Semantic-Based Information Retrieval for Content Management and Security

An information retrieval system has to retrieve all and only those documents that are relevant to a user query, even if index terms and query terms are not matched exactly. However, term mismatches between index terms and query terms have been a serious obstacle to the enhancement of retrieval performance. In this article, we discuss automatic term normalization between words and phrases in text corpora and their application to a Korean information retrieval system. We perform three new types of term normalizations: transliterated word normalization, noun phrase normalization, and context-based term normalization. Transliterated words are normalized into equivalence classes by using contextual similarity to alleviate lexical term mismatches. Then, noun phrases are normalized into phrasal terms by segmenting compound nouns as well as normalizing noun phrases. Moreover, context-based terms are normalized by using a combination of mutual information and word context to establish word similarities. Next, unsupervised clustering is done by using the K-means algorithm and cooccurrence clusters are identified to alleviate semantic term mismatches. These term normalizations are used in both the indexing and the retrieval system. The experimental results show that our proposed system can alleviate three types of term mismatches and can also provide the appropriate similarity measurements. As a result, our system can improve the retrieval effectiveness of the information retrieval system.