지능적인 웹 검색을 위한 의미적 문서 태깅 방법 연구

Abstract

Nowadays, the fast advance of digital technologies and the current Web environment have been accelerating the field of information retrieval and processing. The Internet space using the Web is not strange any more to most people and they can obtain any information desired from the Web. These changes have spawned a great deal of research aiming at enhancing service and convenience. Thus, many computer science researchers are committed to finding more useful and efficient methods to provide appropriate results to meet users' needs. Among those, the methods of this dissertation have been studied for semantic document tagging to realize Semantic Web as an ultimate purpose.

Semantic Web is a very important technique aiming at processing and understanding the information spread on the Web and subsequently providing semantic and exact retrieval results. To realize Semantic Web, this research concentrates on tagging methods of text documents. The amount of the texts is increasing according to trend of Web 2.0 and it is the most frequently utilized communication medium to express and share information between people. Therefore, the text retrieval is important and this research proposes tagging methods of Web documents to provide standardized, systematic and semantic retrieval.

The previous works on Web document tagging generally choose core words from a document itself. However, the core words are not standardized taggers so, in retrieving, users should make an effort to grasp the tagger words first. To improve the point, this research contains methods to utilize titles (Wiki concept) of Wikipedia documents and to find the best Wiki concept which describes the Web documents (target documents). In addition to these methods, the research tries to classify target documents into Wikipedia category (Wiki category) for semantic document interconnections.

In order to use Wiki categories and concepts for classifying and tagging target documents, the research extracts context information from Wiki concepts, Wiki categories and target documents and finds the nearest Wiki categories and concepts of target documents through similarity measure. Experimenting diverse cases, it was confirmed that this research can provide semantic classification and tagging methods and that the context information of documents has much potentiality to be applied to various works for Semantic Web. By the way, it is worth noting that some future works, which can give semantics to proper nouns and technical terms, need to be done.