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Abstract

Documents in digital and paper libraries may be arranged, based on their topics, in order to facilitate browsing. It may seem intuitively obvious that ordering documents by their subject should improve browsing performance; the results presented in this article suggest that ordering library materials by their Gray code values and through using links consistent with the small world model of document relationships is consistent with improving browsing performance. Below, library circulation data, including ordering with Library of Congress Classification numbers and Library of Congress Subject Headings, are used to provide information useful in generating user-centered document arrangements, as well as user-independent arrangements. Documents may be linearly arranged so they can be placed in a line by topic, such as on a library shelf, or in a list on a computer display. Crossover links, jumps between a document and another document to which it is not adjacent, can be used in library databases to allow additional paths that one might take when browsing. The improvement that is obtained with different combinations of document orderings and different crossovers is examined and applications suggested.

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Abstract

Relationships between terms and features are an essential component of thesauri, ontologies, and a range of controlled vocabularies. In this article, we describe ways to identify important concepts in documents using the relationships in a thesaurus or other vocabulary structures. We introduce a methodology for the analysis and modeling of the indexing process based on a weighted random walk algorithm. The primary goal of this research is the analysis of the contribution of thesaurus structure to the indexing process. The resulting models are evaluated in the context of automatic subject indexing using four collections of documents pre-indexed with 4 different thesauri (AGROVOC [UN Food and Agriculture Organization], high-energy physics taxonomy [HEP], National Agricultural Library Thesaurus [NALT], and medical subject headings [MeSH]). We also introduce a thesaurus-centric matching algorithm intended to improve the quality of candidate concepts. In all cases, the weighted random walk improves automatic indexing performance over matching alone with an increase in average precision (AP) of 9% for HEP, 11% for MeSH, 35% for NALT, and 37% for AGROVOC. The results of the analysis support our hypothesis that subject indexing is in part a browsing process, and that using the vocabulary and its structure in a thesaurus contributes to the indexing process. The amount that the vocabulary structure contributes was found to differ among the 4 thesauri, possibly due to the vocabulary used in the corresponding thesauri and the structural relationships between the terms. Each of the thesauri and the manual indexing associated with it is characterized using the methods developed here.