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Abstract

Automated subject classification has been a challenging research issue for many years now, receiving particular attention in the past decade due to rapid increase of digital documents. The most frequent approach to automated classification is machine learning. It, however, requires training documents and performs well on new documents only if these are similar enough to the former. We explore a string-matching algorithm based on a controlled vocabulary, which does not require training documents - instead it reuses the intellectual work put into creating the controlled vocabulary. Terms from the Engineering Information thesaurus and classification scheme were matched against title and abstract of engineering papers from the Compendex database. Simple string-matching was enhanced by several methods such as term weighting schemes and cut-offs, exclusion of certain terms, and en- richment of the controlled vocabulary with automatically extracted terms. The best results are 76% recall when the controlled vocabulary is enriched with new terms, and 79% precision when certain terms are excluded. Precision of individual classes is up to 98%. These results are comparable to state-of-the-art machine-learning algorithms.

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Abstract

Under the auspices of the Desire II project, researchers at NetLab and OCLC are providing searching and browsing of a test collection of engineering documents on the Web. The goal of the project is to explore simple methods of automatic classification to provide subject browsing of a robot-generated engineering index. At NetLab the documents are automatically classified and organized using an engineering-specific scheme, the Engineering Index (Ei) Thesaurus and Classification; at OCLC the Dewey Decimal Classification (DDC), a general knowledge organization scheme, is being used

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Abstract

Electronic information sources are being collected at the Lund University Library within the areas of computer science, Internet use and environmental studies. Within each area there are several different types of sources e.g. the environment area has bibliographic information, journal content pages, a local directory database on environmental related research projects and an archive of articles from relevant electronic conferences. A seed-bank database is planned in collaboration with the Nordic Gene Bank. The popularity of the wide area information server is growing and there are several hundred available sources today, however, improvements need to be done with the possibilities to select sources and in the search and relevance-ranking algorithms

Source

Opportunity 2000: understanding and serving users in an electronic library; 15th Int. Symp., 12.-15.10.1992; Festschrift in honour of Herbert S. White. Ed.: A.H. Helal

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