NDLI: Ontology enhanced web image retrieval: aided by wikipedia & spreading activation theory

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Ontology enhanced web image retrieval: aided by wikipedia & spreading activation theory

Ontology enhanced web image retrieval: aided by wikipedia & spreading activation theory

Content Provider	ACM Digital Library
Author	Wang, Huan Jiang, Xing Chia, Liang-Tien Tan, Ah-Hwee
Abstract	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, we are particularly interested in exploiting ontology for a better understanding of media task (particularly, images) on the World Wide Web. To achieve our 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, we 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, we choose it as our knowledge source and propose an automatic ontology construction approach. We prove that it is a viable way to build ontology under various domains. To address the inference model issue, we 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. We have implemented a prototype system with the developed solutions for web image retrieval. By comprehensive experiments on the canine category of the animal kingdom, we show that this is a scalable architecture for our proposed methods.
Starting Page	195
Ending Page	201
Page Count	7
File Format	PDF
ISBN	9781605583129
DOI	10.1145/1460096.1460128
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-10-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ontology Wikipedia Spreading activation
Content Type	Text
Resource Type	Article

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