Linked data for cross-disciplinary collaboration cohort discovery

Myers, Trina, Trevathan, Jarrod, Madden, Dianna, and O'Neill, Tristan (2013) Linked data for cross-disciplinary collaboration cohort discovery. In: CEUR Workshop Proceedings (1057) From: ISWC: 12th International Semantic Web Conference and LD4IE: 1st workshop on Linked Data for Information Extraction, 21-25 October 2013, Sydney, NSW, Australia.

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Cross-disciplinary collaborations potentially offer the diversity of understanding required to answer complex problems. However, barriers to cohort discovery exist because content about people is predominantly only in human-readable form on websites and/or in disparate databases. Notably, many cross-disciplinary collaborations never form due to a lack of awareness of cross-boundary synergies. This project applies semantic technologies to automate linkages to reveal hidden connections between people from metadata parameters about data, rather than from publication products. The information in metadata, commonly used for data discovery, can be used to link researchers for potential partnerships. The proposed system combines pre-existing and custom ontologies, populated from a number of accessible repositories, to describe the relationships between researchers based on metadata parameters. The system was tested from the researcher's perspective where significant alignments with potential partners were found based on transitive relationships, similar interests (e.g., research fields) and/or other commonalities (e.g., location/time of research).

Item ID: 29757
Item Type: Conference Item (Research - E1)
ISSN: 1613-0073
Keywords: Semantic Web, ontologies, collaborative research, knowledge systems
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Proceedings of the First International Workshop on Linked Data for Information Extraction (LD4IE 2013).

Projects and Grants: JCU FLBCA ECR Grant
Date Deposited: 23 Jan 2014 01:48
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 100%
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