Research Of Domain Ontology Concept Extraction Based On Association Rules

The semantic web,as a kind of network which people can interact with the machine intelligence on the basis of understanding, and can be achieved from the "Content Match" to "machine understanding", has become the assumptions and expectations of future networks. Ontology as a conceptual model describing the semantics and knowledge is an important medium and core components to achieve intelligent interactive information of Semantic Web. At present, the Ontology has been widely used in knowledge engineering, semantic retrieval, intelligent libraries, but mainly rely on the Ontology to build by hand, consuming resources. Therefore, need an ontology learning approach to achieve automatic or semi-automatic adaptation of new or expansion of existing ontology, and improve the degree of automation and efficiency of ontology construction, and reduce labor participation and ontology construction engineering, while avoiding the tendency to artificially constructed Ontology error.Domain ontology concepts is critical in Ontology Learning extraction, will directly affect the relationship between the concept of extraction accuracy and completeness. In order to improve the quality of domain ontology extraction,this article introduced the concept of association rules and semantic rules in the domain ontology extraction. The main contents of this paper are as follows:Ontology candidate concepts extraction. Since Ontology concept generally consists of a noun or noun phrases constitute, need to use the word corpus processing system to extract noun, noun phrases parts of data source as a candidate set of ontology concepts. At the same time, use the candidate bitmap records the physical relationship between the candidate concepts, mainly used in association rule mining frequent items.Acquiring terminology membership of domain and build its formal definition and computational models. Computing term relationship between the concepts and establishing relationship matrix, for each extracted term, the establishment of the depth and breadth value of the term in the field based on the concept relation matrix, used to quantitative analysis the degree of field membership of term in the field, namely the field membership checks.Developing semantic rules. Based on previous research on Chinese and Chinese-processing tools available for POS tagging, and then combined with natural language syntax, lexical rules and intrinsic properties of segmentation system, developing semantic rules by nouns and noun phrase structure analysis and refinement.Generate domain ontology concepts. Candidate field ontology set is a rough ontology concept set, in order to obtain high accuracy and high completeness of domain ontology concept collection, the collection need be optimized. Using rselevance and consistency of the field inspection techniques to filter domain-independent terms and general terms in the field; Combining semantic rules to filter the term of the set which do not constitute nouns and noun phrases; filtering the terminology which field membership is not high enough, the term will be optimized (including synthetic and non-synthetic terminology) as the main domain concepts.