Abstract
Given a biomedical article a, identification of those articles with similar core contents (including research goals, backgrounds, and conclusions) as a is essential for the survey and cross-validation of the highly related biomedical evidence presented in a. We thus present a technique CCSE (Core Content Similarity Estimation) that retrieves these highly related articles by estimating and integrating three kinds of inter-article similarity: goal similarity, background similarity, and conclusion similarity. CCSE works on titles and abstracts of biomedical articles, which are publicly available. Experimental results show that CCSE performs better than PubMed (a popular biomedical search engine) and typical techniques in identifying those scholarly articles that are judged (by biomedical experts) to be the ones whose core contents focus on the same gene-disease associations. The contribution is essential for the retrieval, clustering, mining, and validation of the biomedical evidence in literature.
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Notes
- 1.
Google Scholar is available at https://scholar.google.com.
- 2.
PubMed is available at http://www.ncbi.nlm.nih.gov/pubmed.
- 3.
DisGeNET is available at http://www.disgenet.org/web/DisGeNET/menu/home.
- 4.
GAD is available at http://geneticassociationdb.nih.gov.
- 5.
CTD is available at http://ctdbase.org.
- 6.
PMC is available at http://www.ncbi.nlm.nih.gov/pmc.
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Acknowledgment
This research was supported by the Ministry of Science and Technology (grant ID: MOST 105-2221-E-320-004) and Tzu Chi University (grant IDs: TCRPP103020 and TCRPP104010), Taiwan.
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Liu, RL. (2017). Identification of Biomedical Articles with Highly Related Core Contents. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_21
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