Approximate Answering of Graph Queries

Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza +4 more
8/12/2023
cs.LGcs.AIcs.DB

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Abstract

Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and evolves, making existing facts deprecated or introducing new ones. However, we would still want to be able to answer queries as if the graph were complete. In this chapter, we will give an overview of several methods which have been proposed to answer queries in such a setting. We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations. Then, we give an overview of the different approaches and describe them in terms of expressiveness, supported graph types, and inference capabilities.

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Cite this paper

@article{cochez2023approximate,
  title  = {Approximate Answering of Graph Queries},
  author = {Michael Cochez and Dimitrios Alivanistos and Erik Arakelyan and Max Berrendorf and Daniel Daza and Mikhail Galkin and Pasquale Minervini and Mathias Niepert and Hongyu Ren},
  year   = {2023},
  eprint = {2308.06585},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.06585v1}
}

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