Supporting Competitive Intelligence with Linked Enterprise Data

Vitor Afonso Pinto, Guilherme Sousa Bastos, Fabricio Ziviani, Fernando Silva Parreiras


Competitive Intelligence is a process which involves retrieving, analyzing and packaging information to offer a final product that responds to the intelligence needs of a particular decision maker or community of decision makers. Internet-based information sources are becoming increasingly important in this process because most of the contents available on the Web are available free of charge. In this work the following research question was addressed: What are the concepts and technologies related to linked data which allow gathering, integration and sharing of information to support competitive intelligence? To answer this question, firstly, the literature was reviewed in order to outline the conceptual framework. Next, some competency questions were defined through a focus group in a study object. Finally, DB4Trading tool was built as a prototype able to validate the conceptual framework. Results point out that adoption of Semantic Web technologies enable to obtain the data needed for the analysis of external environments. Besides that, results indicate that companies use Semantic Web technologies to support its operations despite consider these technologies as complex. This work adds to the decision-making process, specially in the context of competitive intelligence. This work also contributes to reducing costs to obtain information beyond organization boundaries by using Semantic Web technologies.


  1. Abrahams, B., McGrath, G. M., and Dai, W. (2004). A multi-agent approach for dynamic ontology loading to support semantic web applications. In Zhang, D., Grgoire, r., and DeGroot, D., editors, IRI, pages 570- 575. IEEE Systems, Man, and Cybernetics Society.
  2. Allemang, D. (2010). Semantic web and the linked data enterprise. In Wood, D., editor, Linking Enterprise Data, pages 3-23. Springer US.
  3. Berners-Lee, T. (2009). Linked data. http:// Accessed: 2014-03-10.
  4. Bouthillier, F. and Shearer, K. (2003). Assessing Competitive Intelligence Software: A Guide to Evaluating CI Technology. Information Today.
  5. Chen, H., Chau, M., and Zeng, D. (2002). fCIg spider: a tool for competitive intelligence on the web. Decision Support Systems, 34(1):1 - 17.
  6. Chen, H. and Wu, Z. (2003). On case-based knowledge sharing in semantic web. In ICTAI, pages 200-207. IEEE Computer Society.
  7. Chen, T., Zhang, Y., Zhang, S., Chen, C., and Chen, H. (2012). Building semantic information search platform with extended sesame framework. In Proceedings of the 8th International Conference on Semantic Systems, I-SEMANTICS 7812, pages 193-196, New York, NY, USA. ACM.
  8. Cronin, B., Overfelt, K., Fouchereaux, K., Manzvanzvike, T., Cha, M., and Sona, E. (1994). The internet and competitive intelligence: A survey of current practice. International Journal of Information Management, 14(3):204 - 222.
  9. Cyganiak, R. (2012). Accessing relational databases as virtual rdf graphs. Accessed: 2014- 10-01.
  10. Eisenberg, V. and Kanza, Y. (2011). Ruby on semantic web. In Abiteboul, S., Bhm, K., Koch, C., and Tan, K.-L., editors, ICDE, pages 1324-1327. IEEE Computer Society.
  11. Feridun, M. and Tanner, A. (2010). Using linked data for systems management. In NOMS, pages 926-929. IEEE.
  12. Ferrara, E., Meo, P. D., Fiumara, G., and Baumgartner, R. (2012). Web data extraction, applications and techniques: A survey. CoRR, abs/1207.0246.
  13. Gelb, B. D., Saxton, M. J., Zinkhan, G. M., and Albers, N. D. (1991). Competitive intelligence: Insights from executives. Business Horizons, 34(1):43 - 47.
  14. Hu, B. and Svensson, G. (2010). A case study of linked enterprise data. In Patel-Schneider, P. F., Pan, Y., Hitzler, P., Mika, P., 0007, L. Z., Pan, J. Z., Horrocks, I., and Glimm, B., editors, International Semantic Web Conference (2), volume 6497 of Lecture Notes in Computer Science, pages 129-144. Springer.
  15. Hyland, S., Haugen, A. S., and yvind Thomassen (2014). Perceptions of time spent on safety tasks in surgical operations: A focus group study. Safety Science, 70(0):70 - 79.
  16. Khan, A. and Hussain, M. (2009). Application of semantic web in e-business and telecommunication. In Advanced Computer Control, 2009. ICACC 7809. International Conference on, pages 513-517.
  17. Kitchenham, B. A. (2004). Procedures for undertaking systematic reviews. awangenh/kitchenham.pdf. Accessed: 2013-09-01.
  18. Li, W. (2002). Intelligent information agent with ontology on the semantic web. In Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on, volume 2, pages 1501-1504 vol.2.
  19. Lindström, A., Johnson, P., Johansson, E., Ekstedt, M., and Simonsson, M. (2006). A survey on cio concerns-do enterprise architecture frameworks support them? Information Systems Frontiers, 8(2):81-90.
  20. Mi, J., Chen, H., Lu, B., Yu, T., and Pan, G. (2009). Deriving similarity graphs from open linked data on semantic web. In IRI, pages 157-162. IEEE Systems, Man, and Cybernetics Society.
  21. Moresi, E. (2006). Memoria Organizacional e Gestao do Conhecimento. In Tarapanoff, K., editor, Inteligencia, Informacao e Conhecimento, pages 277-301. IBICT.
  22. Nettleton, D. (2014). Chapter 3 - incorporating various sources of data and information. In Nettleton, D., editor, Commercial Data Mining, pages 17 - 47. Morgan Kaufmann, Boston.
  23. Niles, C. and Jeremijenko, N. (2001). Collated path: A onedimensional interface element to promote user orientation and sense-making activities in the semantic web. In IV, pages 555-562. IEEE Computer Society.
  24. PAN, J. (2009). Resource description framework. In Staab, S. and Studer, R., editors, Handbook on Ontologies. Springer, second edition.
  25. Passant, A., Laublet, P., Breslin, J. G., and Decker, S. (2009). Semslates: Improving enterprise 2.0 information systems using semantic web technologies. In CollaborateCom, pages 1-10. IEEE.
  26. Tarapanoff, K. (2006). Informacao, Conhecimento e Inteligencia em Corporacoes: relacoes e complementaridade. In Tarapanoff, K., editor, Inteligencia, Informacao e Conhecimento, pages 19-36. IBICT.
  27. Xu, J., Zhu, Q., Li, J.-Z., Zhang, P., and Wang, K. (2004). Modeling and implementation of unified semantic web platform. In Web Intelligence, pages 603-606. IEEE Computer Society.
  28. Yang, Y., Akers, L., Klose, T., and Barcelonyang, C. (2008). Text mining and visualization tools Impressions of emerging capabilities. World Patent Information, 30(4):280-293.

Paper Citation

in Harvard Style

Afonso Pinto V., Sousa Bastos G., Ziviani F. and Silva Parreiras F. (2015). Supporting Competitive Intelligence with Linked Enterprise Data . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 409-415. DOI: 10.5220/0005472604090415

in Bibtex Style

author={Vitor Afonso Pinto and Guilherme Sousa Bastos and Fabricio Ziviani and Fernando Silva Parreiras},
title={Supporting Competitive Intelligence with Linked Enterprise Data},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Supporting Competitive Intelligence with Linked Enterprise Data
SN - 978-989-758-096-3
AU - Afonso Pinto V.
AU - Sousa Bastos G.
AU - Ziviani F.
AU - Silva Parreiras F.
PY - 2015
SP - 409
EP - 415
DO - 10.5220/0005472604090415