Abstract
Semantic Routed Network (SRN) can provide a scalable distributed solution for searching data in a large grid. In SRN, messages are routed in a overlay network based on the meaning of the message key. If the message key describes the desired data, then SRN nodes can be addressed and accessed by the description of their data content. The key challenges of materializing a SRN are: (1) designing a data structure which will represent complex descriptions of data objects; (2) computing similarity of descriptors; and (3) constructing a small world network topology that minimize the routing response time and maximize routing success, which depends on solving the first two problems. We present a design of a descriptor data structure and a technique to compare their similarity to address the first two problems.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bergman, M.: The Deep Web: Surfacing Hidden Value, White Paper (2001)
SRS Server at EMBI-EBI, http://srs.ebi.ac.uk
Biswas, A., Mohan, S., Mahapatra, R.: Optimization of Semantic Routing Table. In: 17th International Conference on Computer Communications and Networks (2008)
Tempich, C., Staab, S., Wranik, A.: REMINDIN: Semantic Query Routing in Peer-to-Peer Networks Based on Social Metaphors. In: 13th Int. WWW Conf., pp. 640–649 (2004)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)
Yang, D., Powers, D.M.: Measuring semantic similarity in the taxonomy of WordNet. In: 28th Australasian Conference on Computer Science, vol. 38 (2005)
Hariri, B.H., Abolhassani, H., Khodaei, A.: A New Structural Similarity Measure for Ontology Alignment. In: Int. Conf. on Semantic Web & Web Services, pp. 36–42 (2006)
Lemaire, B., Denhière, G.: Incremental Construction of an Associative Network from a Corpus. In: 26th Annual Meeting of the Cognitive Science Society, pp. 825–830 (2004)
Rajapske, R., Denham, M.: Text retrieval with more realistic concept matching and reinforcement learning. Info. Processing and Management 42, 1260–1275 (2006)
Gene Ontology, http://www.geneontology.org/
Disease Ontology, http://diseaseontology.sourceforge.net/
Bottini, N., et al.: A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nature Genetics 36, 337–338 (2004)
Irgens, F.: Tensors. Continuum Mechanics. Springer, Heidelberg (2008)
Broder, A., Mitzenmacher, M.: Network applications of Bloom filters: A survey. Internet Mathematics 1(4), 485–509 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Biswas, A., Mohan, S., Panigrahy, J., Tripathy, A., Mahapatra, R. (2008). Representation of Complex Concepts for Semantic Routed Network. In: Garg, V., Wattenhofer, R., Kothapalli, K. (eds) Distributed Computing and Networking. ICDCN 2009. Lecture Notes in Computer Science, vol 5408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92295-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-92295-7_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92294-0
Online ISBN: 978-3-540-92295-7
eBook Packages: Computer ScienceComputer Science (R0)