Extraction Research-oriented Social Networking Applications

We are surrounded by huge amount of information since the search engine appeared yet. But most of them are repetitive information. Search engine returned too many results to find useful information. The efficiency that people get the information they need will be greatly improved if there is a method could filter the retrieval results and just extract the key information. Based on this, Problems of relation extraction of named entities recognition in the field of social network have been mainly studied in this thesis. And an entity of social relations facing social network has been tried to established in the research. It means that relevant words were extracted from the sentences with two or more named entities to describe their relations. Meanwhile, with the help of reasoning engine Jess, a series of SWRL rules have been defined to reason and excavate the implicit relationships of entities.In named entity recognition task, personal name and organization name have been aimly identified in the study. The different roles in personal and institutional names represented by phrase segments in the sentences have been marked by using semantic role labeling and Viterbi algorithm. Then some proper word-formation rules were generated according to characteristics of word-formation to do pattern match, so as to get the final results. In the open test on realistic corpus, the result reflected that its recalling rate is better than precision, which is nearly 70%, and still have greatly improval space. The results show the effectiveness of the method.In the task of relation extraction, seven-step and iterative methods in ontological engineering will be integrated in this thesis to construct an social relationship ontology which faced social network and applied to the Internet business enterprisesis. The defined SWRL rules and the social relationship ontology have been imported into Jess rule reasoning engine to excavate the implicit relationship between entities, and eventually get the (entity relationship entity) relationship triad. The method greatly refined the content of information and improved the efficiency of information acquisition.