Search (3 results, page 1 of 1)

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Abstract

Semantic mapping between controlled vocabulary and keywords is the first step towards knowledge-based subject access. This study reports the preliminary result of a semantic mapping experiment for the Gateway to Educational Materials (GEM). A total of 3,555 keywords were mapped with 322 concept names in the GEM controlled vocabulary. The preliminary test to 10,000 metadata records presented widely varied sets of results between the mapped and non-mapped data. The paper discussed linguistic and technical problems encountered in the mapping process and raised issues in the representation technologies and methods, which will lead to future study of knowledge-based access to networked information resources.

Source

Subject retrieval in a networked environment: Proceedings of the IFLA Satellite Meeting held in Dublin, OH, 14-16 August 2001 and sponsored by the IFLA Classification and Indexing Section, the IFLA Information Technology Section and OCLC. Ed.: I.C. McIlwaine

Type

a

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Abstract

Artificial intelligence (AI) is a multidisciplinary subject, typically studied as a research area within Computer Science. AI study aims at achieving a good understanding of the nature of intelligence and building intelligent agents which are computational systems demonstrating intelligent behavior. AI has been developed over more than 50 years. The topics studied in AI are quite broad, ranging from knowledge representation and reasoning, knowledge-based systems, machine learning and data mining, natural language processing, to search, image processing, robotics, and intelligent information systems. Numerous successful AI systems have been deployed in real-life applications in engineering, finance, science, health care, education, and service sectors. AI research has also significantly impacted the subject area of Library and Information Science (LIS), helping to develop smart Web search engines, personalized news filters, and knowledge-sharing and indexing systems. This entry briefly outlines the main topics studied in AI, samples some typical successful AI applications, and discusses the cross-fertilization between AI and LIS.

Type

a

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Abstract

This study proposes and explores a natural language processing- (NLP) based strategy to address out-ofdictionary and vocabulary mismatch problems in query translation based English-Chinese Cross-Language Information Retrieval (EC-CLIR). The strategy, named the LKB approach, is to construct a lexical knowledge base (LKB) and to use it for query translation. In this article, the author describes the LKB construction process, which customizes available translation resources based an the document collection of the EC-CLIR system. The evaluation shows that the LKB approach is very promising. It consistently increased the percentage of correct translations and decreased the percentage of missing translations in addition to effectively detecting the vocabulary gap between the document collection and the translation resource of the system. The comparative analysis of the top EC-CLIR results using the LKB and two other translation resources demonstrates that the LKB approach has produced significant improvement in EC-CLIR performance compared to performance using the original translation resource without customization. It has also achieved the same level of performance as a sophisticated machine translation system. The study concludes that the LKB approach has the potential to be an empirical model for developing real-world CLIR systems. Linguistic knowledge and NLP techniques, if appropriately used, can improve the effectiveness of English-Chinese crosslanguage information retrieval.

Type

a