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
Published inLecture Notes in Computer Science
© 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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ConferenceTransactions on Computational Collective Intelligence
Is part of publicationTransactions on Computational Collective Intelligencev XXVI
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