Title: Automated subject indexing using word embeddings and controlled vocabularies: a comparative study

Authors: Michalis Sfakakis; Leonidas Papachristopoulos; Kyriaki Zoutsou; Christos Papatheodorou; Giannis Tsakonas

Addresses: Department of Archives, Library Science and Museology, Ionian University, Corfu, Greece ' Department of Archives, Library Science and Museology, Ionian University, Corfu, Greece; Hellenic Open University Distance Library and Information Centre, Patras, Greece ' Department of History and Philosophy of Science, National and Kapodistrian University of Athens, Athens, Greece ' Department of History and Philosophy of Science, National and Kapodistrian University of Athens, Athens, Greece ' Library and Information Centre, University of Patras, Patras, Greece

Abstract: Text mining methods contribute significantly to the understanding and the management of digital content, increasing the potential of entry links. This paper introduces a method for subject analysis combining topic modelling and automated labelling of the generated topics exploiting terms from existing knowledge organisation systems. A testbed was developed in which the Latent Dirichlet Allocation (LDA) algorithm was deployed for modelling the topics of a corpus of papers related to the Digital Library Evaluation domain. The generated topics were represented in the form of bags-of-words word embeddings and were utilised for retrieving terms from the EuroVoc Thesaurus and the Computer Science Ontology (CSO). The results of this study show that the domain of DL can be described with different vocabularies, but during the process of automatic labelling the context needs to be taken into account.

Keywords: subject indexing; similarity measures; text classification; machine learning; word embedding.

DOI: 10.1504/IJMSO.2021.125884

International Journal of Metadata, Semantics and Ontologies, 2021 Vol.15 No.4, pp.233 - 243

Received: 31 Mar 2021
Accepted: 14 Jan 2022
Published online: 03 Oct 2022 *

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