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

Institut für Informationssysteme

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

Strategische Forschungsbereiche und Zentren

Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
Querschnittsbereich: Intelligente Systeme

DFG-Fachsystematik

4.43-01 Theoretische Informatik

UN SDGs

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

http://www.ifis.uni-luebeck.de/uploads/tx_wapublications/ai2018_con_public.pdf

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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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UN SDGs

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