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Balanced Large Scale Knowledge Matching Using LSH Forest

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9398)


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.


  • Evolving knowledge ecosystems
  • Locality-sensitive hashing
  • LSH forest
  • Big data

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The authors would like to thank the department of Mathematical Information Technology of the University of Jyväskylä for financially supporting this research. This research is also in part financed by the N4S SHOK organized by Digile Oy and financially supported by TEKES. The authors would further like to thank Steeri Oy for supporting the research and the members of the Industrial Ontologies Group (IOG) of the University of Jyväskylä for their support in the research. Further, it has to be mentioned that the implementation of the software was greatly simplified by the Guava library by Google, the Apache Commons Math\(^\mathrm{TM}\) library, and the Rabin hash library by Bill Dwyer and Ian Brandt.

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Correspondence to Michael Cochez .

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Cochez, M., Terziyan, V., Ermolayev, V. (2015). Balanced Large Scale Knowledge Matching Using LSH Forest. In: Cardoso, J., Guerra, F., Houben, GJ., Pinto, A.M., Velegrakis, Y. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2015. Lecture Notes in Computer Science(), vol 9398. Springer, Cham.

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