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dc.contributor.authorCochez, Michael
dc.contributor.authorTerziyan, Vagan
dc.contributor.authorErmolayev, Vadim
dc.date.accessioned2016-02-03T07:21:06Z
dc.date.available2017-01-07T22:45:08Z
dc.date.issued2015
dc.identifier.citationCochez, 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.), <em>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</em> (pp. 36-50). Lecture Notes in Computer Science (9398). Springer International Publishing. <a href="http://dx.doi.org/10.1007/978-3-319-27932-9_4">doi:10.1007/978-3-319-27932-9_4</a>
dc.identifier.otherTUTKAID_69028
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/48578
dc.description.abstractEvolving 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.
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofSemantic 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
dc.relation.ispartofseriesLecture Notes in Computer Science;9398
dc.subject.otherevolving knowledge ecosystems
dc.subject.otherlocality-sensitive hashing
dc.subject.otherLSH forest
dc.subject.otherbig data
dc.titleBalanced Large Scale Knowledge Matching Using LSH Forest
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201602021401
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineTietotekniikka
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2016-02-02T16:15:02Z
dc.type.coarconference paper
dc.description.reviewstatuspeerReviewed
dc.format.pagerange36-50
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.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.
dc.rights.accesslevelopenAccessfi
dc.relation.doi10.1007/978-3-319-27932-9_4


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