Study On Ontology-based Micro-content Aggregation And Inquiry Technology

Traditional HTML can only display but can not make the computer understand the information. For this reason, the traditional keyword-based Search tools are not high in precision rate, recall rate and intelligence. Existing ontology-based semantic matching technology only takes into account the class concepts, leading to the precision of searching was low for the individual-based application of micro-content.In order to improve the accuracy of the discovery and intelligence level of Micro-content, based on Semantic Web technology and take the micro-content aggregation and query mechanism as the research object, this article make some in-depth study in the following aspects:( 1) The semantic composition structure of micro-content and ontology-based tagging system. Micro-content, must be semantic tagging in order to make computer be able to understand them. To this end, first of all, the semantic structure of micro-content was studied and then a prototype micro-content tagging system is presented. An improved ontology-based semantic micro-content tagging system is presented at the end in order to overcome the defect of the prototype that does not have ability to express knowledge.( 2) The micro-content aggregation framework based on OWL ontology individual concepts and semantic relations. First of all, an analysis on polymerization mode is done and then the model based on external semantic elements is selected as micro-content aggregation model. Based on the analysis on the semantics elements distribution characteristics of OWL ontology, it's pointed out that those semantic relations between concepts must be taken into consideration in the process of aggregation of individual concepts because of the semantic features of individual. On the other hand, there are mapping relations among micro-contents and individual concepts of the individual layer of OWL ontology. Followed by an analysis on the aggregation principle of micro-content based on individual concepts and semantic relations, semantic indexing based on individual concepts and semantic relations is presented as the specific technical implementation of micro-content aggregation mechanism.( 3) Micro-content discovery algorithm based on the semantic indexing. Based on the semantic distribution characteristics of the OWL ontology, matching rules of individual concept is studied, and then discovery algorithm of micro- content is presented based on the semantic indexing.Finally, use protégéand relational databases to build the OWL ontology-based knowledge base and semantic indexing, respectively. Base on this, a simulation experiment is done for the micro-contents discovery algorithm and the effectiveness of the semantic index and micro-content discovery algorithm are proved.