• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.1
• /
• pp.21-32
• /
• 2010

User query formation highly acts on the effectiveness of information retrieval when we retrieve documents from the general domain as a web. This thesis proposes a intelligent information retrieval method based on a cross-lingual knowledge base to effectively perform a cross-lingual text retrieval from the web. The inferred knowledge from the cross-lingual knowledge base helps user's word association to make up user query easily and exactly for effective cross-lingual text information retrieval. This thesis develops user's query reformation algorithm and experiments it with Korean and English web. Experimental results show that the algorithm based on the proposed knowledge base is much more effective than without knowledge base in the cross-lingual text retrieval.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.8 no.1
• /
• pp.53-62
• /
• 2008

Cross-lingual text retrieval(CLTR) is the information retrieval in which a user tries to search a set of documents written in one language for a query another language. This thesis proposes a CLTR system based on fuzzy multilingual thesaurus to handle a partial matching between terms of two different languages. The proposed CLTR system uses a fuzzy term matrix defined in our thesis to perform the information retrieval effectively. In the defined fuzzy term matrix, all relation degrees between terms are inferred from using the transitive closure algorithm to reflect all implicit links between terms into processing of the information retrieval. With this framework, the CLTR system proposed in our thesis enhances the retrieval effectiveness because it is able to emulate a human expert's decision making well in CLTR.