A4 Article in conference proceedings
Balanced Large Scale Knowledge Matching Using LSH Forest (2015)


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). Springer International Publishing. Lecture Notes in Computer Science, 9398. https://doi.org/10.1007/978-3-319-27932-9_4


JYU authors or editors


Publication details

All authors or editorsCochez, Michael; Terziyan, Vagan; Ermolayev, Vadim

Parent publicationSemantic 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

Parent publication editorsCardoso, Jorge; Guerra, Francesco; Houben, Geert-Jan; Pinto, Alexandre Miguel; Velegrakis, Yannis

Conference:

  • International keystone conference

ISBN978-3-319-27931-2

Journal or seriesLecture Notes in Computer Science

ISSN0302-9743

Publication year2015

Number in series9398

Pages range36-50

PublisherSpringer International Publishing

Publication countrySwitzerland

Publication languageEnglish

DOIhttps://doi.org/10.1007/978-3-319-27932-9_4

Publication open accessNot open

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/48578


Abstract

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.
See presentation slides: https://ai.it.jyu.fi/IKC-2015.pptx


Keywordsbig data

Free keywordsevolving knowledge ecosystems; locality-sensitive hashing; LSH forest


Contributing organizations

Other organizations:


Ministry reportingYes

Reporting Year2015

JUFO rating0


Last updated on 2024-27-03 at 13:08