Answer set programming via controlled natural language processing

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

Abstract

Controlled natural languages are subsets of natural languages that can be used to describe a problem in a very precise way, furthermore they can often be translated automatically into a formal notation. We investigate in this paper how a controlled natural language can be used as a specification language for Answer Set Programming (ASP). ASP is a declarative approach to problem solving and has its roots in knowledge representation, logic programming, and constraint satisfaction. Solutions of ASP programs are stable models (= answer sets) that build the starting point for question answering. As a proof of concept, we translate a problem specification written in controlled natural language into an ASP program and compute a stable model that contains the answers to a number of questions.

LanguageEnglish
Title of host publicationControlled Natural Language - Third International Workshop, CNL 2012, Proceedings
EditorsTobias Kuhn, Norbert E. Fuchs
Place of PublicationHeidelberg, Germany
PublisherSpringer, Springer Nature
Pages26-43
Number of pages18
Volume7427 LNAI
ISBN (Print)9783642326110
DOIs
Publication statusPublished - 2012
Event3rd International Workshop on Controlled Natural Language, CNL 2012 - Zurich, Switzerland
Duration: 29 Aug 201231 Aug 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7427 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Workshop on Controlled Natural Language, CNL 2012
CountrySwitzerland
CityZurich
Period29/08/1231/08/12

Fingerprint

Answer Set Programming
Natural Language
Stable Models
Specification languages
Logic programming
Knowledge representation
Processing
Specifications
Answer Sets
Constraint Satisfaction
Question Answering
Specification Languages
Knowledge Representation
Logic Programming
Notation
Roots
Specification
Subset

Cite this

Schwitter, R. (2012). Answer set programming via controlled natural language processing. In T. Kuhn, & N. E. Fuchs (Eds.), Controlled Natural Language - Third International Workshop, CNL 2012, Proceedings (Vol. 7427 LNAI, pp. 26-43). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7427 LNAI). Heidelberg, Germany: Springer, Springer Nature. https://doi.org/10.1007/978-3-642-32612-7_3
Schwitter, Rolf. / Answer set programming via controlled natural language processing. Controlled Natural Language - Third International Workshop, CNL 2012, Proceedings. editor / Tobias Kuhn ; Norbert E. Fuchs. Vol. 7427 LNAI Heidelberg, Germany : Springer, Springer Nature, 2012. pp. 26-43 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{ef659de8544040a68a962555406b826e,
title = "Answer set programming via controlled natural language processing",
abstract = "Controlled natural languages are subsets of natural languages that can be used to describe a problem in a very precise way, furthermore they can often be translated automatically into a formal notation. We investigate in this paper how a controlled natural language can be used as a specification language for Answer Set Programming (ASP). ASP is a declarative approach to problem solving and has its roots in knowledge representation, logic programming, and constraint satisfaction. Solutions of ASP programs are stable models (= answer sets) that build the starting point for question answering. As a proof of concept, we translate a problem specification written in controlled natural language into an ASP program and compute a stable model that contains the answers to a number of questions.",
author = "Rolf Schwitter",
year = "2012",
doi = "10.1007/978-3-642-32612-7_3",
language = "English",
isbn = "9783642326110",
volume = "7427 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "26--43",
editor = "Tobias Kuhn and Fuchs, {Norbert E.}",
booktitle = "Controlled Natural Language - Third International Workshop, CNL 2012, Proceedings",
address = "United States",

}

Schwitter, R 2012, Answer set programming via controlled natural language processing. in T Kuhn & NE Fuchs (eds), Controlled Natural Language - Third International Workshop, CNL 2012, Proceedings. vol. 7427 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7427 LNAI, Springer, Springer Nature, Heidelberg, Germany, pp. 26-43, 3rd International Workshop on Controlled Natural Language, CNL 2012, Zurich, Switzerland, 29/08/12. https://doi.org/10.1007/978-3-642-32612-7_3

Answer set programming via controlled natural language processing. / Schwitter, Rolf.

Controlled Natural Language - Third International Workshop, CNL 2012, Proceedings. ed. / Tobias Kuhn; Norbert E. Fuchs. Vol. 7427 LNAI Heidelberg, Germany : Springer, Springer Nature, 2012. p. 26-43 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7427 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

TY - GEN

T1 - Answer set programming via controlled natural language processing

AU - Schwitter, Rolf

PY - 2012

Y1 - 2012

N2 - Controlled natural languages are subsets of natural languages that can be used to describe a problem in a very precise way, furthermore they can often be translated automatically into a formal notation. We investigate in this paper how a controlled natural language can be used as a specification language for Answer Set Programming (ASP). ASP is a declarative approach to problem solving and has its roots in knowledge representation, logic programming, and constraint satisfaction. Solutions of ASP programs are stable models (= answer sets) that build the starting point for question answering. As a proof of concept, we translate a problem specification written in controlled natural language into an ASP program and compute a stable model that contains the answers to a number of questions.

AB - Controlled natural languages are subsets of natural languages that can be used to describe a problem in a very precise way, furthermore they can often be translated automatically into a formal notation. We investigate in this paper how a controlled natural language can be used as a specification language for Answer Set Programming (ASP). ASP is a declarative approach to problem solving and has its roots in knowledge representation, logic programming, and constraint satisfaction. Solutions of ASP programs are stable models (= answer sets) that build the starting point for question answering. As a proof of concept, we translate a problem specification written in controlled natural language into an ASP program and compute a stable model that contains the answers to a number of questions.

UR - http://www.scopus.com/inward/record.url?scp=84865682581&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-32612-7_3

DO - 10.1007/978-3-642-32612-7_3

M3 - Conference proceeding contribution

SN - 9783642326110

VL - 7427 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 26

EP - 43

BT - Controlled Natural Language - Third International Workshop, CNL 2012, Proceedings

A2 - Kuhn, Tobias

A2 - Fuchs, Norbert E.

PB - Springer, Springer Nature

CY - Heidelberg, Germany

ER -

Schwitter R. Answer set programming via controlled natural language processing. In Kuhn T, Fuchs NE, editors, Controlled Natural Language - Third International Workshop, CNL 2012, Proceedings. Vol. 7427 LNAI. Heidelberg, Germany: Springer, Springer Nature. 2012. p. 26-43. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-32612-7_3