NDLI: Self-Supervised Chinese Ontology Learning from Online Encyclopedias

Content Provider	PubMed Central
Author	Hu, Fanghuai Shao, Zhiqing Ruan, Tong
Copyright Year	2014
Abstract	Constructing ontology manually is a time-consuming, error-prone,and tedious task. We present SSCO, a self-supervised learningbased chinese ontology, which contains about 255 thousand concepts,5 million entities, and 40 million facts. We explore the three largest onlineChinese encyclopedias for ontology learning and describe how totransfer the structured knowledge in encyclopedias, including article titles,category labels, redirection pages, taxonomy systems, and InfoBoxmodules, into ontological form. In order to avoid the errors in encyclopediasand enrich the learnt ontology, we also apply some machinelearning based methods. First, we proof that the self-supervised machinelearning method is practicable in Chinese relation extraction (at leastfor synonymy and hyponymy) statistically and experimentally and trainsome self-supervised models (SVMs and CRFs) for synonymy extraction,concept-subconcept relation extraction, and concept-instance relation extraction;the advantages of our methods are that all training examplesare automatically generated from the structural information of encyclopediasand a few general heuristic rules. Finally, we evaluate SSCO intwo aspects, scale and precision; manual evaluation results show thatthe ontology has excellent precision, and high coverage is concluded bycomparing SSCO with other famous ontologies and knowledge bases; theexperiment results also indicate that the self-supervised models obviouslyenrich SSCO.
Related Links	http://dx.doi.org/10.1155/2014/848631
Starting Page	848631
File Format	PDF
ISSN	23566140
e-ISSN	1537744X
Journal	The Scientific World Journal
Volume Number	2014
Language	English
Publisher	Hindawi Publishing Corporation
Publisher Date	2014-01-01
Access Restriction	Open
Rights Holder	Hindawi Publishing Corporation
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Environmental Science(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine Biochemistry, Genetics and Molecular Biology Environmental Science

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