Learning domain labels using conceptual fingerprints: An in-use case study in the neurology domain

Zubair Afzal, George Tsatsaronis, Marius Doornenbal, Pascal Coupet, Michelle Gregory

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Modelling a science domain for the purposes of thematically categorizing the research work and enabling better browsing and search can be a daunting task, especially if a specialized taxonomy or ontology does not exist for this domain. Elsevier, the largest academic publisher, faces this challenge often, for the needs of supporting the journals submission system, but also for supplying ScienceDirect and Scopus, two flagship platforms of the company, with sufficient metadata, such as conceptual labels that characterize the research works, which can improve the user experience in browsing and searching the literature. In this paper we describe an Elsevier in-use case study of learning appropriate domain labels from a collection of 6, 357 full text articles in the neurology domain, exploring different document representations and clustering mechanisms. Besides the baseline approaches for document representation (e.g., bag-of-words) and their variations (e.g., n-grams), we employ a novel in-house methodology which produces conceptual fingerprints of the research articles, starting from a general domain taxonomy, such as the Medical Subject Headings (MeSH). A thorough empirical evaluation is presented, using a variety of clustering mechanisms and several validity indices to evaluate the resulting clusters. Our results summarize the best practices in modelling this specific domain and we report on the advantages and disadvantages of using the different clustering mechanisms and document representations that were examined, with the aim to learn appropriate conceptual labels for this domain.

Original languageEnglish
Title of host publicationKnowledge Engineering and Knowledge Management - 20th International Conference, EKAW 2016, Proceedings
EditorsPaolo Ciancarini, Francesco Poggi, Fabio Vitali, Eva Blomqvist
PublisherSpringer Verlag
Pages731-745
Number of pages15
ISBN (Print)9783319490038
DOIs
StatePublished - Jan 1 2016
Event20th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2016 - Bologna, Italy
Duration: Nov 19 2016Nov 23 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10024 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2016
CountryItaly
CityBologna
Period11/19/1611/23/16

Keywords

  • Best practices
  • Clustering evaluation
  • Conceptual fingerprints
  • Document clustering
  • Document labeling
  • Domain taxonomy
  • Neurology domain

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  • Cite this

    Afzal, Z., Tsatsaronis, G., Doornenbal, M., Coupet, P., & Gregory, M. (2016). Learning domain labels using conceptual fingerprints: An in-use case study in the neurology domain. In P. Ciancarini, F. Poggi, F. Vitali, & E. Blomqvist (Eds.), Knowledge Engineering and Knowledge Management - 20th International Conference, EKAW 2016, Proceedings (pp. 731-745). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10024 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-49004-5_47