Title :
Rough Ontology Based Semantic Information Retrieval
Author :
Yinghui Huang ; Guanyu Li ; Qiangqiang Li
Author_Institution :
Fac. of Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China
Abstract :
It is known that traditional precise ontology based information retrieval cannot finish implicit semantic information mining. To solve this problem, rough ontology is introduced into semantic information retrieval to meet the user needs to utmost extent. Firstly, semantic information retrieval is defined and its advantages are analyzed in detail. Secondly, rough ontology is employed to extend precise ontology. Thirdly, rough ontology based the semantic information retrieval model and its semantic similarity calculation method is given. Finally, a rough ontology based semantic information retrieval system named as ROSRS is designed and its implementation method is given. The experimental results show that system ROSRS can retrieval information semantically not only from precise ontology but also from rough ontology, and the implicit information can be gotten. Furthermore, the recall ratio and precision ratio of retrieval results can be improved.
Keywords :
data mining; information retrieval; ontologies (artificial intelligence); ROSRS system; precise ontology based information retrieval; retrieval precision ratio; retrieval recall ratio; rough ontology based semantic information retrieval model; rough ontology based semantic information retrieval system; semantic information mining; semantic similarity calculation method; Approximation methods; Information retrieval; Ontologies; Ports (Computers); Semantic Web; Semantics; Syntactics; precise ontology; rough ontology; semantic information retrieval; semantic similarity;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on
Conference_Location :
Hangzhou
DOI :
10.1109/ISCID.2013.23