Weaving the Threads of Bibliographic Ontologies
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出版日期 | 16 Jun 2025 |
內容 | Bibliographic ontologies are crucial to make the most of networked library metadata, but they show interoperability limitations in the Semantic Web. Following a research study on the subject, this paper presents a possible solution to such limitations by means of a reference ontology (RO) intended to allow integration of different ontologies without imposing a common central one and to overcome limitations of mapping techniques, such as crosswalks and application profiles, most used in interconnecting bibliographic ontologies. Interoperability issues of Resource Description and Access (RDA) and Bibliographic Framework Initiative—BIBFRAME (BF) ontologies are addressed using real-world examples from the Library of Congress (LoC) and Biblioteca Nacional de España (BNE) datasets. For a proof of concept of the RO, this paper is focused on two specific interoperability problems that are not solvable with the usual data transformative techniques: misalignments concerning the definition and representation of Work and Expression classes; and the absence of formalization of properties essential to whole-part relationships, namely transitivity, nonreflexivity and asymmetry. The potential of the RO for solving such problem examples is demonstrated by making in-depth use of Resource Description Framework Schema/Web Ontology Language (RDFS/OWL) semantic reasoning and inference mechanisms, combined with Shapes Constraint Language (SHACL), when restrictions are needed to impose data constraints and validation. The RO innovation consists in the formulation of an independent high-level ontology, through which the elements of different source-ontologies are interlinked without being modified or replaced, but rather preserved, and in using semantic mechanisms to generate additional elements needed to consistently describe the relationship between them. |
刊名 | Information Technology and Libraries |
卷期 | Volume 44, No. 2, 2025 |
頁數 | 35 |
關鍵字 | reference ontology, linked data |
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