Minimally Supervised Relation Identification from Wikipedia Articles
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 o...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Korea Institute of Science and Technology Information
2018-12-01
|
| Series: | Journal of Information Science Theory and Practice |
| Subjects: | |
| Online Access: | https://doi.org/10.1633/JISTaP.2018.6.4.3 |
| _version_ | 1819319294834507776 |
|---|---|
| author | Heung-Seon Oh Yuchul Jung |
| author_facet | Heung-Seon Oh Yuchul Jung |
| author_sort | Heung-Seon Oh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T11:01:24Z |
| format | Article |
| id | doaj.art-943be1988b5e4c4c90e8e3c883b82e88 |
| institution | Directory Open Access Journal |
| issn | 2287-9099 2287-4577 |
| language | English |
| last_indexed | 2024-12-24T11:01:24Z |
| publishDate | 2018-12-01 |
| publisher | Korea Institute of Science and Technology Information |
| record_format | Article |
| series | Journal of Information Science Theory and Practice |
| spelling | doaj.art-943be1988b5e4c4c90e8e3c883b82e882022-12-21T16:58:43ZengKorea Institute of Science and Technology InformationJournal of Information Science Theory and Practice2287-90992287-45772018-12-0164283810.1633/JISTaP.2018.6.4.3Minimally Supervised Relation Identification from Wikipedia ArticlesHeung-Seon Oh0Yuchul Jung1Korea University of Technology and Education, Cheonan, KoreaKumoh National Institute of Technology, Gumi, 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.https://doi.org/10.1633/JISTaP.2018.6.4.3relation identificationWikipedia miningunsupervised clustering |
| spellingShingle | Heung-Seon Oh Yuchul Jung Minimally Supervised Relation Identification from Wikipedia Articles Journal of Information Science Theory and Practice relation identification Wikipedia mining unsupervised clustering |
| title | Minimally Supervised Relation Identification from Wikipedia Articles |
| title_full | Minimally Supervised Relation Identification from Wikipedia Articles |
| title_fullStr | Minimally Supervised Relation Identification from Wikipedia Articles |
| title_full_unstemmed | Minimally Supervised Relation Identification from Wikipedia Articles |
| title_short | Minimally Supervised Relation Identification from Wikipedia Articles |
| title_sort | minimally supervised relation identification from wikipedia articles |
| topic | relation identification Wikipedia mining unsupervised clustering |
| url | https://doi.org/10.1633/JISTaP.2018.6.4.3 |
| work_keys_str_mv | AT heungseonoh minimallysupervisedrelationidentificationfromwikipediaarticles AT yuchuljung minimallysupervisedrelationidentificationfromwikipediaarticles |