Swift Logic for Big Data and Knowledge Graphs

Luigi  Bellomarini; Georg Gottlob; Andreas Pieris; Emanuel Sallinger

doi:10.24963/ijcai.2017/1

Swift Logic for Big Data and Knowledge Graphs

Luigi Bellomarini, Georg Gottlob, Andreas Pieris, Emanuel Sallinger

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

Abstract / Description of output

Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully-fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine learning and analytics packages. We present KRR formalisms and a system achieving these goals.

Original language	English
Title of host publication	Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17)
Publisher	IJCAI Inc
Pages	2-10
Number of pages	9
ISBN (Electronic)	978-0-9992411-0-3
DOIs	https://doi.org/10.24963/ijcai.2017/1
Publication status	Published - 25 Aug 2017
Event	26th International Joint Conference on Artificial Intelligence - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 https://ijcai-17.org/index.html https://ijcai-17.org/ https://ijcai-17.org/

Conference

Conference	26th International Joint Conference on Artificial Intelligence
Abbreviated title	IJCAI 2017
Country/Territory	Australia
City	Melbourne
Period	19/08/17 → 25/08/17
Internet address	https://ijcai-17.org/index.html https://ijcai-17.org/ https://ijcai-17.org/

Access to Document

10.24963/ijcai.2017/1

IJCAI-Swift-Logic-1Accepted author manuscript, 851 KB

https://www.ijcai.org/proceedings/2017/0001.pdf

1 Finished

VADA: Value Added Data Systems: Principles and Architecture
Libkin, L., Buneman, P., Fan, W. & Pieris, A.
EPSRC
1/04/15 → 30/09/20
Project: Research

2 Conference contribution

Swift Logic for Big Data and Knowledge Graphs: Overview of Requirements, Language, and System
Bellomarini, L., Gottlob, G., Pieris, A. & Sallinger, E., 2018, SOFSEM 2018: Theory and Practice of Computer Science: 44th International Conference on Current Trends in Theory and Practice of Computer Science, Krems, Austria, January 29 - February 2, 2018, Proceedings. Edizioni della Normale, Cham, p. 3-16 14 p. (Theoretical Computer Science and General Issues; vol. 10706)(Lecture Notes in Computer Science; vol. 10706).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
The Vadalog System: Swift Logic for Big Data and Enterprise Knowledge Graphs
Bellomarini, L., Gottlob, G., Pieris, A. & Sallinger, E., 2018, Proceedings of the 12th Alberto Mendelzon International Workshop on Foundations of Data Management: Cali, Colombia, May 21-25, 2018. CEUR Workshop Proceedings (CEUR-WS.org), 5 p. (CEUR Workshop Proceedings; vol. 2100).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Open Access
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Cite this

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title = "Swift Logic for Big Data and Knowledge Graphs",

abstract = "Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully-fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine learning and analytics packages. We present KRR formalisms and a system achieving these goals.",

author = "Luigi Bellomarini and Georg Gottlob and Andreas Pieris and Emanuel Sallinger",

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N2 - Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully-fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine learning and analytics packages. We present KRR formalisms and a system achieving these goals.

AB - Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully-fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine learning and analytics packages. We present KRR formalisms and a system achieving these goals.

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M3 - Conference contribution

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BT - Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17)

PB - IJCAI Inc

T2 - 26th International Joint Conference on Artificial Intelligence

Y2 - 19 August 2017 through 25 August 2017

ER -

Swift Logic for Big Data and Knowledge Graphs

Abstract / Description of output

Conference

Access to Document

Fingerprint

Projects

VADA: Value Added Data Systems: Principles and Architecture

Research output

Swift Logic for Big Data and Knowledge Graphs: Overview of Requirements, Language, and System

The Vadalog System: Swift Logic for Big Data and Enterprise Knowledge Graphs

Cite this