Lifted Temporal Most Probable Explanation

Marcel Gehrke; Tanya Braun; Ralf Möller

doi:10.1007/978-3-030-23182-8_6

Lifted Temporal Most Probable Explanation

Marcel Gehrke^*, Tanya Braun, Ralf Möller

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Informationssysteme

3 Zitate (Scopus)

Abstract

The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Another type of query asks for a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJT^mpe to efficiently solve the temporal MPE problem for temporal probabilistic relational models and (ii) a combination of LDJT and LDJT^mpe to efficiently answer assignment queries for a given number of time steps.

Originalsprache	Englisch
Titel	ICCS 2019: Graph-Based Representation and Reasoning
Redakteure/-innen	Dominik Endres, Mehwish Alam, Diana Şotropa
Seitenumfang	14
Band	11530 LNAI
Herausgeber (Verlag)	Springer, Cham
Erscheinungsdatum	19.06.2019
Seiten	72-85
ISBN (Print)	978-3-030-23181-1
ISBN (elektronisch)	978-3-030-23182-8
DOIs	https://doi.org/10.1007/978-3-030-23182-8_6
Publikationsstatus	Veröffentlicht - 19.06.2019
Veranstaltung	24th International Conference on Conceptual Structures - Marburg, Deutschland Dauer: 01.07.2019 → 04.07.2019 Konferenznummer: 227759

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@inproceedings{0eefeb47a2384d94b9a35c5d198dc928,

title = "Lifted Temporal Most Probable Explanation",

abstract = "The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Another type of query asks for a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJTmpe to efficiently solve the temporal MPE problem for temporal probabilistic relational models and (ii) a combination of LDJT and LDJTmpe to efficiently answer assignment queries for a given number of time steps.",

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

note = "Funding Information: This research originated from the Big Data project being part of Joint Lab 1, funded by Cisco Systems Germany, at the centre COPICOH, University of L{\"u}beck. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 24th International Conference on Conceptual Structures , ICCS 2019 ; Conference date: 01-07-2019 Through 04-07-2019",

year = "2019",

month = jun,

day = "19",

doi = "10.1007/978-3-030-23182-8\_6",

language = "English",

isbn = "978-3-030-23181-1",

volume = "11530 LNAI",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Cham",

pages = "72--85",

editor = "Endres, \{Dominik \} and Alam, \{Mehwish \} and {\c S}otropa, \{Diana \}",

booktitle = "ICCS 2019: Graph-Based Representation and Reasoning",

}

Gehrke, M, Braun, T & Möller, R 2019, Lifted Temporal Most Probable Explanation. in D Endres, M Alam & D Şotropa (Hrsg.), ICCS 2019: Graph-Based Representation and Reasoning. Bd. 11530 LNAI, Lecture Notes in Computer Science, Bd. 11530 LNAI, Springer, Cham, S. 72-85, 24th International Conference on Conceptual Structures , Marburg, Hessen, Deutschland, 01.07.19. https://doi.org/10.1007/978-3-030-23182-8_6

Lifted Temporal Most Probable Explanation. / Gehrke, Marcel; Braun, Tanya; Möller, Ralf.
ICCS 2019: Graph-Based Representation and Reasoning. Hrsg. / Dominik Endres; Mehwish Alam; Diana Şotropa. Band 11530 LNAI Springer, Cham, 2019. S. 72-85 (Lecture Notes in Computer Science; Band 11530 LNAI).

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

TY - GEN

T1 - Lifted Temporal Most Probable Explanation

AU - Gehrke, Marcel

AU - Braun, Tanya

AU - Möller, Ralf

N1 - Conference code: 227759

PY - 2019/6/19

Y1 - 2019/6/19

N2 - The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Another type of query asks for a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJTmpe to efficiently solve the temporal MPE problem for temporal probabilistic relational models and (ii) a combination of LDJT and LDJTmpe to efficiently answer assignment queries for a given number of time steps.

AB - The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Another type of query asks for a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJTmpe to efficiently solve the temporal MPE problem for temporal probabilistic relational models and (ii) a combination of LDJT and LDJTmpe to efficiently answer assignment queries for a given number of time steps.

UR - https://www.scopus.com/pages/publications/85068619556

U2 - 10.1007/978-3-030-23182-8_6

DO - 10.1007/978-3-030-23182-8_6

M3 - Conference contribution

SN - 978-3-030-23181-1

VL - 11530 LNAI

T3 - Lecture Notes in Computer Science

SP - 72

EP - 85

BT - ICCS 2019: Graph-Based Representation and Reasoning

A2 - Endres, Dominik

A2 - Alam, Mehwish

A2 - Şotropa, Diana

PB - Springer, Cham

T2 - 24th International Conference on Conceptual Structures

Y2 - 1 July 2019 through 4 July 2019

ER -

Lifted Temporal Most Probable Explanation

Abstract

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