Concept-Lattice-Based Text Filtering System Design and Implementation

The study of traditional text filtering with keywords retrieval ignores the semantic relations between keywords, and it results in the bottleneck of the further development of text filtering. To solve this problem, three steps are adopted. Firstly, concept lattice theory should be introduced into traditional text filtering, the formal context is employed to organize filtering text and domain feature words, and then the concept lattice is created. Secondly, the structure of concept lattice and the relevancy of Object-Properties are used to convert the match between text and user information demands into the match between concept lattice nodes and the user's information demands, and then the text filtering is achieved. Lastly, the semantic relations between concepts are measured, and the domain ontology is used to calculate the similarity between domain feature words and user interest feature words, and then the accuracy of text filtering is improved. In order to achieve this goal, a concept-lattice-based text filtering model (CL-TFM) is proposed, the algorithm of related concept lattice progressive construction and the computing method of domain ontology based conceptual similarity are constructed, and a novel text filtering system named as CL-TFS (Concept Lattice based Text Filtering System) is designed and implemented. The related experimental results show that the recall and precision rates of the text filtering system of CL-TFS has obvious advantages superior to the ones of keyword-based text filtering system.