Chiang Mai Journal of Science

Constructing Biological Knowledge Base using Named Entities Recognition and Term Collocation

Paper Type	Contributed Paper
Title	Constructing Biological Knowledge Base using Named Entities Recognition and Term Collocation
Author	Supattanawaree Thipcharoen [a,b], Watshara Shoombuatong [c], Samerkae Somhom [b], Rattasit Sukhahuta [b], Jeerayut Chaijaruwanich*[a,b]
Email	jeerayut.c@cmu.ac.th
Abstract: Over the last few decades, the publishing of biological literature has dramatically increased due to technological developments. Thus, a crucial process is to extract information from this large number of writings by utilizing a biological named entity (NER) approach to automatically label corresponding biological terms.  It is desirable to propose an effective method to identify biological named entities and automatically establish the specific knowledge base from biological literature. Herein, we made efforts in investigating biological information extraction for establishing specific knowledge as follows: 1) proposing NER method based on the efficient conditional random fields (CRFs) model, called NER-CRF, for performing on the benchmarking data (JNLPBA2004). The proposed NER method provided a higher result with 90.42% recall, 97.74% precision, and 94.30% F-measure, compared with the existing method with 75.99% recall, 69.42% precision, and 72.55% F-measure; 2) applying the Poisson approach for constructing an interpretability biological knowledge network to give good understanding to the global properties collocation of biological terms from the literature. Our finding provided the collocations of biological terms from the literature providing some insights to the specific biological literature.
Start & End Page	661 - 671
Received Date	2014-10-06
Revised Date
Accepted Date	2015-03-16
Full Text	Download
Keyword	Biological information extraction, Biological Named Entity Recognition, Conditional Random Fields, Poisson Collocations
Volume	Vol.43 No.3 (APRIL 2016)
DOI
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