Approximate answering of graph queries
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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Approximate answering of graph queries. / Cochez, Michael; Alivanistos, Dimitrios; Arakelyan, Erik; Berrendorf, Max; Daza, Daniel; Galkin, Mikhail; Minervini, Pasquale; Niepert, Mathias; Ren, Hongyu.
Compendium of Neurosymbolic Artificial Intelligence. IOS Press, 2023. p. 373-386 (Frontiers in Artificial Intelligence and Applications, Vol. 369).Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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TY - CHAP
T1 - Approximate answering of graph queries
AU - Cochez, Michael
AU - Alivanistos, Dimitrios
AU - Arakelyan, Erik
AU - Berrendorf, Max
AU - Daza, Daniel
AU - Galkin, Mikhail
AU - Minervini, Pasquale
AU - Niepert, Mathias
AU - Ren, Hongyu
N1 - Publisher Copyright: © 2023 The authors and IOS Press. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.3233/FAIA230149
DO - 10.3233/FAIA230149
M3 - Book chapter
AN - SCOPUS:85172821943
SN - 9781643684062
T3 - Frontiers in Artificial Intelligence and Applications
SP - 373
EP - 386
BT - Compendium of Neurosymbolic Artificial Intelligence
PB - IOS Press
ER -
ID: 391745468