PKDE4J: Entity and relation extraction for public knowledge discovery

Min Song, Won Chul Kim, Dahee Lee, Go Eun Heo, Keun Young Kang

Research output: Contribution to journalArticlepeer-review

89 Citations (Scopus)


Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction.

Original languageEnglish
Pages (from-to)320-332
Number of pages13
JournalJournal of Biomedical Informatics
Publication statusPublished - 2015 Oct 1

Bibliographical note

Publisher Copyright:
© 2015 Elsevier Inc.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics


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