[2308.06585] Approximate Answering of Graph Queries
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Computer Science > Machine Learning

arXiv:2308.06585 (cs)
[Submitted on 12 Aug 2023]

Title:Approximate Answering of Graph Queries

Authors:Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, Hongyu Ren
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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.
Comments: Preprint of Ch. 17 "Approximate Answering of Graph Queries" in "Compendium of Neurosymbolic Artificial Intelligence", this https URL
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Databases (cs.DB); Logic in Computer Science (cs.LO); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2308.06585 [cs.LG]
  (or arXiv:2308.06585v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2308.06585

Submission history

From: Pasquale Minervini [view email]
[v1] Sat, 12 Aug 2023 14:47:21 UTC (94 KB)
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