Patterns and Logic for Reasoning with Networks View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Angelika Kimmig , Esther Galbrun , Hannu Toivonen , Luc De Raedt

ABSTRACT

Biomine and ProbLog are two frameworks to implement bisociative information networks (BisoNets). They combine structured data representations with probabilities expressing uncertainty. While Biomine is based on graphs, ProbLog’s core language is that of the logic programming language Prolog. This chapter provides an overview of important concepts, terminology, and reasoning tasks addressed in the two systems. It does so in an informal way, focusing on intuition rather than on mathematical definitions. It aims at bridging the gap between network representations and logical ones. More... »

PAGES

122-143

References to SciGraph publications

  • 2008. Logical and Relational Learning in NONE
  • 2008-03. Compressing probabilistic Prolog programs in MACHINE LEARNING
  • 2012. Finding Representative Nodes in Probabilistic Graphs in BISOCIATIVE KNOWLEDGE DISCOVERY
  • 2010. A Framework for Path-Oriented Network Simplification in ADVANCES IN INTELLIGENT DATA ANALYSIS IX
  • 2008-08. Finding reliable subgraphs from large probabilistic graphs in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2007. The Most Reliable Subgraph Problem in KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2007
  • 2010. Fast Discovery of Reliable k-terminal Subgraphs in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • 2012. From Information Networks to Bisociative Information Networks in BISOCIATIVE KNOWLEDGE DISCOVERY
  • 2011. Probabilistic Rule Learning in INDUCTIVE LOGIC PROGRAMMING
  • 2006. Link Discovery in Graphs Derived from Biological Databases in DATA INTEGRATION IN THE LIFE SCIENCES
  • 2007. Probabilistic Explanation Based Learning in MACHINE LEARNING: ECML 2007
  • Book

    TITLE

    Bisociative Knowledge Discovery

    ISBN

    978-3-642-31829-0
    978-3-642-31830-6

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-31830-6_9

    DOI

    http://dx.doi.org/10.1007/978-3-642-31830-6_9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1049140282


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