Xing Jiang

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Xing Jiang is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Wikipedia2Onto - building concept ontology automatically, experimenting with web image retrieval Ontology
Semantic concept
Web image classification
Informatica (Ljubljana) English 2010 Given its effectiveness to better understand data, ontology has been used in various domains including cartificial intelligence, biomedical informatics and library science. What we have tried to promote is the use of ontology to better understand media (in particular, images) on the World Wide Web. This paper describes our preliminary attempt to construct a large-scale multi-modality ontology, called AutoMMOnto, for web image classification. Particularly, to enable the automation of text ontology construction, we take advantage of both structural and content features of Wikipedia and formalize real world objects in terms of concepts and relationships. For visual part, we train classifiers according to both global and local features, and generate middle-level concepts from the training images. A variant of the association rule mining algorithm is further developed to refine the built ontology. Our experimental results show that our method allows automatic construction of large-scale multi-modality ontology with high accuracy from challenging web image data set. 0 0
Ontology enhanced web image retrieval: Aided by wikipedia & spreading activation theory Ontology
Spreading activation
Proceedings of the 1st International ACM Conference on Multimedia Information Retrieval, MIR2008, Co-located with the 2008 ACM International Conference on Multimedia, MM'08 English 2008 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 ex- ploiting ontology for a better understanding of media task (particularly, images) on the World Wide Web. To achieve our goal, two open issues are inevitably in- volved: 1) How to avoid the tedious manual work for ontol- ogy construction? 2) What are the effective inference models when using an ontology? Recent works[11, 16] about ontol- ogy learned from Wikipedia has been reported in conferences targeting the areas of knowledge management and artificial intelligent. There are also reports of different inference mod- els 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 cate- gorization and page content, we choose it as our knowledge source and propose an automatic ontology construction ap- proach. We prove that it is a viable way to build ontology under various domains. To address the inference model is- sue, 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 cor- rect information processing model in the semantic network, are consequently introduced for inference. We have imple- mented 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. Copyright 2008 ACM. 0 0
Ontology enhanced web image retrieval: aided by wikipedia \& spreading activation theory Ontology
Spreading activation
MIR English 2008 0 0
A framework for inter-organizational collaboration using communication and knowledge management tools Blogs
Bulletin board
Knowledge management
Online community
Lecture Notes in Computer Science English 2007 Organizations are often involved in joint ventures or coalitions with multiple, diverse partners. While the ability to communicate across organizational boundaries is important to their success, the organizations may have different cultures, processes, and jargon which inhibit their ability to effectively collaborate. The objective of this paper is to identify a framework that enables organizations to communicate complex knowledge across organizational boundaries. It leverages communication and knowledge management tools such as the wiki, and calls for more integration between these tools. 0 0