A Hybrid Genetic-Bootstrapping Approach to Link Resources in the Web of Data

Abstract

In the Web of Data, real-world entities are represented by means of resources, for instance the southern Spanish city “Seville” that is represented by means of the resource that is available at http://es.dbpedia.org/page/Sevilla in the DBpedia dataset. Link rules are intended to link resources that are different, but represent the same real-world entities; for instance the resource that is available at https://www.wikidata.org/wiki/Q8717 represents exactly the same real-world entity as the resource aforementioned. A link rule may establish that two resources that represent cities should be linked as long as the GPS coordinates are the same. Such rules are then paramount to integrating web data, because otherwise programs would deal with every resource independently from the other. Knowing that the previous resources rep resent the same real-world entity allows them to merge the information that they provide independently (which is commonly known as integrat ing link data). State-of-the-art link rules are learnt by genetic program ming systems and build on comparing the values of the attributes of the resources. Unfortunately, this approach falls short in cases in which resources have similar values for their attributes, but represent different real-world entities. In this paper, we present a proposal that hybridises a genetic programming system that learns link rules and an ad-hoc filter ing technique that bootstraps them to decide whether the links that they produce must be selected or not. Our analysis of the literature reveals that our approach is novel and our experimental analysis confirms that it helps improve the F1 score, which is defined in the literature as the harmonic mean of precision and recall, by increasing precision without a significant penalty on recall.