Classification of scientific publications according to library controlled vocabularies: A new concept matching-based approach

Abstract

Purpose

This paper aims to report on the design and development of a new approach for automatic classification and subject indexing of research documents in scientific digital libraries and repositories (DLR) according to library controlled vocabularies such as DDC and FAST.

Design/methodology/approach

The proposed concept matching-based approach (CMA) detects key Wikipedia concepts occurring in a document and searches the OPACs of conventional libraries via querying the WorldCat database to retrieve a set of MARC records which share one or more of the detected key concepts. Then the semantic similarity of each retrieved MARC record to the document is measured and, using an inference algorithm, the DDC classes and FAST subjects of those MARC records which have the highest similarity to the document are assigned to it.

Findings

The performance of the proposed method in terms of the accuracy of the DDC classes and FAST subjects automatically assigned to a set of research documents is evaluated using standard information retrieval measures of precision, recall, and F1. The authors demonstrate the superiority of the proposed approach in terms of accuracy performance in comparison to a similar system currently deployed in a large scale scientific search engine.

Originality/value

The proposed approach enables the development of a new type of subject classification system for DLR, and addresses some of the problems similar systems suffer from, such as the problem of imbalanced training data encountered by machine learning-based systems, and the problem of word-sense ambiguity encountered by string matching-based systems.

Citation

Joorabchi, A. and E. Mahdi, A. (2013), "Classification of scientific publications according to library controlled vocabularies: A new concept matching-based approach", Library Hi Tech, Vol. 31 No. 4, pp. 725-747. https://doi.org/10.1108/LHT-03-2013-0030