Difference between revisions of "Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory"
(Creating a new page - Ontology Enhanced Web Image Retrieval: Aided by Wikipedia & Spreading Activation Theory) |
(+ links) |
||
| Line 1: | Line 1: | ||
| − | '''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. | + | '''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 == | == 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. | + | 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. |
Revision as of 08:09, 16 June 2019
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.
