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Structuring Linked Data Search Results Using Probabilistic Soft Logic Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Learning to Assess Linked Data Relationships Using Genetic Programming

Authors : Ilaria Tiddi, Mathieu d’Aquin, Enrico Motta

Published in: The Semantic Web – ISWC 2016

Publisher: Springer International Publishing

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Abstract

The goal of this work is to learn a measure supporting the detection of strong relationships between Linked Data entities. Such relationships can be represented as paths of entities and properties, and can be obtained through a blind graph search process traversing Linked Data. The challenge here is therefore the design of a cost-function that is able to detect the strongest relationship between two given entities, by objectively assessing the value of a given path. To achieve this, we use a Genetic Programming approach in a supervised learning method to generate path evaluation functions that compare well with human evaluations. We show how such a cost-function can be generated only using basic topological features of the nodes of the paths as they are being traversed (i.e. without knowledge of the whole graph), and how it can be improved through introducing a very small amount of knowledge about the vocabularies of the properties that connect nodes in the graph.

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previous chapter Integrating Medical Scientific Knowledge with the Semantically Quantified Self
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Footnotes
1
Retrieving a representation of a resource identified by a URI.
 
6
Dataset and results are available online at http://​linkedu.​eu/​dedalo/​pathfinding/​.
 
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Metadata
Title
Learning to Assess Linked Data Relationships Using Genetic Programming
Authors
Ilaria Tiddi
Mathieu d’Aquin
Enrico Motta
Copyright Year
2016
Book
The Semantic Web – ISWC 2016

Print ISBN: 978-3-319-46522-7

Electronic ISBN: 978-3-319-46523-4

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46523-4_35

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