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Title
TAKES: Two-step Approach for Knowledge Extraction in Biomedical Digital Libraries
Author(s)
Min Song
Publication Year
2014-03-30
Abstract
This paper proposes a novel knowledge extraction system, TAKES (Two-step Approach for Knowledge Extraction System), which integrates advanced techniques from Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing (NLP). In particular, TAKES adopts a novel keyphrase extraction-based query expansion technique to collect promising documents. It also uses a Conditional Random Field-based machine learning technique to extract important biological entities and relations. TAKES is applied to biological knowledge extraction, particularly retrieving promising documents that contain Protein-Protein Interaction (PPI) and extracting PPI pairs. TAKES consists of two major components: DocSpotter, which is used to query and retrieve promising documents for extraction, and a Conditional Random Field (CRF)-based entity extraction component known as FCRF. The present paper investigated research problems addressing the issues with a knowledge extraction system and conducted a series of experiments to test our hypotheses. The findings from the experiments are as follows: First, the author verified, using three different test collections to measure the performance of our query expansion technique, that DocSpotter is robust and highly accurate when compared to Okapi BM25 and SLIPPER. Second, the author verified that our relation extraction algorithm, FCRF, is highly accurate in terms of F-Measure compared to four other competitive extraction algorithms: Support Vector Machine, Maximum Entropy, Single POS HMM, and Rapier.
Keyword
Semantic Query Expansion; Information Extraction; Information Retrieval; Text Mining
Journal Title
Journal of Information Science Theory and Practice
Citation Volume
2
ISSN
2287-4577
DOI
10.1633/JISTaP.2014.2.1.1
Files in This Item:
Thumbnail E1JSCH_2014_v2n1_6.pdf270.22 kBDownload
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 2 - No. 1
Type
Article
URI
https://repository.kisti.re.kr/handle/10580/8646
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