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Concept Discovery in Graph Databases

A Case Study with Neo4j

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Hybrid Artificial Intelligent Systems (HAIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10334))

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Abstract

Concept discovery is one of the most commonly addressed tasks of multi-relational data mining and is concerned with inducing logical definitions of a relation in terms of other relations provided. The problem has long been studied from Inductive Logic Programming and graph-oriented perspectives. In this study, we investigate the problem from graph databases perspective and propose a pathfinding-based method for concept discovery in graph databases. More specifically, we introduce a method that employs Neo4j graph database technology to store data and find the concept descriptors that define the target relation and implements several techniques to further improve the post processing steps of concept discovery process. The experimental results show that the proposed method is superior to state-of-the art concept discovery systems in terms of rule induction time, discovers shorter concept descriptors with high coverage and F1 score, and scales well.

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Authors and Affiliations

  1. Department of Computer Engineering, Kocaeli University, Kocaeli, Turkey

    Furkan Goz & Alev Mutlu

Authors
  1. Furkan Goz
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  2. Alev Mutlu
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Correspondence to Alev Mutlu .

Editor information

Editors and Affiliations

  1. University of La Rioja , Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  2. University of La Rioja , Logroño, La Rioja, Spain

    Rubén Urraca

  3. University of A Coruña , Ferrol, La Coruña, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Goz, F., Mutlu, A. (2017). Concept Discovery in Graph Databases. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-59650-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59649-5

  • Online ISBN: 978-3-319-59650-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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