Original Research ARTICLE
Application of Public Knowledge Discovery Tool (PKDE4J) to Represent Biomedical Scientific Knowledge
- 1Yonsei University, South Korea
- 2Library and Information Science Department, Yonsei University, South Korea
In today’s era of information explosion, extracting entities and their relations in large-scale, unstructured collections of text to better represent knowledge has emerged as a daunting challenge in biomedical text mining. To respond to the demand to automatically extract scientific knowledge with higher precision, the public knowledge discovery tool PKDE4J (Song et al., 2015) was proposed as a flexible text-mining tool. In this study, we propose an extended version of PKDE4J to represent scientific knowledge for literature-based knowledge discovery. Specifically, we assess the performance of PKDE4J in terms of three extraction tasks: entity, relation, and event detection. We also suggest applications of PKDE4J along three lines: 1) knowledge search, 2) knowledge linking, and 3) knowledge inference. We first describe the updated features of PKDE4J and report on tests of its performance. With additional options in the processes of named entity extraction, verb expansion, and event detection, we expect that the enhanced PKDE4J can be utilized for literature-based knowledge discovery.
Keywords: text-mining, named entity recognition, Relation extraction, Event extraction, Representation of Scientific Knowledge
Received: 20 Sep 2017;
Accepted: 08 Feb 2018.
Edited by:Xianwen Wang, Dalian University of Technology (DUT), China
Reviewed by:Andreas Holzinger, Medical University of Graz, Austria
Sam Henry, Virginia Commonwealth University, United States
Copyright: © 2018 Song, Kim, Kang, Kim and Jeon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Min Song, Yonsei University, Seoul, South Korea, firstname.lastname@example.org