Constructing dynamic ontologies from biomedical publications

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In recent years, there has been explosive growth in the amount of biomedical publications. In this paper, we propose a semantic framework that aims to automatically generate an ontology by extracting assertions and topics from multiple free-text scientific publications in PubMed. The pipeline approach for knowledge discovery and ontology generation in the proposed framework has been implemented on the Spark parallel engine based on the Stanford CoreNLP for Natural Language Processing, TF-IDF (Term Frequency Inverse Document Frequency) for feature extraction, OpenIE (Open Information Extraction) for relation extraction, K-Means clustering for topic discovery and OWL API for ontology generation. We have shown that the ontology generated may be very effective in biomedical applications (such as paper search and summarization) with scientific publications.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
EditorsIllhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages581-584
Number of pages4
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume2017-January

Conference

Conference2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Country/TerritoryUnited States
CityKansas City
Period11/13/1711/16/17

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