Learning to assess linked data relationships using genetic programming

Ilaria Tiddi*, Mathieu d’Aquin, Enrico Motta

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


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.

Original languageEnglish
Title of host publicationThe Semantic Web - 15th International Semantic Web Conference, ISWC 2016, Proceedings
EditorsMarkus Krotzsch, Freddy Lecue, Freddy Lecue, Elena Simperl, Alasdair Gray, Fabian Flock, Yolanda Gil, Paul Groth, Marta Sabou
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783319465227
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event15th International Semantic Web Conference, ISWC 2016 - Kobe, Japan
Duration: 17 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9981 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Semantic Web Conference, ISWC 2016


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