download607 view1,171
twitter facebook

공공누리This item is licensed Korea Open Government License

dc.contributor.author
Oh, Heung-Seon
dc.contributor.author
Jung, Yuchul
dc.date.accessioned
2019-08-28T01:35:59Z
dc.date.available
2019-08-28T01:35:59Z
dc.date.issued
2018-12-30
dc.identifier.issn
2287-4577
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/13538
dc.description.abstract
Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Korea Institute of Science and Technology Information
dc.relation.ispartofseries
Journal of Information Science Theory and Practice;Volume 6 Issue 4
dc.title
Minimally Supervised Relation Identification from Wikipedia Articles
dc.type
Article
dc.citation.endPage
16
dc.citation.startPage
6
dc.contributor.approver
KOAR, ADMIN
dc.date.dateaccepted
2019-08-28T01:35:59Z
dc.date.datesubmitted
2019-08-28T01:35:59Z
dc.identifier.bibliographicCitation
page. 6 - 16
dc.subject.keyword
relation identification
dc.subject.keyword
Wikipedia mining
dc.subject.keyword
unsupervised clustering
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 6 - No. 4
Files in This Item:
Thumbnail Oh_Jung_v6n4_28-38.pdf235.64 kBDownload

Browse