontology-based mapping",

abstract = "Assigning keywords to articles can be extremely costly. In this paper we propose a new approach to biomedical concept extraction using semantic features of concept graphs to help in automatic labeling of scientific publications. The proposed system extracts key concepts similar to author-provided keywords. We represent full-text documents by graphs and map biomedical terms to predefined ontology concepts. In addition to occurrence frequency weights, we use concept relation weights to rank potential key concepts. We compare our technique to that of KEA's, a state-of-the-art keyphrase extraction software. The results show that using the relations weight significantly improves the performance of concept extraction. The results also highlight the subjectivity of the concept extraction procedure as well as of its evaluation.",

author = "Said Bleik and Wei Xiong and Yiran Wang and Min Song",

year = "2010",

doi = "10.1109/BIBM.2010.5706627",

language = "English",

isbn = "9781424483075",

series = "Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010",

pages = "553--556",

booktitle = "Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010",

note = "2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 ; Conference date: 18-12-2010 Through 21-12-2010",

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Bleik, S, Xiong, W, Wang, Y & Song, M 2010, Biomedical concept extraction using concept graphs and ontology-based mapping. in Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010., 5706627, Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, pp. 553-556, 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, Hong Kong, China, 10/12/18. https://doi.org/10.1109/BIBM.2010.5706627

Biomedical concept extraction using concept graphs and ontology-based mapping. / Bleik, Said; Xiong, Wei; Wang, Yiran et al.
Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010. 2010. p. 553-556 5706627 (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Biomedical concept extraction using concept graphs and ontology-based mapping

AU - Bleik, Said

AU - Xiong, Wei

AU - Wang, Yiran

AU - Song, Min

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N2 - Assigning keywords to articles can be extremely costly. In this paper we propose a new approach to biomedical concept extraction using semantic features of concept graphs to help in automatic labeling of scientific publications. The proposed system extracts key concepts similar to author-provided keywords. We represent full-text documents by graphs and map biomedical terms to predefined ontology concepts. In addition to occurrence frequency weights, we use concept relation weights to rank potential key concepts. We compare our technique to that of KEA's, a state-of-the-art keyphrase extraction software. The results show that using the relations weight significantly improves the performance of concept extraction. The results also highlight the subjectivity of the concept extraction procedure as well as of its evaluation.

AB - Assigning keywords to articles can be extremely costly. In this paper we propose a new approach to biomedical concept extraction using semantic features of concept graphs to help in automatic labeling of scientific publications. The proposed system extracts key concepts similar to author-provided keywords. We represent full-text documents by graphs and map biomedical terms to predefined ontology concepts. In addition to occurrence frequency weights, we use concept relation weights to rank potential key concepts. We compare our technique to that of KEA's, a state-of-the-art keyphrase extraction software. The results show that using the relations weight significantly improves the performance of concept extraction. The results also highlight the subjectivity of the concept extraction procedure as well as of its evaluation.

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ER -

Biomedical concept extraction using concept graphs and ontology-based mapping

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