Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-04-30T17:11:44.487Z Has data issue: false hasContentIssue false
  • English
  • Français

Knowledge Authoring for Rules and Actions

Published online by Cambridge University Press:  12 July 2023

YUHENG WANG Open the ORCID record for YUHENG WANG [Opens in a new window] ,
PAUL FODOR Open the ORCID record for PAUL FODOR [Opens in a new window]  and
MICHAEL KIFER
YUHENG WANG
Affiliation:
Stony Brook University, Stony Brook, NY, USA (e-mails: yuhewang@cs.stonybrook.edu, pfodor@cs.stonybrook.edu, kifer@cs.stonybrook.edu)
PAUL FODOR
Affiliation:
Stony Brook University, Stony Brook, NY, USA (e-mails: yuhewang@cs.stonybrook.edu, pfodor@cs.stonybrook.edu, kifer@cs.stonybrook.edu)
MICHAEL KIFER
Affiliation:
Stony Brook University, Stony Brook, NY, USA (e-mails: yuhewang@cs.stonybrook.edu, pfodor@cs.stonybrook.edu, kifer@cs.stonybrook.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

Knowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALMFL, a successor of KALM, replaced CNL with factual English, which is much less restrictive and requires very little training from users. However, KALMFL has limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALMRA to enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALMRA achieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALMRA achieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALMRA by drawing attention to the problems faced by the recently made famous AI, ChatGPT.

Keywords

knowledge authoringknowledge representation and reasoningnatural language understandingframe-based parsing
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 4: 2023 International Conference on Logic Programming , July 2023 , pp. 797 - 811
Check for updates
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Research partially funded by NSF grant 1814457.

References

Baker, C. F., Fillmore, C. J. and Lowe, J. B. 1998. The berkeley framenet project. In 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics. COLING, 86–90.Google Scholar
Committee on Quality Improvement, S. O. U. T. I. 1999. Practice parameter: The diagnosis, treatment, and evaluation of the initial urinary tract infection in febrile infants and young children. Pediatrics 103, 4, 843–852.10.1542/peds.103.4.843CrossRefGoogle Scholar
Fillmore, C. J. 2006. Frame semantics. In Cognitive Linguistics: Basic Readings 34, 373–400.Google Scholar
Fuchs, N. E., Kaljurand, K. and Kuhn, T. 2008. Attempto controlled english for knowledge representation. In Reasoning Web. Springer, 104–124.Google Scholar
Fuchs, N. E. and Schwitter, R. 1996. Attempto controlled english (ace). arXiv preprint cmplg/9603003.Google Scholar
Gao, T., Fodor, P. and Kifer, M. 2018a. High accuracy question answering via hybrid controlled natural language. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 17–24.Google Scholar
Gao, T., Fodor, P. and Kifer, M. 2018b. Knowledge authoring for rule-based reasoning. In OTM Confederated International Conferences on the Move to Meaningful Internet Systems. Springer, 461–480.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2019. Multi-shot asp solving with clingo. Theory and Practice of Logic Programming 19, 1, 27–82.10.1017/S1471068418000054CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In ICLP/SLP, vol. 88. Cambridge, MA, 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385.10.1007/BF03037169CrossRefGoogle Scholar
Kifer, M. and Lausen, G. 1989. F-logic: A higher-order language for reasoning about objects, inheritance, and scheme. In Proceedings of the 1989 ACM SIGMOD International Conference on Management of Data, 134–146.Google Scholar
Kifer, M., Lausen, G. and Wu, J. 1995. Logical foundations of object-oriented and frame-based languages. The Journal of the ACM 42, 741–843.10.1145/210332.210335CrossRefGoogle Scholar
Kowalski, R. and Sergot, M. 1989. A logic-based calculus of events. In Foundations of knowledge Base Management. Springer, 23–55.10.1007/978-3-642-83397-7_2CrossRefGoogle Scholar
Le, H., Tran, T. and Venkatesh, S. 2020. Self-attentive associative memory. In International Conference on Machine Learning. PMLR, 5682–5691.Google Scholar
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S. and Scarcello, F. 2006. The DLV system for knowledge representation and reasoning. ACM Transactions on Computational Logic (TOCL) 7, 3, 499–562.10.1145/1149114.1149117CrossRefGoogle Scholar
Mitra, A. and Baral, C. 2016. Addressing a question answering challenge by combining statistical methods with inductive rule learning and reasoning. In AAAI.10.1609/aaai.v30i1.10354CrossRefGoogle Scholar
Qi, P., Zhang, Y., Zhang, Y., Bolton, J. and Manning, C. D. 2020. Stanza: A python natural language processing toolkit for many human languages. arXiv preprint arXiv:2003.07082.10.18653/v1/2020.acl-demos.14CrossRefGoogle Scholar
Ringgaard, M., Gupta, R. and Pereira, F. C. 2017. Sling: A framework for frame semantic parsing. arXiv preprint arXiv:1710.07032.Google Scholar
Sadri, F. and Kowalski, R. A. 1995. Variants of the event calculus. In ICLP, 67–81.Google Scholar
Schwitter, R. 2002. English as a formal specification language. In Proceedings. 13th International Workshop on Database and Expert Systems Applications. IEEE, 228–232.Google Scholar
Shiffman, R. N., Michel, G., Krauthammer, M., Fuchs, N. E., Kaljurand, K. and Kuhn, T. 2009. Writing clinical practice guidelines in controlled natural language. In International Workshop on Controlled Natural Language. Springer, 265–280.Google Scholar
Swayamdipta, S., Thomson, S., Dyer, C. and Smith, N. A. 2017. Frame-semantic parsing with softmax-margin segmental rnns and a syntactic scaffold. arXiv preprint arXiv:1706.09528.Google Scholar
Swift, T. and Warren, D. S. 2012. XSB: Extending prolog with tabled logic programming. Theory and Practice of Logic Programming 12, 1–2, 157–187.10.1017/S1471068411000500CrossRefGoogle Scholar
Wan, H., Grosof, B. N., Kifer, M., Fodor, P. and Liang, S. 2009. Logic programming with defaults and argumentation theories. In Logic Programming, 25th International Conference, ICLP 2009, Pasadena, CA, USA, 14–17 July 2009. Proceedings, P. M. Hill and D. S. Warren, Eds. Lecture Notes in Computer Science, vol. 5649. Springer, 432–448.Google Scholar
Wang, Y., Borca-Tasciuc, G., Goel, N., Fodor, P. and Kifer, M. 2022. Knowledge authoring with factual english. arXiv preprint arXiv:2208.03094.10.4204/EPTCS.364.10CrossRefGoogle Scholar
Weston, J., Bordes, A., Chopra, S., Rush, A. M., van Merriënboer, B., Joulin, A. and Mikolov, T. 2015. Towards AI-complete question answering: A set of prerequisite toy tasks. arXiv preprint arXiv:1502.05698.Google Scholar
Supplementary material: PDF

Wang et al. supplementary material

Wang et al. supplementary material

Download Wang et al. supplementary material(PDF)
PDF 360.4 KB