Approximate answering of graph queries

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

Standard

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. s. 373-386 (Frontiers in Artificial Intelligence and Applications, Bind 369).

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

Harvard

Cochez, M, Alivanistos, D, Arakelyan, E, Berrendorf, M, Daza, D, Galkin, M, Minervini, P, Niepert, M & Ren, H 2023, Approximate answering of graph queries. i Compendium of Neurosymbolic Artificial Intelligence. IOS Press, Frontiers in Artificial Intelligence and Applications, bind 369, s. 373-386. https://doi.org/10.3233/FAIA230149

APA

Cochez, M., Alivanistos, D., Arakelyan, E., Berrendorf, M., Daza, D., Galkin, M., Minervini, P., Niepert, M., & Ren, H. (2023). Approximate answering of graph queries. I Compendium of Neurosymbolic Artificial Intelligence (s. 373-386). IOS Press. Frontiers in Artificial Intelligence and Applications Bind 369 https://doi.org/10.3233/FAIA230149

Vancouver

Cochez M, Alivanistos D, Arakelyan E, Berrendorf M, Daza D, Galkin M o.a. Approximate answering of graph queries. I Compendium of Neurosymbolic Artificial Intelligence. IOS Press. 2023. s. 373-386. (Frontiers in Artificial Intelligence and Applications, Bind 369). https://doi.org/10.3233/FAIA230149

Author

Cochez, Michael ; Alivanistos, Dimitrios ; Arakelyan, Erik ; Berrendorf, Max ; Daza, Daniel ; Galkin, Mikhail ; Minervini, Pasquale ; Niepert, Mathias ; Ren, Hongyu. / Approximate answering of graph queries. Compendium of Neurosymbolic Artificial Intelligence. IOS Press, 2023. s. 373-386 (Frontiers in Artificial Intelligence and Applications, Bind 369).

Bibtex

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title = "Approximate answering of graph queries",
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.",
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",
note = "Publisher Copyright: {\textcopyright} 2023 The authors and IOS Press. All rights reserved.",
year = "2023",
doi = "10.3233/FAIA230149",
language = "English",
isbn = "9781643684062",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "373--386",
booktitle = "Compendium of Neurosymbolic Artificial Intelligence",
address = "United States",

}

RIS

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