A semantic-based approach to content abstraction and annotation for content management

Hui Chuan Chu, Ming Yen Chen, Yuh Min Chen

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

In recent years, knowledge becomes the most important asset of individuals as well organizations, and determines the competitiveness of an enterprise. Content is a knowledge container that implies what human beings transform their knowledge in when they want to communicate with other people. Therefore, effective content management can achieve the goal and value of knowledge management. Content management activities are depending on meta-content which represents the content feature and are generated through the procedures of content abstraction and annotation. In general, the content creator utilizes words and logical combinations to express his intentions and concepts in semantic formats that are comprehensible to human beings. Accordingly, managing content from a semantic approach can effectively improve transparency and visibility of the content, and guides both the content creator and the user to engage in seamless, semantic-based communications and interactions. This study proposes a semantic-based content abstraction and annotation approach and a semantic pattern for bridging the semantic gaps of content management. Based on this approach, a semantic-driven content management environment that features a high interoperability of content can be constructed to accomplish the following goals and benefits: (1) effectively bridging semantic gaps in the existing content management systems; (2) delivering the right content to the right user at the right time; (3) bridging the semantic gaps for the content author and the customer to increase the efficiency of content management; and (4) realizing the acquisition, analysis, access, management, diffusion, interpretation, and innovation of knowledge.

Original languageEnglish
Pages (from-to)2360-2376
Number of pages17
JournalExpert Systems With Applications
Volume36
Issue number2 PART 1
DOIs
Publication statusPublished - 2009 Mar

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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