Research On Semantic Search Based On Ontology Repository Reasoning

As the quickly increasement of web information, web search has become the most widely application based on Internet. The effect of the current search engine can't satisfy users. The recall and precision of earch engine need to be improved. The apperance of Semantic Web provides a new method for search engine. To research semantic search, we need bring the technology of Semantic Web into search engine, tightly integrate retrieval and reasoning to improve search results and evolve to the next generation search engine building on Semantic Web.Now the research of semantic search is still on the primary stage. Only several research cases are reported. There is not any universal method for the research of semantic search. Based on reading related references and analizing the research status, we classify the semantic search. According the role in which ontology plays, current research of semantic search are sort into three types, they are augment semantic search based on traditional search, intelligent semantic search based on ontology reasoning and other semantic search. Existed systems can't preferable integrate retrieval and inference. Some of them only use traditional search function and others just offer formal query. The inference services already implemented is still in the tentative process and there is not any full-grown semantic ranking method. We do some deep research mainly in semantic search model, semantic search reasoning and result ranking of association relationship.Traditional search technology can be used for semantic search which integrate retrieval and inference. However it is not fully applicable. Based on traditional search, a semantic search model is provided. The model syncretizes vector space model and modified bool model, integrate reasoning and retrieval to get better semantic information of user's query. The model is applied in the field of secure access control. Based on RBAC security ontology, secure access controlling is implemented. The aim of extending the search capability is implemented. Camparing with traditional search, the recall and precision of semantic search are improved. At the same time semantic search can provide association relationship query which finds out the complicated relatiships between entities. Semantic search is more intelligent than traditional search for bringing inference into search.Reasoning is the key of semantic search. Description logic has become the logic base for Semantic Web. However description logic is not faultless. It has its own limitation. The description capbiltiy and inference power still need to be extended. Combining rules and description logic is a more feasible method than others. SWRL is introduced to implement the ablity of description for ontology rules. Based on these, a reasoning algorithm which transforms special default rules into instances of Abox in description logic is provided. The algorithm is designed specially for the common cases that the change of special default rules usually does not affect Tbox. The conversion between the default rules and instances can simplify the reasoning process and the comlixity of the algorithm is unaltered. So the algorithm is feasible. The reasoning case validates the algorithm. Presently the reasoning of semantic search is implemented mostly by forward deduction system which is inefficient. So the inference implementation of description logic in semantic search can improve the efficiency using the optimized tableaux algorithm and combining special default rules to implement the reasoning in semantic search. It's more efficient than the general forward deduction system based on RDF triples. The reasoning system offers ontology parsing, adding default rules and ontology reasoning function. It improves machine's understanding capability and satisfies the inference requirement of semantic search.Association relationship search can find out the complicated relationships between entities. As the fast increasement of resources in Semantic web, the number of association relationship is possiblely greater than the number of entities themselves. So how to rank association relationship is becoming the hot key of semantic search. Aiming at the common path association relationship, three most important influence factors are confirmed. They are domain related degree, semantic assocation length and semantic assocition frenquency. The method of computing these three factors is provided. Based on these a method of ranking semantic association is offered. The method can firstly return the useful semantic association relationships to users.Based on the theory and research production mentioned above, Smartch, a prototype system of semantic search, is designed and implemented. The main function includes basic search, concept search, graphic user-defined search and association relationship search. We give performance analysis and evaluation through system experiment.