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Information Systems Frontiers

, Volume 15, Issue 2, pp 167–192 | Cite as

Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making

  • Naeem Khalid Janjua
  • Farookh Khadeer HussainEmail author
  • Omar Khadeer Hussain
Article

Abstract

The availability of integrated, high quality information is a pre-requisite for a decision support system (DSS) to aid in the decision-making process. The introduction of semantic web ensures the seamless integration of information derived from diverse sources and transforms the DSS to an adoptable and flexible Semantic Web-DSS (Web-DSS). However, due to the monotonic nature of the layered development of semantic web, it lacks the capability to represent, reason and integrate incomplete and conflicting information. This, in turn, renders an enterprise incapable of knowledge integration; that is, integration of information about a subject that could potentially be incomplete, inconsistent and distributed among different Web-DSS within or across enterprises. In this article, we address the issues of incomplete and inconsistent semantic information and knowledge integration by using argumentation and argumentation schemes. We discuss the Argumentation-enabled Information Integration Web-DSS (Web@IDSS) along with its syntax and semantics for semantic information integration, and devise a methodology for sharing the results of Web@IDSS in Argument Interchange Format (AIF) format. We also discuss Argumentation-enabled Knowledge Integration Web-DSS (Web@KIDSS) for semantic knowledge integration. We provide formal syntax and semantics for the Web@KIDSS, propose a conceptual framework, and describe it in detail. We present the algorithms for knowledge integration and the prototype application for validation of results.

Keywords

Semantic web Information integration Argumentation Argumentation schemes Web based DSS 

References

  1. Alaranta, M., & Henningsson S. (2008). An approach to analyzing and planning post-merger is integration: insights from two field studies. Information Systems Frontiers, 10, 307–319.CrossRefGoogle Scholar
  2. Antoniou, G., & Bikakis, A. (2007). Dr-prolog: a system for defeasible reasoning with rules and ontologies on the semantic web. IEEE Transactions on Knowledge and Data Engineering, 19(2), 233.CrossRefGoogle Scholar
  3. Antoniou, G., Baldoni, M., Bonatti, P.A., Nejdl, W., Olmedilla, D. (2004). Rule-based policy specification. In Yu, T., Jajodia, S. (Eds.), Secure data management in decentralized systems (pp. 169–216, Vol. 33). US: SpringerGoogle Scholar
  4. Antoniou, G., Damasio, C.V., Grosof, B., Horrocks, I., Kifer, M., Maluszynski, J., Patel-Schneider, P.F. (2005). Combining rules and ontologies: A survey. Tech. Rep. IST-2004-506779 REWERSE Deliverable I3-D3, Technical Report IST506779/Linköping/I3-D3/D/PU/a1. Linköping UniversityGoogle Scholar
  5. Ba, S., Lang, K.R., Whinston, A.B. (1997). Enterprise decision support using intranet technology. Decision Support Systems, 20(2), 99–134. doi: 10.1016/S0167-9236(96)00068-1, http://www.sciencedirect.com/science/article/pii/S0167923696000681.CrossRefGoogle Scholar
  6. Baroni, P., Fogli, D., Guida, G. (1998). Modeling argumentation in practical reasoning: a conceptual analysis of argument life cycle. In 7th international conference on information processing and management of uncertainty in knowledge-based systems (pp. 1790–1797). Paris.Google Scholar
  7. Bassiliades, N., Antoniou, G., Vlahavas, I. (2004). Dr-device: A defeasible logic system for the semantic web. Principles and practice of semantic web reasoning (pp 134–148).Google Scholar
  8. Benkö, T., Lukácsy, G., Fokt, A., Szeredi, P. (2003). Information integration through reasoning on meta-data. In AI moves to IA: Workshop on artificial intelligence, information access, and mobile computing.Google Scholar
  9. Berners-Lee, T. (2000). Semantic web - xml2000, W3C. http://www.w3.org/2000/Talks/1206-xml2k-tbl/. Accessed 1 March 2012
  10. Bex, F., Prakken, H., Reed, C. (2010). A formal analysis of the aif in terms of the aspic framework. In 3rd international conference on computational models of argument.Google Scholar
  11. Bhatt, M., Flahive, A., Wouters, C., Rahayu, W., Taniar, D. (2006). Move: a distributed framework for materialized ontology view extraction. Algorithmica, 45, 457–481. doi: 10.1007/s00453-006-1221-2, http://portal.acm.org/citation.cfm?id=1165166.1165175.CrossRefGoogle Scholar
  12. Buccella, A., Cechich, A., Fillottrani, P. (2009). Ontology-driven geographic information integration: a survey of current approaches. Computers & Geosciences 35(4), 710–723. Geoscience Knowledge Representation in Cyberinfrastructure.Google Scholar
  13. Carlsson, C, & Turban, E. (2002). Dss: directions for the next decade. Decision Support Systems, 33(2), 105–110.CrossRefGoogle Scholar
  14. Ceccaroni, L., Cortés, U., Sànchez-Marrè, M. (2004). Ontowedss: augmenting environmental decision-support systems with ontologies. Environmental Modelling & Software, 19(9), 785–797. Environmental Sciences and Artificial Intelligence.CrossRefGoogle Scholar
  15. Chesnevar, C., McGinnis, J., Modgil, S., Rahwan, I., Reed, C., Simari, G., South, M., Vreeswijk, G., Willmott, S. (2006a). Towards an argument interchange format. The Knowledge Engineering Review, 21(4), 293.CrossRefGoogle Scholar
  16. Chesnevar, C.I., Maguitman, A.G., Simari, G.R. (2006b). Argument-based critics and recommenders: a qualitative perspective on user support systems. Data & Knowledge Engineering, 59(2), 293.CrossRefGoogle Scholar
  17. Cheung, K., & Cheong, M.P. (2007). Intelligent on-line decision support tools for market operators. In International conference on intelligent systems applications to power systems, 2007 (pp. 1–6).Google Scholar
  18. Chua, W.W.K., & Goh, A.E.S. (2010). Techniques for discovering correspondences between ontologies. International Journal of Web and Grid Services Archive, 6(3), 213–243. doi: 10.1504/IJWGS.2010.035090.CrossRefGoogle Scholar
  19. Fan, X., Toni, F., Hussain, A. (2010). Two-agent conflict resolution with assumption-based argumentation. In Proceeding of the 2010 conference on computational models of argument: Proceedings of COMMA 2010 (pp. 231–242). Amsterdam, The Netherlands: IOS Press.Google Scholar
  20. Flahive, A., Rahayu, W., Taniar, D., Apduhan, B. (2005). A distributed ontology framework in the semantic grid environment. In 19th international conference on Advanced Information Networking and Applications, 2005. AINA 2005 (pp. 193–196, Vol. 2). doi: 10.1109/AINA.2005.19.
  21. Flahive, A., Taniar, D., Rahayu, W., Apduhan, B.O. (2009). Ontology tailoring in the semantic grid. Computer Standards & Interfaces, 31(5), 870–885. doi: 10.1016/j.csi.2008.03.016, http://www.sciencedirect.com/science/article/pii/S0920548908000330. Specification, Standards and Information Management for Distributed Systems.CrossRefGoogle Scholar
  22. Garcia, A.J., & Simari, G.R. (2004). Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming, 4(1+2), 95–138.CrossRefGoogle Scholar
  23. Garcia-Crespo, A., Ruiz-Mezcua, B., Lopez-Cuadrado, J.L., Gonzalez-Carrasco, I. (2011). Semantic model for knowledge representation in e-business. Knowledge-Based Systems, 24(2), 282–296. doi: 10.1016/j.knosys.2010.09.006.CrossRefGoogle Scholar
  24. Godden, D.J., & Walton, D. (2007) Advances in the theory of argumentation schemes and critical questions. Informal Logic, 27, 267–292.Google Scholar
  25. Grosof BN, Gandhe MD, Finin TW (2002) Sweetjess: Translating damlruleml to jess. In Proceedings of the international workshop on rule markup languages for business rules on the semantic web.Google Scholar
  26. Hurt, C.D. (1998). Nonmonotonic logic for use in information retrieval: an exploratory paper. Information processing & management, 34(1), 35.CrossRefGoogle Scholar
  27. Iyad Rahwan, C.R. (2009). The argument interchange format, argumentation in artifical intelligence. Springer.Google Scholar
  28. Janjua, N.K., & Hussain, F.K. (2011). Web@idss : argumentation-enabled web-based idss for reasoning over incomplete and conflicting information. Knowledge-Based Systems, 32, 9–27. doi: 10.1016/j.knosys.2011.09.009, http://www.sciencedirect.com/science/article/pii/S0950705111002103.CrossRefGoogle Scholar
  29. Kartha, N., & Novstrup, A. (2009). Ontology and rule based knowledge representation for situation management and decision support. In Mott, S., Buford, J.F., Jakobson, G., Mendenhall, J.M. (Eds.), Intelligent sensing, situation management, impact assessment, and cyber-sensing. SPIE.Google Scholar
  30. Katie Atkinson, T.B.C. (2008). Abstract argumentation scheme frameworks, artificial intelligence: Methodology, systems, and applications (Vol. 5253/2008). Berlin/Heidelberg: Springer.Google Scholar
  31. Kim, J., Kim, P., Chung, H. (2011). Ontology construction using online ontologies based on selection, mapping and merging. International Journal of Web and Grid Services, 7, 170–189.CrossRefGoogle Scholar
  32. Kontopoulos, E., Bassiliades, N., Antoniou, G. (2011). Visualizing semantic web proofs of defeasible logic in the DR-DEVICE system. Knowledge-Based Systems, 24(3), 406–419. doi: 10.1016/j.knosys.2010.12.001.CrossRefGoogle Scholar
  33. Lee, T.B. (2003). The semantic web and challenges. W3C. http://www.w3.org/2003/Talks/01-sweb-tbl/. Accessed 1 March 2012.
  34. Lee, T.B. (2005). Www 2005 keynote. W3C. http://www.w3.org/2005/Talks/0511-keynote-tbl/. Accessed 1 March 2012.
  35. Lee, T.B. (2006). Artificial intelligence and the semantic web: Aaai 2006 keynote. W3C. http://www.w3.org/2006/Talks/0718-aaai-tbl/Overview.html. Accessed 1 March 2012.
  36. Letia, I., & Groza, A. (2008). A planning-based approach for enacting world wide argument web. In Badica, C., Mangioni, G., Carchiolo, V., Burdescu, D. (Eds.), Intelligent distributed computing, systems and applications, studies in computational intelligence (pp. 137–146, Vol. 162). Berlin/Heidelberg: Springer.Google Scholar
  37. Liu, S., Duffy, A., Whitfield, R., Boyle, I. (2010). Integration of decision support systems to improve decision support performance. Knowledge and Information Systems, 22, 261–286.CrossRefGoogle Scholar
  38. Loui, R.P. (1998). Process and policy: Resource-bounded nondemonstrative reasoning. Computational intelligence, 14(1), 1.CrossRefGoogle Scholar
  39. March, S.T., & Hevner, A.R. (2007). Integrated decision support systems: a data warehousing perspective. Decision Support Systems, 43(3), 1031–1043. Integrated Decision Support.CrossRefGoogle Scholar
  40. Morge, M. (2008). The hedgehog and the fox: An argumentation-based decision support system. In: Proceedings of the 4th international conference on Argumentation in Multi-agent Systems, ArgMAS’07 (pp. 114–131). Berlin, Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
  41. Muthaiyah, S., & Kerschberg, L. (2007). Virtual organization security policies: an ontology-based integration approach. Information Systems Frontiers, 9, 505–514. doi: 10.1007/s10796-007-9050-7.CrossRefGoogle Scholar
  42. Nguyen, H.Q., Taniar, D., Rahayu, J.W., Nguyen, K. (2011). Double-layered schema integration of heterogeneous xml sources. Journal of Systems and Software, 84(1), 63–76. doi: 10.1016/j.jss.2010.07.055. Information Networking and Software Services.CrossRefGoogle Scholar
  43. Nicolicin-Georgescu, V., Benatier, V., Lehn, R., Briand, H. (2010). Ontology-based autonomic computing for decision support systems management: Shared ressources allocation between groups of data warehouses. In 2010 3rd international conference on Communication Theory, Reliability, and Quality of Service (CTRQ) (pp. 233–236). doi: 10.1109/CTRQ.2010.46.
  44. Norta, A., & Eshuis, R. (2010). Specification and verification of harmonized business-process collaborations. Information Systems Frontiers, 12, 457–479. doi: 10.1007/s10796-009-9164-1.CrossRefGoogle Scholar
  45. Noy, N.F. (2004). Semantic integration: a survey of ontology-based approaches. SIGMOD Record, 33, 65–70.CrossRefGoogle Scholar
  46. Osei-Bryson, K.M., & Ngwenyama, O. (2008). Decision models for information systems management. Information Systems Frontiers, 10, 277–279.CrossRefGoogle Scholar
  47. Palau, R.M., & Moens, M.F. (2009). Argumentation mining: the detection, classification and structure of arguments in text. In ICAIL ’09: Proceedings of the 12th international conference on artificial intelligence and law (pp. 98–107). New York, NY: ACM.Google Scholar
  48. Pesic, M., & van der Aalst, W. (2006). A declarative approach for flexible business processes management. In Eder, J., & Dustdar, S. (Eds.), Business process management workshops, lecture notes in computer science (Vol. 4103, pp 169–180). Berlin/Heidelberg: Springer. doi: 10.1007/11837862_18.CrossRefGoogle Scholar
  49. Power, D.J. (2002). Decision support systems: Concepts and resources for managers. Greenwood Publishing Group.Google Scholar
  50. Power, D.J., & Sharda, R. (2009). Decision support systems. In Nof, S.Y. (Ed.), Springer handbook of automation (pp. 1539–1548). Berlin/Heidelberg: Springer.Google Scholar
  51. Rahwan, I., Zablith, F., Reed, C. (2007a). Towards large scale argumentation support on the semantic web. In AAAI’07: Proceedings of the 22nd national conference on artificial intelligence (pp. 1446–1451). AAAI Press.Google Scholar
  52. Rahwan, I., Zablitha, F., Reed, C. (2007b). Laying the foundations for a world wide argument web. Artificial intelligence, 171(10–15), 897.CrossRefGoogle Scholar
  53. Salam, A. (2007). Design and implementation of semantic decision support system for supplier performance contract monitoring and execution: Integrating description logics, semantic web rules and service-oriented computing in the context of the extended enterprise. In Americas conference on information systems.Google Scholar
  54. Seng, J.L., & Kong, I.L. (2009). A schema and ontology-aided intelligent information integration. Expert Systems with Applications, 36, 10,538–10,550.CrossRefGoogle Scholar
  55. Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33(2), 111–126.CrossRefGoogle Scholar
  56. Silverman, B.G., Bachann, M., Al-Akharas, K. (2001). Implications of buyer decision theory for design of e-commerce websites. International Journal of Human-Computer Studies, 55(5), 815–844.CrossRefGoogle Scholar
  57. Suguri, H., Ahmad, H.F., Pasha, M., Khalid, N. (2008). Foundation for autonomous semantic grid. USA: Nova Science Publications.Google Scholar
  58. Toni, F. (2007). E-business in argugrid. In Veit, D., & Altmann, J. (Eds.), Grid economics and business models, lecture notes in computer science (Vol. 4685, pp. 164–169). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  59. Torroni, P., Gavanelli, M., Chesani, F. (2009). Arguing on the semantic grid. USA: Springer.Google Scholar
  60. Toulmin, S.E. (2003). The uses of argument. Cambridge University Press.Google Scholar
  61. Vahidov, R., Kersten, G.E. (2004). Decision station: situating decision support systems. Decision Support Systems, 38(2), 283–303.CrossRefGoogle Scholar
  62. Walton, D. (2009). Argumentation in artificial intelligence, chap argumentation theory: A very short introduction (pp. 1–24). Springer.Google Scholar
  63. Wang, H.J., Zhao, J.L., Zhang, L.J. (2009). Policy-driven process mapping (pdpm): discovering process models from business policies. Decision Support Systems, 48(1), 267–281. doi: 10.1016/j.dss.2009.08.006, http://www.sciencedirect.com/science/article/pii/S0167923609002012, Information Product Markets.CrossRefGoogle Scholar
  64. Xue, Y., Ghenniwa, H., Shen, W. (2009). Ontological view-driven semantic integration in collaborative networks. In Camarinha-Matos, L., Paraskakis, I., Afsarmanesh, H. (Eds.), Leveraging knowledge for innovation in collaborative networks, IFIP advances in information and communication technology (Vol. 307, pp. 311–318). Boston: Springer.Google Scholar
  65. Yang, X., Bo, Z., Bei, Z. (2009). Research on semantic decision support system. In WRI World congress on computer science and information engineering, 2009 (Vol. 5, pp. 687–691). doi: 10.1109/CSIE.2009.364.
  66. Zarefsky, D. (2009). Argumentation: The study of effective reasoning (2nd Edn., Vol. 2009). Northwestern University. URL: http://www.teach12.com/ttcx/CourseDescLong2.aspx?cid=4294.
  67. Zhou, J., Yang, H., Wang, M., Zhang, R., Yue, T., Zhang, S., Mo, R. (2010). A survey of semantic enterprise information integration. In: 2010 3rd International Conference on Information Sciences and Interaction Sciences (ICIS) (pp. 234–239).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Naeem Khalid Janjua
    • 1
  • Farookh Khadeer Hussain
    • 2
    Email author
  • Omar Khadeer Hussain
    • 1
  1. 1.School of Information Systems, Curtin Business SchoolCurtin UniversityPerthAustralia
  2. 2.Decision Support and e-Service Intelligence (DeSI) Lab, Quantum Computation and Intelligent Systems (QCIS), School of Software, Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoAustralia

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