View source for Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory

{{Infobox work
| title = Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory
| date = 2008
| authors = [[Huan Wang]]<br />[[Xing Jiang]]<br />[[Liang-Tien Chia]]<br />[[Ah-Hwee Tan]]
| doi = 10.1145/1460096.1460128
| link = https://dl.acm.org/citation.cfm?id=1460096.1460128
}}
'''Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Huan Wang]], [[Xing Jiang]], [[Liang-Tien Chia]] and [[Ah-Hwee Tan]].

== Overview ==
Ontology, as an effective approach to bridge the semantic gap in various domains, has attracted a lot of interests from multimedia researchers. Among the numerous possibilities enabled by [[ontology]], authors are particularly interested in exploiting ontology for a better understanding of media task (particularly, images) on the World Wide Web. To achieve goal, two open issues are inevitably involved: 1) How to avoid the tedious manual work for ontology construction? 2) What are the effective inference models when using an ontology? Recent works[11, 16] about ontology learned from [[Wikipedia]] has been reported in conferences targeting the areas of knowledge management and artificial intelligent. There are also reports of different inference models being investigated [5, 13, 15]. However, so far there has not been any comprehensive solution. In this paper, authors look at these challenges and attempt to provide a general solution to both questions. Through a careful analysis of the online encyclopedia Wikipedia's categorization and page content, authors choose it as knowledge source and propose an automatic ontology construction approach. Authors prove that it is a viable way to build ontology under various domains. To address the inference model issue, authors provide a novel understanding of the ontology and consider it as a type of semantic network, which is similar to brain models in the cognitive research field. Spreading Activation Techniques, which have been proved to be a correct information processing model in the semantic network, are consequently introduced for inference. Authors have implemented a prototype system with the developed solutions for web image retrieval. By comprehensive experiments on the canine category of the animal kingdom, authors show that this is a scalable architecture for proposed methods.

== Embed ==
=== Wikipedia Quality ===
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Wang, Huan; Jiang, Xing; Chia, Liang-Tien; Tan, Ah-Hwee. (2008). "[[Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory]]".DOI: 10.1145/1460096.1460128. 
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=== English Wikipedia ===
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{{cite journal |last1=Wang |first1=Huan |last2=Jiang |first2=Xing |last3=Chia |first3=Liang-Tien |last4=Tan |first4=Ah-Hwee |title=Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory |date=2008 |doi=10.1145/1460096.1460128 |url=https://wikipediaquality.com/wiki/Ontology_Enhanced_Web_Image_Retrieval:_Aided_by_Wikipedia_&_Spreading_Activation_Theory}}
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=== HTML ===
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Wang, Huan; Jiang, Xing; Chia, Liang-Tien; Tan, Ah-Hwee. (2008). &amp;quot;<a href="https://wikipediaquality.com/wiki/Ontology_Enhanced_Web_Image_Retrieval:_Aided_by_Wikipedia_&_Spreading_Activation_Theory">Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory</a>&amp;quot;.DOI: 10.1145/1460096.1460128. 
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[[Category:Scientific works]]