Automatic ontology population using deep learning for triple extraction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Ontology is a kind of representation used to represent knowledge in a form that computers can derive the content meaning. The purpose of this work is to automatically populate an ontology using deep neural networks for updating an ontology with new facts from an input knowledge resource. In this study for automatic ontology population, a bi-LSTM-based term extraction model based on character embedding is proposed to extract the terms from a sentence. The extracted terms are regarded as the concepts of the ontology. Then, a multi-layer perception network is employed to decide the predicates between the pairs of the extracted concepts. The two concepts (one serves as subject and the other as object) along with the predicate form a triple. The number of occurrences of the dependency relations between the concepts and the predicates are estimated. The predicates with low occurrence frequency are filtered out to obtain precise triples for ontology population. For evaluation of the proposed method, we collected 46, 646 sentences from Ontonotes 5.0 for training and testing the bi-LSTM-based term extraction model. We also collected 404, 951 triples from ConceptNet 5 for training and testing the multilayer perceptron-based triple extraction model. From the experimental results, the proposed method could extract the triples from the documents, achieving 74.59% accuracy for ontology population.

Original languageEnglish
Title of host publication2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages262-267
Number of pages6
ISBN (Electronic)9781728132488
DOIs
Publication statusPublished - 2019 Nov
Event2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China
Duration: 2019 Nov 182019 Nov 21

Publication series

Name2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

Conference

Conference2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
CountryChina
CityLanzhou
Period19-11-1819-11-21

All Science Journal Classification (ASJC) codes

  • Information Systems

Fingerprint Dive into the research topics of 'Automatic ontology population using deep learning for triple extraction'. Together they form a unique fingerprint.

  • Cite this

    Su, M. H., Wu, C. H., & Shih, P. C. (2019). Automatic ontology population using deep learning for triple extraction. In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 (pp. 262-267). [9023113] (2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPAASC47483.2019.9023113