Entity Identification Problem in Big and Open Data

J. G. Enríquez, Vivian Lee, Masatomo Goto, F. J. Domínguez-Mayo, M. J. Escalona

2015

Abstract

Big and Open Data provide great opportunities to businesses to enhance their competitive advantages if utilized properly. However, during past few years’ research in Big and Open Data process, we have encountered big challenge in entity identification reconciliation, when trying to establish accurate relationships between entities from different data sources. In this paper, we present our innovative Intelligent Reconciliation Platform and Virtual Graphs solution that addresses this issue. With this solution, we are able to efficiently extract Big and Open Data from heterogeneous source, and integrate them into a common analysable format. Further enhanced with the Virtual Graphs technology, entity identification reconciliation is processed dynamically to produce more accurate result at system runtime. Moreover, we believe that our technology can be applied to a wide diversity of entity identification problems in several domains, e.g., e- Health, cultural heritage, and company identities in financial world.

References

  1. Manyika, J., Chui, M., Brown, B. 2011. Big data: The next frontier for innovation, competition, and productivity.
  2. Official websites of government: data.gov and data.gov.uk. Last check November 2014.
  3. Berners-Lee. July 2006. Linked Data - Design Issues.
  4. Berners-Lee. 2005. Uniform Resource Identifier (URI): Generic Syntax.
  5. Fielding, 1999. R. Hypertext Transfer Protocol.
  6. Mondal, J., Deshpande. 2012. Managing large dynamic graphs efficiently. A. pp. 145-156.
  7. Powell, J., Shadbolt. S.N. March 2014. Creating Value with Identifiers in an Open Data World. Open Data Institute and Thomson Reuters.
  8. Maali, F., Cyganiak, R., Peristeras. V. 2011. Entity Reconciliation Against LOD Hubs.
  9. Insights on Data Integration Methodologies. ESSnet-ISAD workshop, Vienna, 29-30 May 2008.
  10. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z. 2007. DBpedia: A Nucleus for a Web of Open Data. pp 722-735.
Download


Paper Citation


in Harvard Style

G. Enríquez J., Lee V., Goto M., J. Domínguez-Mayo F. and J. Escalona M. (2015). Entity Identification Problem in Big and Open Data . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 404-408. DOI: 10.5220/0005470704040408


in Bibtex Style

@conference{iceis15,
author={J. G. Enríquez and Vivian Lee and Masatomo Goto and F. J. Domínguez-Mayo and M. J. Escalona},
title={Entity Identification Problem in Big and Open Data},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2015},
pages={404-408},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005470704040408},
isbn={978-989-758-096-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Entity Identification Problem in Big and Open Data
SN - 978-989-758-096-3
AU - G. Enríquez J.
AU - Lee V.
AU - Goto M.
AU - J. Domínguez-Mayo F.
AU - J. Escalona M.
PY - 2015
SP - 404
EP - 408
DO - 10.5220/0005470704040408