陳明彥

In recent years, knowledge becomes the most important asset of individuals as well organizations, and also determines the competitiveness of an enterprise. Information content is a knowledge container that implies what human beings transform their knowledge in when they want to communicate with other people. Therefore, effective information content retrieval can achieve the goal and value of knowledge sharing and reusing. The existing information retrieval systems are mostly keyword-based and retrieve relevant information content by matching keywords. Keyword-based search, in spite of its merits of expedient query for information and ease-of-use, has failed to represent the complete semantics contained in the content and has let to the retrieval failure. In a textual content, the author’s intention is represented in a semantic format of various combinations of word-word relations that are comprehensible to human beings. Accordingly, retrieving information content from a semantic approach can effectively improve transparency and visibility of the content and guide both the content creator and the content user to engage in seamless, semantic-based communications and interactions. This study developed a semantic-aware mechanism for multipurpose information retrieval that handles the processing, recognition, extraction, extensions and matching of content semantics to achieve the following objectives: (1) to analyze and determine the semantic features of information content; (2) to develop a semantic pattern that represents semantic features of the content, and to structuralize and materialize semantic features; (3) to design a multipurpose information retrieval model that provides the most appropriate retrieval method for different types of users depending on their needs. This mechanism is capable of improving the traditional problem of keyword search and enables the user to perform a semantic-aware query and search for the required information, thereby improving the reusing and sharing of information content.