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Balanced Large Scale Knowledge Matching Using LSH Forest

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Cochez, M., Terziyan, V., & Ermolayev, V. (2015). Balanced Large Scale Knowledge Matching Using LSH Forest. In J. Cardoso, F. Guerra, G.-J. Houben, A. M. Pinto, & Y. Velegrakis (Eds.), Semantic Keyword-based Search on Structured Data Sources : First COST Action IC1302 International KEYSTONE Conference, IKC 2015, Coimbra, Portugal, September 8-9, 2015. Revised Selected Papers (pp. 36-50). Lecture Notes in Computer Science (9398). Springer International Publishing. doi:10.1007/978-3-319-27932-9_4
Published in
Lecture Notes in Computer Science;9398
Authors
Cochez, Michael |
Terziyan, Vagan |
Ermolayev, Vadim
Date
2015
Discipline
Tietotekniikka
Copyright
© Springer International Publishing Switzerland 2015. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.

 
Evolving Knowledge Ecosystems were proposed recently to approach the Big Data challenge, following the hypothesis that knowledge evolves in a way similar to biological systems. Therefore, the inner working of the knowledge ecosystem can be spotted from natural evolution. An evolving knowledge ecosystem consists of Knowledge Organisms, which form a representation of the knowledge, and the environment in which they reside. The environment consists of contexts, which are composed of so-called knowledge tokens. These tokens are ontological fragments extracted from information tokens, in turn, which originate from the streams of information flowing into the ecosystem. In this article we investigate the use of LSH Forest (a self-tuning indexing schema based on locality-sensitive hashing) for solving the problem of placing new knowledge tokens in the right contexts of the environment. We argue and show experimentally that LSH Forest possesses required properties and could be used for large distributed set-ups. ...
Publisher
Springer International Publishing
Is part of publication
Semantic Keyword-based Search on Structured Data Sources : First COST Action IC1302 International KEYSTONE Conference, IKC 2015, Coimbra, Portugal, September 8-9, 2015. Revised Selected Papers, ISBN 978-3-319-27931-2
ISSN Search the Publication Forum
0302-9743
Keywords
evolving knowledge ecosystems locality-sensitive hashing LSH forest big data
DOI
10.1007/978-3-319-27932-9_4
URI

http://urn.fi/URN:NBN:fi:jyu-201602021401

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