**arXiv ID:** 2308.06585 **Authors:** Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, Hongyu Ren **Published:** 2023-08-12T14:47:21Z **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 w...
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# Approximate Answering of Graph Queries
**arXiv ID:** 2308.06585
**Authors:** Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, Hongyu Ren
**Published:** 2023-08-12T14:47:21Z
**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.
## Skill Description
This skill is generated from the arXiv paper: Approximate Answering of Graph Queries (2308.06585).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:2308.06585](http://arxiv.org/abs/2308.06585v1)
