Developing a semantic-enable information retrieval mechanism

The existing information retrieval systems are mostly keyword-based and retrieve relevant documents or information 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. Query constructed by descriptions in natural language best reflects querist’s intention. This study developed a semantic-enable information retrieval mechanism 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 content, to develop a semantic pattern that represents semantic features of the content, and to structuralize and materialize semantic features; (2) to analyze user’s query and extend its implied semantics through semantic extension so as to identify more semantic features for matching; and (3) to generate contents with approximate semantics by matching against the extended query to provide correct contents to the querist. This mechanism is capable of improving the traditional problem of keyword search and enables the user to perform a semantic-based query and search for the required information, thereby improving the reusing and sharing of information.