Counting and Conjunctive Queries in the Lifted Junction Tree Algorithm

Tanya Braun; Ralf Möller

doi:10.1007/978-3-319-78102-0_3

Counting and Conjunctive Queries in the Lifted Junction Tree Algorithm

Tanya Braun, Ralf Möller

Institut für Informationssysteme

5 Zitate (Scopus)

Abstract

Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a knowledge base and LVE in its computations. We extend LJT with a full formal specification of its algorithm steps incorporating (i) the lifting tool of counting and (ii) answering of conjunctive queries. Given multiple queries, e.g., in machine learning applications, our approach enables us to compute answers faster than the current LJT and existing approaches tailored for single queries.

Originalsprache	Englisch
Titel	Graph Structures for Knowledge Representation and Reasoning
Redakteure/-innen	Madalina Croitoru, Pierre Marquis, Sebastian Rudolph, Gem Stapleton
Seitenumfang	19
Band	10775
Erscheinungsort	Cham
Herausgeber (Verlag)	Springer International Publishing
Erscheinungsdatum	21.03.2018
Seiten	54-72
ISBN (Print)	978-3-319-78101-3
ISBN (elektronisch)	978-3-319-78102-0
DOIs	https://doi.org/10.1007/978-3-319-78102-0_3
Publikationsstatus	Veröffentlicht - 21.03.2018
Veranstaltung	5th International Workshop on Graph Structures for Knowledge Representation and Reasoning - Melbourne, Australien Dauer: 21.08.2017 → 21.08.2017 Konferenznummer: 212419

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 3 – Gesundheit und Wohlergehen
SDG 9 – Industrie, Innovation und Infrastruktur

DFG-Fachsystematik

4.43-01 Theoretische Informatik

Dokumente

10.1007/978-3-319-78102-0_3

Weitere Links

https://www.scopus.com/pages/publications/85045210721

Zitieren

Braun, T., & Möller, R. (2018). Counting and Conjunctive Queries in the Lifted Junction Tree Algorithm. in M. Croitoru, P. Marquis, S. Rudolph, & G. Stapleton (Hrsg.), Graph Structures for Knowledge Representation and Reasoning (Band 10775, S. 54-72). (Lecture Notes in Computer Science; Band 10775). Springer International Publishing. https://doi.org/10.1007/978-3-319-78102-0_3

@inproceedings{2b1dffa185084671a2348a5f0fb1343d,

title = "Counting and Conjunctive Queries in the Lifted Junction Tree Algorithm",

abstract = "Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a knowledge base and LVE in its computations. We extend LJT with a full formal specification of its algorithm steps incorporating (i) the lifting tool of counting and (ii) answering of conjunctive queries. Given multiple queries, e.g., in machine learning applications, our approach enables us to compute answers faster than the current LJT and existing approaches tailored for single queries.",

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

year = "2018",

month = mar,

day = "21",

doi = "10.1007/978-3-319-78102-0\_3",

language = "English",

isbn = "978-3-319-78101-3",

volume = "10775",

series = "Lecture Notes in Computer Science",

publisher = "Springer International Publishing",

pages = "54--72",

editor = "Madalina Croitoru and Pierre Marquis and Sebastian Rudolph and Gem Stapleton",

booktitle = "Graph Structures for Knowledge Representation and Reasoning",

note = "5th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2017 ; Conference date: 21-08-2017 Through 21-08-2017",

}

Braun, T & Möller, R 2018, Counting and Conjunctive Queries in the Lifted Junction Tree Algorithm. in M Croitoru, P Marquis, S Rudolph & G Stapleton (Hrsg.), Graph Structures for Knowledge Representation and Reasoning. Bd. 10775, Lecture Notes in Computer Science, Bd. 10775, Springer International Publishing, Cham, S. 54-72, 5th International Workshop on Graph Structures for Knowledge Representation and Reasoning, Melbourne, South Australia, Australien, 21.08.17. https://doi.org/10.1007/978-3-319-78102-0_3

Counting and Conjunctive Queries in the Lifted Junction Tree Algorithm. / Braun, Tanya; Möller, Ralf.
Graph Structures for Knowledge Representation and Reasoning. Hrsg. / Madalina Croitoru; Pierre Marquis; Sebastian Rudolph; Gem Stapleton. Band 10775 Cham: Springer International Publishing, 2018. S. 54-72 (Lecture Notes in Computer Science; Band 10775).

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

TY - GEN

T1 - Counting and Conjunctive Queries in the Lifted Junction Tree Algorithm

AU - Braun, Tanya

AU - Möller, Ralf

N1 - Conference code: 212419

PY - 2018/3/21

Y1 - 2018/3/21

N2 - Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a knowledge base and LVE in its computations. We extend LJT with a full formal specification of its algorithm steps incorporating (i) the lifting tool of counting and (ii) answering of conjunctive queries. Given multiple queries, e.g., in machine learning applications, our approach enables us to compute answers faster than the current LJT and existing approaches tailored for single queries.

AB - Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a knowledge base and LVE in its computations. We extend LJT with a full formal specification of its algorithm steps incorporating (i) the lifting tool of counting and (ii) answering of conjunctive queries. Given multiple queries, e.g., in machine learning applications, our approach enables us to compute answers faster than the current LJT and existing approaches tailored for single queries.

UR - https://www.scopus.com/pages/publications/85045210721

U2 - 10.1007/978-3-319-78102-0_3

DO - 10.1007/978-3-319-78102-0_3

M3 - Conference contribution

SN - 978-3-319-78101-3

VL - 10775

T3 - Lecture Notes in Computer Science

SP - 54

EP - 72

BT - Graph Structures for Knowledge Representation and Reasoning

A2 - Croitoru, Madalina

A2 - Marquis, Pierre

A2 - Rudolph, Sebastian

A2 - Stapleton, Gem

PB - Springer International Publishing

CY - Cham

T2 - 5th International Workshop on Graph Structures for Knowledge Representation and Reasoning

Y2 - 21 August 2017 through 21 August 2017

ER -

Counting and Conjunctive Queries in the Lifted Junction Tree Algorithm

Abstract

UN SDGs

DFG-Fachsystematik

Dokumente

Weitere Links

Fingerprint

Zitieren