Ontology-based Semantic Search Engine For Deep Web

WWW has been a tremendous information depository along with its rapid evolution and popularization. Search on WWW become more and more difficult because information over loading and drift off course on WWW. The shortcoming of directory tree Search Engine and keyword Search Engine is emerged because of autonomy, commonality, heterogeneity, dynamic, openness and increase on exponent. Search like navigation base on keyword only and surface Web make index capability increase on exponent, make recall ratio and precision ratio lower and lower. The new knowledge representation on WWW has become significant to improve recall ratio and precision ratio also satisfy request of user on knowledge granularity search and semantic search. The creator of semantic Web Tim Berners-lee put forward architecture of semantic Web, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Semantic Web is capable of solve these problem. This thesis applies the architecture of semantic Web on search of Deep Web to put forward semantic search engine on Deep Web based on ontology. Semantic search engine on Deep Web based on ontology could solve these problem traditional search engine can not solve like searching only surface Web, can not semantic search, can not search on"knowledge granularity". Four innovations in this thesis are as follows:First: semantic search engine on Deep Web based on ontology makes up traditional search engine's shortage. For example, traditional search engine could only search surface Web based on keyword, but semantic search and metadata search. Because of these, it could improve the recall ratio and precision ratio, also avoid the restriction on index capability. Second: this thesis represent query interface by RDF metadata. Query interface is pattern of database, so which is described in metadata descriptive language RDF. Search query interface is searched through searching RDF semantically using ontology to make precision ratio higher. Because query interface has high domain pertinence, it make search engine's precision ratio higher.Third: semantic search engine on Deep Web based on ontology are composed of Deep Web crawler, Deep Web classifier, Deep Web form extractor, NLI (nature language interface), semantic reasoning, form retrieval, Web retrieval, query interface integration and result integration. In this thesis, discovery and classification of Deep Web, semantic search of query interface's RDF are researched weightily.Fouth: vocable relation computing algorithms uses pattern match based on structure using HowNet as ontology. Deep Web feature select algorithms search feature by Tabu searching strategy using vocable frequency as separability criterion. Deep Web query interface RDF extractor algorithms makes map between query interface html code and model of RDF. Deep Web query interface RDF search algorithms make keyword sequence that user input as search condition to Classify, then Extend Concept to get Concept sequence and Instance sequence. RDF is searched by language RDQL according to Concept sequence. Search on Web is sent in http protocol according to RDF search result and Instance sequence. Algorithms discussed above is validated in this thesisDeep Web search engine based on semantic Web has been investigated theoretically in this thesis. Framework and algorithms thinking of search engine are feasible. The search engine could be developed on Jena. We validate it in domain.