Learning to Assess Linked Data Relationships Using Genetic Programming

Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico (2016). Learning to Assess Linked Data Relationships Using Genetic Programming. In: The Semantic Web – ISWC 2016 Proceedings, Part 1 (Groth, Paul; Simperl, Elena; Gray, Alasdair; Sabou, Marta; Krötzsch, Markus; Lecue, Freddy; Flöck, Fabian and Gil, Yolanda eds.), Lecture Notes in Computer Science, Springer, pp. 581–597.

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


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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