Large Scale Knowledge Matching with Balanced Efficiency-Effectiveness Using LSH Forest
Cochez, M., Terziyan, V., & Ermolayev, V. (2017). Large Scale Knowledge Matching with Balanced Efficiency-Effectiveness Using LSH Forest. In N. T. Nguyen, R. Kowalczyk, A. M. Pinto, & J. Cardoso (Eds.), Transactions on Computational Collective Intelligencev XXVI (pp. 46-66). Springer. Lecture Notes in Computer Science, 10190. https://doi.org/10.1007/978-3-319-59268-8_3
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© Springer International Publishing AG 2017. 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 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. Further, we show experimentally
that for our type of data minhashing works better than random hyperplane
hashing. This paper is an extension of the paper “Balanced Large
Scale Knowledge Matching Using LSH Forest” presented at the International
Keystone Conference 2015.
...


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