Ontology enhanced web image retrieval: Aided by Wikipedia and spreading activation theory

• Main Authors: WANG, Huan, JIANG, Xing, CHIA, Liang-Tien, TAN, Ah-hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2008
• Subjects: Ontology; Wikipedia; Spreading Activation; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6278; https://ink.library.smu.edu.sg/context/sis_research/article/7281/viewcontent/Ontology_WebIR_MIR08_pv.pdf
• Institution: Singapore Management University

Ontology, as an efective 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 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.