Automatic ontology population using deep learning for triple extraction

Ming Hsiang Su, Chung Hsien Wu, Po Chen Shih

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面262-267
頁數6
ISBN(電子)9781728132488
DOIs
出版狀態Published - 2019 十一月
事件2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China
持續時間: 2019 十一月 182019 十一月 21

出版系列

名字2019 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
國家China
城市Lanzhou
期間19-11-1819-11-21

All Science Journal Classification (ASJC) codes

  • Information Systems

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