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AYNEC: All You Need for Evaluating Completion Techniques in Knowledge Graphs

  • Daniel AyalaEmail author
  • Agustín Borrego
  • Inma Hernández
  • Carlos R. Rivero
  • David Ruiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11503)

Abstract

The popularity of knowledge graphs has led to the development of techniques to refine them and increase their quality. One of the main refinement tasks is completion (also known as link prediction for knowledge graphs), which seeks to add missing triples to the graph, usually by classifying potential ones as true or false. While there is a wide variety of graph completion techniques, there is no standard evaluation setup, so each proposal is evaluated using different datasets and metrics. In this paper we present AYNEC, a suite for the evaluation of knowledge graph completion techniques that covers the entire evaluation workflow. It includes a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of graphs, the splitting into training and testing examples, and the generation of negative examples. AYNEC also provides a visual summary of the graph and the optional exportation of the datasets in an open format for their visualisation. We use AYNEC to generate a library of datasets ready to use for evaluation purposes based on several popular knowledge graphs. Finally, it includes a tool that computes relevant metrics and uses significance tests to compare each pair of techniques. These open source tools, along with the datasets, are freely available to the research community and will be maintained.

Keywords

Knowledge graph Graph refinement Evaluation Datasets 

Notes

Acknowledgements

Our work was supported the Spanish R&D&I programme by grant TIN2016-75394-R. We would also like to thank Prof. Dr. José Luis Ruiz-Reina, head of the Computer Science and Artificial Intelligence Department at the University of Seville, who kindly provided us with the invaluable resources that helped us in our research.

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Authors and Affiliations

  • Daniel Ayala
    • 1
    Email author
  • Agustín Borrego
    • 1
  • Inma Hernández
    • 1
  • Carlos R. Rivero
    • 2
  • David Ruiz
    • 1
  1. 1.University of SevilleSevilleSpain
  2. 2.Rochester Institute of TechnologyRochesterUSA

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