Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification: From Ranking to Binary Classification

Ruud van Bakel; Teodor Aleksiev; Daniel Daza; Dimitrios Alivanistos; Michael Cochez

doi:10.1007/978-3-030-72308-8_8

Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification: From Ranking to Binary Classification

Ruud van Bakel, Teodor Aleksiev, Daniel Daza, Dimitrios Alivanistos, Michael Cochez

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.

Original language	Undefined/Unknown
Title of host publication	Graph Structures for Knowledge Representation and Reasoning
Editors	Michael Cochez, Madalina Croitoru, Pierre Marquis, Sebastian Rudolph
Place of Publication	Cham
Publisher	Springer International Publishing
Pages	107-124
Number of pages	18
ISBN (Print)	978-3-030-72308-8
DOIs	https://doi.org/10.1007/978-3-030-72308-8_8
State	Published - Sep 1 2021
Externally published	Yes
Event	6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020 - Virtual, Online Duration: Sep 5 2020 → Sep 5 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12640 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020
City	Virtual, Online
Period	09/5/20 → 09/5/20

Keywords

Approximation
Box embeddings
Geometric representation
Query answering

Access to Document

10.1007/978-3-030-72308-8_8

Cite this

van Bakel, R., Aleksiev, T., Daza, D., Alivanistos, D., & Cochez, M. (2021). Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification: From Ranking to Binary Classification. In M. Cochez, M. Croitoru, P. Marquis, & S. Rudolph (Eds.), Graph Structures for Knowledge Representation and Reasoning (pp. 107-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12640 LNAI). Springer International Publishing. https://doi.org/10.1007/978-3-030-72308-8_8

van Bakel, Ruud ; Aleksiev, Teodor ; Daza, Daniel et al. / Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification : From Ranking to Binary Classification. Graph Structures for Knowledge Representation and Reasoning. editor / Michael Cochez ; Madalina Croitoru ; Pierre Marquis ; Sebastian Rudolph. Cham : Springer International Publishing, 2021. pp. 107-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification: From Ranking to Binary Classification",

abstract = "Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.",

keywords = "Approximation, Box embeddings, Geometric representation, Query answering",

author = "\{van Bakel\}, Ruud and Teodor Aleksiev and Daniel Daza and Dimitrios Alivanistos and Michael Cochez",

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doi = "10.1007/978-3-030-72308-8\_8",

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isbn = "978-3-030-72308-8",

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van Bakel, R, Aleksiev, T, Daza, D, Alivanistos, D & Cochez, M 2021, Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification: From Ranking to Binary Classification. in M Cochez, M Croitoru, P Marquis & S Rudolph (eds), Graph Structures for Knowledge Representation and Reasoning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12640 LNAI, Springer International Publishing, Cham, pp. 107-124, 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020, Virtual, Online, 09/5/20. https://doi.org/10.1007/978-3-030-72308-8_8

Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification: From Ranking to Binary Classification. / van Bakel, Ruud; Aleksiev, Teodor; Daza, Daniel et al.
Graph Structures for Knowledge Representation and Reasoning. ed. / Michael Cochez; Madalina Croitoru; Pierre Marquis; Sebastian Rudolph. Cham: Springer International Publishing, 2021. p. 107-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12640 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification

T2 - 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020

AU - van Bakel, Ruud

AU - Aleksiev, Teodor

AU - Daza, Daniel

AU - Alivanistos, Dimitrios

AU - Cochez, Michael

PY - 2021/9/1

Y1 - 2021/9/1

N2 - Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.

AB - Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.

KW - Approximation

KW - Box embeddings

KW - Geometric representation

KW - Query answering

UR - https://www.scopus.com/pages/publications/85103519903

U2 - 10.1007/978-3-030-72308-8_8

DO - 10.1007/978-3-030-72308-8_8

M3 - Contribución a la conferencia

SN - 978-3-030-72308-8

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 107

EP - 124

BT - Graph Structures for Knowledge Representation and Reasoning

A2 - Cochez, Michael

A2 - Croitoru, Madalina

A2 - Marquis, Pierre

A2 - Rudolph, Sebastian

PB - Springer International Publishing

CY - Cham

Y2 - 5 September 2020 through 5 September 2020

ER -

van Bakel R, Aleksiev T, Daza D, Alivanistos D, Cochez M. Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification: From Ranking to Binary Classification. In Cochez M, Croitoru M, Marquis P, Rudolph S, editors, Graph Structures for Knowledge Representation and Reasoning. Cham: Springer International Publishing. 2021. p. 107-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-72308-8_8

Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification: From Ranking to Binary Classification

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

DL: ICAI Discovery Lab

Cite this

Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification: From Ranking to Binary Classification

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Projects

DL: ICAI Discovery Lab

Cite this