Approximate answering of graph queries
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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.
Original language | English |
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Title of host publication | Compendium of Neurosymbolic Artificial Intelligence |
Number of pages | 14 |
Publisher | IOS Press |
Publication date | 2023 |
Pages | 373-386 |
Chapter | 17 |
ISBN (Print) | 9781643684062 |
ISBN (Electronic) | 9781643684079 |
DOIs | |
Publication status | Published - 2023 |
Series | Frontiers in Artificial Intelligence and Applications |
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Volume | 369 |
ISSN | 0922-6389 |
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© 2023 The authors and IOS Press. All rights reserved.
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