https://wikipediaquality.com/index.php?title=Bricking_Semantic_Wikipedia_by_Relation_Population_and_Predicate_Suggestion&feed=atom&action=historyBricking Semantic Wikipedia by Relation Population and Predicate Suggestion - Revision history2024-03-29T15:39:08ZRevision history for this page on the wikiMediaWiki 1.30.0https://wikipediaquality.com/index.php?title=Bricking_Semantic_Wikipedia_by_Relation_Population_and_Predicate_Suggestion&diff=490&oldid=prevLibrarian: New scientific work2018-07-03T21:40:21Z<p>New scientific work</p>
<p><b>New page</b></p><div>{{Infobox work<br />
| title = Bricking Semantic Wikipedia by Relation Population and Predicate Suggestion<br />
| date = 2012<br />
| authors = [[Haofen Wang]]<br />[[Linyun Fu]]<br />[[Yong Yu]]<br />
| issn = 15701263<br />
| doi = 10.3233/WIA-2012-0249<br />
}}<br />
'''Bricking Semantic Wikipedia by Relation Population and Predicate Suggestion''' - scientific work about [[Wikipedia quality]] published in 2012, written by [[Haofen Wang]], [[Linyun Fu]] and [[Yong Yu]].<br />
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== Overview ==<br />
Semantic [[Wikipedia]] aims to enhance Wikipedia by adding explicit semantics to links between Wikipedia entities. However, authors have observed that it currently suffers the following limitations: lack of semantic annotations and lack of semantic annotators. In this paper, authors resort to relation population to automatically extract relations between any entity pair to enrich semantic data, and predicate suggestion to recommend proper relation labels to facilitate semantic annotating. Both tasks leverage relation classification which tries to classify extracted relation instances into predefined relations. However, due to the lack of labeled data and the excessiveness of noise in Semantic Wikipedia, existing approaches cannot be directly applied to these tasks to obtain high-quality annotations. In this paper, to tackle the above problems brought by Semantic Wikipedia, authors use a label propagation algorithm and exploit semantic features like domain and range constraints on categories as well as linguistic features such as dependency trees of context sentences in Wikipedia articles. The experimental results on 7 typical relation types show the effectiveness and efficiency of their approach in dealing with both tasks.<br />
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== Embed ==<br />
=== Wikipedia Quality ===<br />
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Wang, Haofen; Fu, Linyun; Yu, Yong. (2012). "[[Bricking Semantic Wikipedia by Relation Population and Predicate Suggestion]]". Web Intelligence and Agent Systems Volume 10, Issue 3, 2012, pp. 319-330. ISSN: 15701263. DOI: 10.3233/WIA-2012-0249. <br />
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=== English Wikipedia ===<br />
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{{cite journal |last1=Wang |first1=Haofen |last2=Fu |first2=Linyun |last3=Yu |first3=Yong |title=Bricking Semantic Wikipedia by Relation Population and Predicate Suggestion |date=2012 |issn=15701263 |doi=10.3233/WIA-2012-0249 |url=https://wikipediaquality.com/wiki/Bricking_Semantic_Wikipedia_by_Relation_Population_and_Predicate_Suggestion |journal=Web Intelligence and Agent Systems Volume 10, Issue 3, 2012, pp. 319-330}}<br />
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Wang, Haofen; Fu, Linyun; Yu, Yong. (2012). &amp;quot;<a href="https://wikipediaquality.com/wiki/Bricking_Semantic_Wikipedia_by_Relation_Population_and_Predicate_Suggestion">Bricking Semantic Wikipedia by Relation Population and Predicate Suggestion</a>&amp;quot;. Web Intelligence and Agent Systems Volume 10, Issue 3, 2012, pp. 319-330. ISSN: 15701263. DOI: 10.3233/WIA-2012-0249. <br />
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