Towards a Bottom-up Approach to Data and Knowledge Modeling and Fusion

Liming Chen, Ying Du, Hui Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The Web is evolving into a global information and knowledge space that brings huge potentials as well as great challenges. The Semantic Web tries to harness the inter-connected information space by giving information well-defined meaning, but so far failed to deliver its promises. This paper introduces a bottom-up approach to heterogeneous data and knowledge modeling and fusion. The enabling technologies for this approach include the Semantic Web, personalized knowledge portal and social networking. We describe a system architecture that allows individuals to create semantic content in their own desktop with little effort and pool it into a global virtual knowledge base where content can be seamlessly fused and shared. A prototype system is partially developed to illustrate the operation of the approach through which testing and evaluation are conducted.
Original languageEnglish
Title of host publicationUnknown Host Publication
Publication statusPublished - 01 Jan 2007

Bibliographical note

Workshop on Semantic Web for Collaborative Knowledge Acquisition, in the 20th International Joint Conference on Artificial Intelligence ; Conference date: 01-01-2007


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