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  • × author_ss:"Daquino, M."
  • × year_i:[2020 TO 2030}
  1. Daquino, M.: ¬A computational analysis of art historical linked data for assessing authoritativeness of attributions (2020) 0.01 
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.7, S.757-769
  2. Daquino, M.; Peroni, S.; Shotton, D.; Colavizza, G.; Ghavimi, B.; Lauscher, A.; Mayr, P.; Romanello, M.; Zumstein, P.: ¬The OpenCitations Data Model (2020) 0.01 
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    Abstract
    A variety of schemas and ontologies are currently used for the machine-readable description of bibliographic entities and citations. This diversity, and the reuse of the same ontology terms with different nuances, generates inconsistencies in data. Adoption of a single data model would facilitate data integration tasks regardless of the data supplier or context application. In this paper we present the OpenCitations Data Model (OCDM), a generic data model for describing bibliographic entities and citations, developed using Semantic Web technologies. We also evaluate the effective reusability of OCDM according to ontology evaluation practices, mention existing users of OCDM, and discuss the use and impact of OCDM in the wider open science community.

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