Authoris: a tool for authority control in the semantic web

• Published in: arXiv.org 2014-02
• Main Authors: Leiva-Mederos, Amed, Senso, Jose A, Dominguez-Velasco, Sandor, Hipola, Pedro
• Format: Article
• Language: English
• Subjects: Algorithms; Authority control; Bibliographies; Data exchange; Linked Data; Machine learning; Obsolescence; Semantic web; Semantics; Software
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1402.2019

Purpose: The purpose of this paper is to propose a tool that generates authority files to be integrated with linked data by means of learning rules. AUTHORIS is software developed to enhance authority control and information exchange among bibliographic and non-bibliographic entities. Design / methodology / approach: The article analyzes different methods previously developed for authority control as well as IFLA and ALA standards for managing bibliographic records. Semantic Web technologies are also evaluated. AUTHORIS relies on Drupal and incorporates the protocols of Dublin Core, SIOC, SKOS and FOAF. The tool has also taken into account the obsolescence of MARC and its substitution by FRBR and RDA. Its effectiveness was evaluated applying a learning test proposed by RDA. Over 80 percent of the actions were carried out correctly. Findings: The use of learning rules and the facilities of linked data make it easier for information organizations to reutilize products for authority control and distribute them in a fair and efficient manner. Research limitations / implications: The ISAD-G records were the ones presenting most errors. EAD was found to be second in the number of errors produced. The rest of the formats --MARC 21, Dublin Core, FRAD, RDF, OWL, XBRL and FOAF-- showed fewer than 20 errors in total. Practical implications: AUTHORIS offers institutions the means of sharing data with a high level of stability, helping to detect records that are duplicated and contributing to lexical disambiguation and data enrichment. Originality / value: The software combines the facilities of linked data, the potency of the algorithms for converting bibliographic data, and the precision of learning rules.