Balanced Large Scale Knowledge Matching Using LSH Forest
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;9398Date
2015Discipline
TietotekniikkaCopyright
© 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 us
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