Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm

Marcel Gehrke; Tanya Braun; Ralf Möller

doi:10.1007/978-3-030-03991-2_50

Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm

Marcel Gehrke, Tanya Braun, Ralf Möller

Institut für Informationssysteme

1 Zitat (Scopus)

Abstract

The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. We extend LDJT to answer conjunctive queries over multiple time steps by avoiding eliminations, while keeping the complexity to answer a conjunctive query low. The extended version of saves computations compared to an existing approach to answer multiple lifted conjunctive queries.

Originalsprache	Englisch
Titel	AI 2018: Advances in Artificial Intelligence
Redakteure/-innen	Tanja Mitrovic, Bing Xue, Xiaodong Li
Seitenumfang	13
Band	11320
Erscheinungsort	Cham
Herausgeber (Verlag)	Springer International Publishing
Erscheinungsdatum	10.11.2018
Seiten	543-555
ISBN (Print)	978-3-030-03990-5
ISBN (elektronisch)	978-3-030-03991-2
DOIs	https://doi.org/10.1007/978-3-030-03991-2_50
Publikationsstatus	Veröffentlicht - 10.11.2018
Veranstaltung	31st Australasian Joint Conference on Artificial Intelligence - Wellington, Niger Dauer: 11.12.2018 → 14.12.2018 https://ecs.victoria.ac.nz/Events/AI2018/WebHome#gallery

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10.1007/978-3-030-03991-2_50

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Gehrke, M., Braun, T., & Möller, R. (2018). Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm. in T. Mitrovic, B. Xue, & X. Li (Hrsg.), AI 2018: Advances in Artificial Intelligence (Band 11320, S. 543-555). ( Lecture Notes in Computer Science; Band 11320). Springer International Publishing. https://doi.org/10.1007/978-3-030-03991-2_50

@inproceedings{7af42893faa5449f8a01cbb2d7ef7e48,

title = "Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm",

abstract = "The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. We extend LDJT to answer conjunctive queries over multiple time steps by avoiding eliminations, while keeping the complexity to answer a conjunctive query low. The extended version of saves computations compared to an existing approach to answer multiple lifted conjunctive queries.",

author = "Marcel Gehrke and Tanya Braun and Ralf M{\"o}ller",

year = "2018",

month = nov,

day = "10",

doi = "10.1007/978-3-030-03991-2\_50",

language = "English",

isbn = "978-3-030-03990-5",

volume = "11320",

series = " Lecture Notes in Computer Science",

publisher = "Springer International Publishing",

pages = "543--555",

editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li",

booktitle = "AI 2018: Advances in Artificial Intelligence",

note = "31st Australasian Joint Conference on Artificial Intelligence<br/>, AI 2018 ; Conference date: 11-12-2018 Through 14-12-2018",

url = "https://ecs.victoria.ac.nz/Events/AI2018/WebHome\#gallery",

}

Gehrke, M, Braun, T & Möller, R 2018, Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm. in T Mitrovic, B Xue & X Li (Hrsg.), AI 2018: Advances in Artificial Intelligence. Bd. 11320, Lecture Notes in Computer Science, Bd. 11320, Springer International Publishing, Cham, S. 543-555, 31st Australasian Joint Conference on Artificial Intelligence
, Wellington, Niger, 11.12.18. https://doi.org/10.1007/978-3-030-03991-2_50

Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm. / Gehrke, Marcel; Braun, Tanya; Möller, Ralf.
AI 2018: Advances in Artificial Intelligence. Hrsg. / Tanja Mitrovic; Bing Xue; Xiaodong Li. Band 11320 Cham: Springer International Publishing, 2018. S. 543-555 ( Lecture Notes in Computer Science; Band 11320).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm

AU - Gehrke, Marcel

AU - Braun, Tanya

AU - Möller, Ralf

PY - 2018/11/10

Y1 - 2018/11/10

N2 - The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. We extend LDJT to answer conjunctive queries over multiple time steps by avoiding eliminations, while keeping the complexity to answer a conjunctive query low. The extended version of saves computations compared to an existing approach to answer multiple lifted conjunctive queries.

AB - The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. We extend LDJT to answer conjunctive queries over multiple time steps by avoiding eliminations, while keeping the complexity to answer a conjunctive query low. The extended version of saves computations compared to an existing approach to answer multiple lifted conjunctive queries.

U2 - 10.1007/978-3-030-03991-2_50

DO - 10.1007/978-3-030-03991-2_50

M3 - Conference contribution

SN - 978-3-030-03990-5

VL - 11320

T3 - Lecture Notes in Computer Science

SP - 543

EP - 555

BT - AI 2018: Advances in Artificial Intelligence

A2 - Mitrovic, Tanja

A2 - Xue, Bing

A2 - Li, Xiaodong

PB - Springer International Publishing

CY - Cham

T2 - 31st Australasian Joint Conference on Artificial Intelligence<br/>

Y2 - 11 December 2018 through 14 December 2018

ER -

Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm

Abstract

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