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

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Hybrid Artificial Intelligent Systems (HAIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10870))

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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 represent the same real-world entity allows them to merge the information that they provide independently (which is commonly known as integrating link data). State-of-the-art link rules are learnt by genetic programming 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 filtering 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 \(F_1\) score, which is defined in the literature as the harmonic mean of precision and recall, by increasing precision without a significant penalty on recall.

Supported by the Spanish R&D programme (grants TIN2013-40848-R and TIN2013-40848-R). The computing facilities were provided by the Andalusian Scientific Computing Centre (CICA). We are grateful to Dr. Carlos R. Rivero and Dr. David Ruiz for earlier ideas that led to the results in this paper. We also thank Dr. Francisco Herrera for his hints on statistical analyses and sharing his software with us.

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Notes

  1. 1.

    The datasets are available at https://goo.gl/asvKQV.

  2. 2.

    The prototype is available at https://github.com/AndreaCimminoArriaga/Teide.

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Authors and Affiliations

  1. University of Seville, ETSI Informática, Avda. Reina Mercedes, s/n, Sevilla, Spain

    Andrea Cimmino & Rafael Corchuelo

Authors
  1. Andrea Cimmino
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  2. Rafael Corchuelo
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Corresponding authors

Correspondence to Andrea Cimmino or Rafael Corchuelo .

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Editors and Affiliations

  1. Department of Mine Operating and Prospection, University of Oviedo, Oviedo, Spain

    Francisco Javier de Cos Juez

  2. Department of Computer Science, University of Oviedo, Oviedo, Spain

    José Ramón Villar

  3. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of A Coruña, A Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    José António Sáez

  7. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Cimmino, A., Corchuelo, R. (2018). A Hybrid Genetic-Bootstrapping Approach to Link Resources in the Web of Data. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-92639-1_13

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