The PENGASP system: architecture, language and authoring tool
In this article, we describe the architecture, the language and the authoring tool of the PENG A S P system. This system supports the writing of non-monotonic specifications in controlled natural language with the help of a web-based predictive text editor. This predictive editor communicates asynch...
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| Published in | Language resources and evaluation Vol. 51; no. 1; pp. 67 - 92 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.03.2017
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In this article, we describe the architecture, the language and the authoring tool of the PENG
A
S
P
system. This system supports the writing of non-monotonic specifications in controlled natural language with the help of a web-based predictive text editor. This predictive editor communicates asynchronously with a controlled natural language processor that translates the specification text via discourse representation structures into executable Answer Set Programs (ASP). The controlled natural language processor additionally generates lookahead categories and anaphoric expressions for the author of a specification text, and it provides a paraphrase of the specification that clarifies the interpretation of the text by the machine. The predictive editor is a central component of the PENG
A
S
P
system; it guides the writing process and displays multiple sets of lookahead categories simultaneously for different possible sentence completions as well as anaphoric expressions, and supports the addition of new content words to the lexicon . |
|---|---|
| AbstractList | In this article, we describe the architecture, the language and the authoring tool of the PENGASP system. This system supports the writing of non-monotonic specifications in controlled natural language with the help of a web-based predictive text editor. This predictive editor communicates asynchronously with a controlled natural language processor that translates the specification text via discourse representation structures into executable Answer Set Programs (ASP). The controlled natural language processor additionally generates lookahead categories and anaphoric expressions for the author of a specification text, and it provides a paraphrase of the specification that clarifies the interpretation of the text by the machine. The predictive editor is a central component of the PENGASP system; it guides the writing process and displays multiple sets of lookahead categories simultaneously for different possible sentence completions as well as anaphoric expressions, and supports the addition of new content words to the lexicon . In this article, we describe the architecture, the language and the authoring tool of the PENG A S P system. This system supports the writing of non-monotonic specifications in controlled natural language with the help of a web-based predictive text editor. This predictive editor communicates asynchronously with a controlled natural language processor that translates the specification text via discourse representation structures into executable Answer Set Programs (ASP). The controlled natural language processor additionally generates lookahead categories and anaphoric expressions for the author of a specification text, and it provides a paraphrase of the specification that clarifies the interpretation of the text by the machine. The predictive editor is a central component of the PENG A S P system; it guides the writing process and displays multiple sets of lookahead categories simultaneously for different possible sentence completions as well as anaphoric expressions, and supports the addition of new content words to the lexicon . |
| Author | Schwitter, Rolf Guy, Stephen C. |
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| DOI | 10.1007/s10579-016-9338-7 |
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| References | GebserMKaminskiRKaufmannBSchaubTAnswer Set Solving in practiceSynthesis Lectures on Artificial Intelligence and Machine Learning201263123810.2200/S00457ED1V01Y201211AIM019 EtzioniOGoldenKWeldDSound and efficient closed-world reasoning for planningArtificial Intelligence1998891–2113148 Erdem, E., & Yeniterzi, R. (2009). Transforming controlled natural language biomedical queries into Answer Set Programs. In Proceedings of the workshop on BioNLP (pp. 117–124), Boulder, CO. Lifschitz, V. (2008). What is Answer Set Programming? In Proceedings of AAAI 2008 (pp. 1594–1597). KuhnTA survey and classification of controlled natural languagesComputational Linguistics201440112117010.1162/COLI_a_00168 KrötzschMVrandečićCVölkelMHallerHStuderRSemantic WikipediaJournal of Web Semantics20075425126110.1016/j.websem.2007.09.001 Kim, T.-W., Lee, J., & Palla, R. (2009). Circumscriptive event calculus as Answer Set Programming. In Proceedings of 21st international joint conference on artificial intelligence (IJCAI 2009) (pp. 823–829). van EijckJKampHvan BenthemJter MeulenADiscourse representation in contextHandbook of logic and language20112AmsterdamElsevier18125210.1016/B978-0-444-53726-3.00003-7 Doherty, P., & Kvarnström, J. (2008). Temporal action logics. In B. Porter, V. Lifschitz, & F. van Harmelen (Eds.), Handbook of knowledge representation, Chapter 18 (pp. 709–757). Elsevier. Grasso, G., Leone, N., & Ricca, F. (2013). Answer Set Programming: Language, applications and development tools. In W. Faber, & D. Lembo (Eds.), Web reasoning and rule systems (RR 2013), LNCS 7994 (pp. 19–34). PowerRKuhnTFuchsNEOWL simplified English: A finite-state language for ontology editingProceedings of CNL 20122012HeidelbergSpringer4460 SowaJFArchitectures for intelligent systemsSpecial Issue on Artificial Intelligence of the IBM Systems Journal2002413331349 Clark, P., & Porter, B. (2011). KM—The knowledge machine 2.0: User manual. KampHReyleUFrom discourse to logic1993DordrechtKluwer Schwitter, R., Ljungberg, A., & Hood, D. (2003). ECOLE: A look-ahead editor for a controlled language. In Proceedings of EAMT-CLAW03 (pp. 141–150), Dublin. Guy, S., & Schwitter, R. (2014). Architecture of a Web-based predictive editor for controlled natural language processing. In B. Davis, et al. (Eds.), CNL 2014, LNAI 8625 (pp. 167–178). Lee, J., & Palla, R. (2012). Reformulating temporal action logics in answer set programming. In Proceedings of the AAAI conference on artificial intelligence (AAAI) (pp. 786–792). Baral, C., & Dzifcak J. (2012). Solving puzzles described in English by automated translation to answer set programming and learning how to do that translation. In Proceedings of KR 2012 (pp. 573–577). Schwitter, R. (2011). Specifying events and their effects in controlled natural language. In N. A. Aziz, K. Hasida, A. W. A. Rahman, & H. Saito (Eds.), Computational linguistics and related eields, procedia—Social and behavioral sciences (vol. 27, pp. 12–21). Elsevier. Sommerville, I. (2011). Software engineering, international edition (9th edn, pp. 155–164). New York: Pearson. Gelfond, M., & Lifschitz, V. (1988). The stable model semantics for logic programming. In Proceedings of the fifth international conference on logic programming (ICLP) (pp. 1070–1080). Schüller, P. (2013). Flexible combinatory categorial grammar parsing using the CYK algorithm and Answer Set Programming. In P. Cabalar, & T. C. Son (Eds.), Logic programming and nonmonotonic reasoning (LPNMR), LNCS 8148 (pp. 499–511). Baral, C., Dzifcak, J., & Son, T. C. (2008). Using Answer Set Programming and lambda calculus to characterize natural language sentences with normatives and exceptions. In Proceedings of the twenty-third conference on artificial intelligence (AAAI 2008) (pp. 818–823). GunningDChaudhriVKClarkPBarkerKProject Halo update—Progress toward digital AristotleAI Magazine20103133358 Fuchs, N. E., Kaljurand, K., & Kuhn, T. (2008). Attempto controlled English for knowledge representation. In C. Baroglio, P. A. Bonatti, J. Maluszynski, M. Marchiori, A. Polleres, & S. Schaffert (Eds.), Reasoning Web, fourth international summer school 2008, LNCS 5224 (pp. 104–124). MuellerETCommonsense reasoning: An event calculus based approach20142Los AltosMorgan Kaufmann Franconi, E., Guagliardo, P., Trevisan, M., & Tessaris S. (2011). Quelo: An ontology-driven query interface. In Proceedings of the 24th international workshop on description logics (DL 2011). Freeman, E., Robson, E., Bates, B., & Sierra, K. (2004). Head first design patterns (pp. 526–577). O’Reilly. Todorova, Y., & Gelfond, M. (2012). Toward question answering in travel domains. In E. Erdem, et al. (Eds.), Correct reasoning, LNCS 7265 (pp. 311–326). Clark, P., Harrison, P., Jenkins, T., Thompson, J., & Wojcik, R. (2005). Acquiring and using world knowledge using a restricted subset of English. In The 18th international FLAIRS conference (FLAIRS’05) (pp. 506–511). Schwitter, R. (2010). Controlled natural languages for knowledge representation. In Proceedings of COLING 2010 (pp. 1113–1121), Beijing, China. Erdem, E., Erdogan, H., & Öztok, U. (2011). BIOQUERY-ASP: Querying biomedical ontologies using Answer Set Programming. In Proceedings of the 5th international RuleML2011@BRF challenge, co-located with the 5th international rule symposium, Fort Lauderdale, FL, USA, November 3–5. GebserMKaminskiRKaufmannBOstrowskiMSchaubTSchneiderMPotassco: The Potsdam Answer Set solving collectionAI Communications2011242105124 Kuhn, T. (2010). Controlled English for knowledge representation. Doctoral thesis, Faculty of Economics, Business Administration and Information Technology of the University of Zurich. Kuhn, T. (2008). AceWiki: A natural and expressive semantic Wiki. In CEUR workshop proceedings semantic Web user interaction at CHI 2008: Exploring HCI challenges. Lierler, Y., & Lifschitz, V. (2013). Logic programs vs. first-order formulas in textual inference. In Proceedings of the 10th international conference on computational semantics (IWCS 2013) (pp. 340–346), Potsdam, Germany. ShanahanMSolving the frame problem: A mathematical investigation of the common sense law of inertia1997Cambridge, MAMIT Press BrewkaGEiterTTruszczyńskiMAnswer Set Programming at a glanceCommunications of the ACM201154129210310.1145/2043174.2043195 Tennant, H. R., Ross, K. M., Saenz, R. M., Thompson, C. W., & Miller, J. R. (1983) Menu-based natural language understanding. In Proceedings of ACL (pp. 151–158). GelfondMKahlYKnowledge representation, reasoning, and the design of intelligent agents, the Answer-Set Programming approach2014CambridgeCambridge University Press10.1017/CBO9781139342124 ReiterRA logic for default reasoningArtificial Intelligence1980138113210.1016/0004-3702(80)90014-4 |
| References_xml | – reference: Gelfond, M., & Lifschitz, V. (1988). The stable model semantics for logic programming. In Proceedings of the fifth international conference on logic programming (ICLP) (pp. 1070–1080). – reference: Schwitter, R. (2011). Specifying events and their effects in controlled natural language. In N. A. Aziz, K. Hasida, A. W. A. Rahman, & H. Saito (Eds.), Computational linguistics and related eields, procedia—Social and behavioral sciences (vol. 27, pp. 12–21). Elsevier. – reference: KuhnTA survey and classification of controlled natural languagesComputational Linguistics201440112117010.1162/COLI_a_00168 – reference: Baral, C., Dzifcak, J., & Son, T. C. (2008). Using Answer Set Programming and lambda calculus to characterize natural language sentences with normatives and exceptions. In Proceedings of the twenty-third conference on artificial intelligence (AAAI 2008) (pp. 818–823). – reference: Fuchs, N. E., Kaljurand, K., & Kuhn, T. (2008). Attempto controlled English for knowledge representation. In C. Baroglio, P. A. Bonatti, J. Maluszynski, M. Marchiori, A. Polleres, & S. Schaffert (Eds.), Reasoning Web, fourth international summer school 2008, LNCS 5224 (pp. 104–124). – reference: Schwitter, R. (2010). Controlled natural languages for knowledge representation. In Proceedings of COLING 2010 (pp. 1113–1121), Beijing, China. – reference: Schwitter, R., Ljungberg, A., & Hood, D. (2003). ECOLE: A look-ahead editor for a controlled language. In Proceedings of EAMT-CLAW03 (pp. 141–150), Dublin. – reference: Clark, P., Harrison, P., Jenkins, T., Thompson, J., & Wojcik, R. (2005). Acquiring and using world knowledge using a restricted subset of English. In The 18th international FLAIRS conference (FLAIRS’05) (pp. 506–511). – reference: GebserMKaminskiRKaufmannBOstrowskiMSchaubTSchneiderMPotassco: The Potsdam Answer Set solving collectionAI Communications2011242105124 – reference: Doherty, P., & Kvarnström, J. (2008). Temporal action logics. In B. Porter, V. Lifschitz, & F. van Harmelen (Eds.), Handbook of knowledge representation, Chapter 18 (pp. 709–757). Elsevier. – reference: Erdem, E., & Yeniterzi, R. (2009). Transforming controlled natural language biomedical queries into Answer Set Programs. In Proceedings of the workshop on BioNLP (pp. 117–124), Boulder, CO. – reference: GelfondMKahlYKnowledge representation, reasoning, and the design of intelligent agents, the Answer-Set Programming approach2014CambridgeCambridge University Press10.1017/CBO9781139342124 – reference: Baral, C., & Dzifcak J. (2012). Solving puzzles described in English by automated translation to answer set programming and learning how to do that translation. In Proceedings of KR 2012 (pp. 573–577). – reference: Erdem, E., Erdogan, H., & Öztok, U. (2011). BIOQUERY-ASP: Querying biomedical ontologies using Answer Set Programming. In Proceedings of the 5th international RuleML2011@BRF challenge, co-located with the 5th international rule symposium, Fort Lauderdale, FL, USA, November 3–5. – reference: SowaJFArchitectures for intelligent systemsSpecial Issue on Artificial Intelligence of the IBM Systems Journal2002413331349 – reference: Lifschitz, V. (2008). What is Answer Set Programming? In Proceedings of AAAI 2008 (pp. 1594–1597). – reference: PowerRKuhnTFuchsNEOWL simplified English: A finite-state language for ontology editingProceedings of CNL 20122012HeidelbergSpringer4460 – reference: van EijckJKampHvan BenthemJter MeulenADiscourse representation in contextHandbook of logic and language20112AmsterdamElsevier18125210.1016/B978-0-444-53726-3.00003-7 – reference: Guy, S., & Schwitter, R. (2014). Architecture of a Web-based predictive editor for controlled natural language processing. In B. Davis, et al. (Eds.), CNL 2014, LNAI 8625 (pp. 167–178). – reference: Lee, J., & Palla, R. (2012). Reformulating temporal action logics in answer set programming. In Proceedings of the AAAI conference on artificial intelligence (AAAI) (pp. 786–792). – reference: EtzioniOGoldenKWeldDSound and efficient closed-world reasoning for planningArtificial Intelligence1998891–2113148 – reference: GunningDChaudhriVKClarkPBarkerKProject Halo update—Progress toward digital AristotleAI Magazine20103133358 – reference: ReiterRA logic for default reasoningArtificial Intelligence1980138113210.1016/0004-3702(80)90014-4 – reference: KampHReyleUFrom discourse to logic1993DordrechtKluwer – reference: Kim, T.-W., Lee, J., & Palla, R. (2009). Circumscriptive event calculus as Answer Set Programming. In Proceedings of 21st international joint conference on artificial intelligence (IJCAI 2009) (pp. 823–829). – reference: Kuhn, T. (2008). AceWiki: A natural and expressive semantic Wiki. In CEUR workshop proceedings semantic Web user interaction at CHI 2008: Exploring HCI challenges. – reference: BrewkaGEiterTTruszczyńskiMAnswer Set Programming at a glanceCommunications of the ACM201154129210310.1145/2043174.2043195 – reference: Grasso, G., Leone, N., & Ricca, F. (2013). Answer Set Programming: Language, applications and development tools. In W. Faber, & D. Lembo (Eds.), Web reasoning and rule systems (RR 2013), LNCS 7994 (pp. 19–34). – reference: Schüller, P. (2013). Flexible combinatory categorial grammar parsing using the CYK algorithm and Answer Set Programming. In P. Cabalar, & T. C. Son (Eds.), Logic programming and nonmonotonic reasoning (LPNMR), LNCS 8148 (pp. 499–511). – reference: Clark, P., & Porter, B. (2011). KM—The knowledge machine 2.0: User manual. – reference: Freeman, E., Robson, E., Bates, B., & Sierra, K. (2004). Head first design patterns (pp. 526–577). O’Reilly. – reference: GebserMKaminskiRKaufmannBSchaubTAnswer Set Solving in practiceSynthesis Lectures on Artificial Intelligence and Machine Learning201263123810.2200/S00457ED1V01Y201211AIM019 – reference: Lierler, Y., & Lifschitz, V. (2013). Logic programs vs. first-order formulas in textual inference. In Proceedings of the 10th international conference on computational semantics (IWCS 2013) (pp. 340–346), Potsdam, Germany. – reference: Sommerville, I. (2011). Software engineering, international edition (9th edn, pp. 155–164). New York: Pearson. – reference: ShanahanMSolving the frame problem: A mathematical investigation of the common sense law of inertia1997Cambridge, MAMIT Press – reference: Tennant, H. R., Ross, K. M., Saenz, R. M., Thompson, C. W., & Miller, J. R. (1983) Menu-based natural language understanding. In Proceedings of ACL (pp. 151–158). – reference: MuellerETCommonsense reasoning: An event calculus based approach20142Los AltosMorgan Kaufmann – reference: Kuhn, T. (2010). Controlled English for knowledge representation. Doctoral thesis, Faculty of Economics, Business Administration and Information Technology of the University of Zurich. – reference: Franconi, E., Guagliardo, P., Trevisan, M., & Tessaris S. (2011). Quelo: An ontology-driven query interface. In Proceedings of the 24th international workshop on description logics (DL 2011). – reference: KrötzschMVrandečićCVölkelMHallerHStuderRSemantic WikipediaJournal of Web Semantics20075425126110.1016/j.websem.2007.09.001 – reference: Todorova, Y., & Gelfond, M. (2012). Toward question answering in travel domains. In E. Erdem, et al. (Eds.), Correct reasoning, LNCS 7265 (pp. 311–326). |
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| SubjectTerms | Anaphora Architecture Authoring Communications systems Computational Linguistics Computer Science Content words Human communication Language and Literature Linguistics Microprocessors Natural language processing Original Paper Paraphrase Predictive control Social Sciences Specifications |
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| Title | The PENGASP system: architecture, language and authoring tool |
| URI | https://link.springer.com/article/10.1007/s10579-016-9338-7 https://www.proquest.com/docview/1879879058 |
| Volume | 51 |
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