Abstract
A knowledge network is a generic structure that organises distributed knowledge into a system that will allow it to be efficiently retrieved. The primary features of this network are its lightweight autonomous framework. The framework allows for smaller components such as pervasive sensors to operate. Stigmergy is thus the preferred method to allow the network to self-organise and maintain itself. To be able to return knowledge, the network must be able to reason over its stored information. As part of the query process, links can be stigmergically created between related sources to allow for query optimisation. This has been proven to be an effective and lightweight way to optimise. These links may also contain useful information for providing knowledge. This paper considers a number of possibilities for using these links to return knowledge through a distributed lightweight reasoning engine, thus upholding the main features of the network.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baumgarten, M., Bicocchi, N., Curran, K., Mamei, M., Mulvenna, M.D., Nugent, C., Zambonelli, F.: Towards Self-Organizing Knowledge Networks for Smart World Infrastructures. In: Invited Session on Service Development and Provisioning through Situated and Autonomic Communications at International Conference on Self-Organization and Autonomous Systems in Computing and Communications (SOAS 2006), Erfurt, Germany, (18-21 September, 2006)
Dorigo, M., Birattari, M., Stutzle, T.: Ant Colony Optimization - Artificial Ants as a Computational Intelligence Technique. IEEE Computational Intelligence Magazine (2006)
Koloniari, G., Petrakis, Y., Pitoura, E., Tsotsos, T.: Query workload-aware overlay construction using histograms. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 640–647 (2005)
Liu, H., Singh, P.: ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal 22 (2004)
Mulvenna, M.D., Zambonelli, F., Curran, K., Nugent, C.D.: Knowledge Networks. In: Sutcliffe, G., Voronkov, A. (eds.) LPAR 2005. LNCS (LNAI), vol. 3835, pp. 99–114. Springer, Heidelberg (2005)
Raschid, L., Wu, Y., Lee, W.-J., Vidal, M.-E., Tsaparas, P., Srinivasan, P., Sehgal, A.K.: Ranking Target Objects of Navigational Queries. In: 8th ACM International Workshop on Web Information and Data Management WIDM 2006, pp. 27–34 (2006)
Ramos, V., Abraham, A.: Evolving a Stigmergic Self-Organized DataMining. In: IADIS, editor, IADIS-04, International Conference on Web Based Communities (2004)
Ricci, A., Omicini, A., Viroli, M., Gardelli, L., Oliva, E.: Cognitive Stigmergy: A Framework Based on Agents and Artifacts. In: The Third International Workshop on Environments for Multiagent Systems (E4MAS) (2006)
Serugendo, G.D.M., Gleizes, M.P., Karageorgos, A.: Self-Organisation and Emergence in MAS: An Overview. Informatica 30, 45–54 (2006)
Izquierdo-Torres, E.: Collective Intelligence in Multi-Agent Robotics: Stigmergy, Self-Organization and Evolution (2004) (last accessed 7/4/07), citeseer.ist.psu.edu/izquierdo-torres04collective.html
Vidal, M.-E., Raschid, L., Mestre, J.: Challenges in Selecting Paths for Navigational Queries: Trade-off of Benefit of Path versus Cost of Plan. In: Seventh International Workshop on the Web and Databases (WebDB 2004), 61–66 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Greer, K., Baumgarten, M., Mulvenna, M., Nugent, C., Curran, K. (2007). Knowledge-Based Reasoning Through Stigmergic Linking. In: Hutchison, D., Katz, R.H. (eds) Self-Organizing Systems. IWSOS 2007. Lecture Notes in Computer Science, vol 4725. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74917-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-74917-2_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74916-5
Online ISBN: 978-3-540-74917-2
eBook Packages: Computer ScienceComputer Science (R0)