Hostname: page-component-74656f9884-54qdv Total loading time: 0 Render date: 2026-11-10T02:57:58.739Z Has data issue: false hasContentIssue false
  • English
  • Français

Action-Centered Information Retrieval

Published online by Cambridge University Press:  09 August 2019

MARCELLO BALDUCCINI  and
EMILY C. LEBLANC
MARCELLO BALDUCCINI
Affiliation:
Saint Joseph’s University, Philadelphia, PA, USA (e-mail: marcello.balduccini@gmail.com)
EMILY C. LEBLANC
Affiliation:
Drexel University, Philadelphia, PA, USA (e-mail: ecl38@drexel.edu)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Information retrieval (IR) aims at retrieving documents that are most relevant to a query provided by a user. Traditional techniques rely mostly on syntactic methods. In some cases, however, links at a deeper semantic level must be considered. In this paper, we explore a type of IR task in which documents describe sequences of events, and queries are about the state of the world after such events. In this context, successfully matching documents and query requires considering the events’ possibly implicit uncertain effects and side effects. We begin by analyzing the problem, then propose an action language-based formalization, and finally automate the corresponding IR task using answer set programming.

Keywords

reasoning about actions and changeanswer set programminginformation retrieval

Information

Type
Technical Note
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 2 , March 2020 , pp. 249 - 272
Copyright
© Cambridge University Press 2019 

References

Baral, C. and Gelfond, M. 2000. Reasoning agents in dynamic domains. In Workshop on Logic-Based Artificial Intelligence. Kluwer Academic Publishers, 257–279.CrossRefGoogle Scholar
Balduccini, M. and Gelfond, M. 2003. Diagnostic reasoning with A-Prolog. Journal of Theory and Practice of Logic Programming (TPLP) 3, 4–5 (Jul), 425–461.CrossRefGoogle Scholar
Balduccini, M., Gelfond, M., and Nogueira, M. 2006. Answer Set Based Design of Knowledge Systems. Annals of Mathematics and Artificial Intelligence 47, 1–2, 183–219.CrossRefGoogle Scholar
Blanco, R. and Lioma, C. 2012. Graph-based term weighting for information retrieval. Information Retrieval 15, 1, 54–92.CrossRefGoogle Scholar
Campos, R. 2015. Survey of temporal information retrieval and related Applications. ACM Computing Surveys (CSUR) 47, 2.Google Scholar
Carpineto, C. and Ramano, G. 2012. A survey of automatic query expansion in information retrieval. ACM Computing Surveys (CSUR) 44, 1.CrossRefGoogle Scholar
Dong, X. L., Gabrilovich, E., Heitz, G., Horn, W., Murphy, K., Sun, S., and Zhang, W. 2014. From data fusion to knowledge fusion. Proceedings of the VLDB Endowment 7, 10, 881–892.CrossRefGoogle Scholar
Glavas, G. and Snajder, J. 2014. Event graphs for information retrieval and multi-document summarization. Expert Systems with Applications 41, 15, 6904–6916.CrossRefGoogle Scholar
Inclezan, D. 2016. CoreALMlib: An ALM library translated from the component library. In 32nd International Conference on Logic Programming (ICLP 2016).Google Scholar
Korfhage, R. R. 1997. Information Storage and Retrieval. John Wiley and Sons, Inc.Google Scholar
LeBlanc, E. and Balduccini, M. 2016. Interpreting natural language sources using transition diagrams. In Logic Programming with Constraints for Language Processing (CSLP 2016), Christiansen, H. and Dahl, V., Eds.Google Scholar
Lierler, Y., Inclezan, D., and Gelfond, M. 2017. Action languages and question answering. In 12th International Conference on Computational Semantics (IWCS 2017).Google Scholar
Liu, T.-Y., Xu, J., Qin, T., Xiong, W., and Li, H. 2007. Letor: Benchmark dataset for research on learning to rank for information retrieval. In Proceedings of SIGIR 2007 Workshop on Learning to Rank for Information Retrieval. Vol. 310. ACM, Amsterdam, The Netherlands.Google Scholar
Lukasiewicz, T. and Straccia, U. 2007. Top-k retrieval in description logic programs under vagueness for the semantic web. In International Conference on Scalable Uncertainty Management. Springer, 16–30.CrossRefGoogle Scholar
Manning, C., Christopher, D., Raghavan, P., and Schütze, H. 2008. Introduction to Information Retrieval. Vol. 1. Cambridge University Press.CrossRefGoogle Scholar
Matuszek, C., Cabral, J., Witbrock, M. J., and DeOliveira, J. 2006. An Introduction to the Syntax and Content of CYC. In AAAI Spring Symposium: Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering, 44–49.Google Scholar
Mitra, A. and Baral, C. 2016. Addressing a question answering challenge by combining statistical methods with inductive rule learning and reasoning. In AAAI, 2779–2785.Google Scholar
Morales, R., Tu, P. H., and Son, T. C. 2007. An extension to conformant planning using logic programming. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI 2007), Veloso, M. M., Ed, 1991–1996.Google Scholar
Nguyen, V., Mitra, A., and Baral, C. 2015. The NL2KR platform for building natural language translation systems. In 53rd Annual Meeting of the Association for Computational Linguistics (ACL-IJCNLP 2015), 899–908.Google Scholar
Page, L., Brin, S., Motwani, R., and Winograd, T. 1999. The pagerank citation ranking: Bringing order to the web.Google Scholar
Suchanek, F. M., Kasneci, G., and Weikum, G. 2008. Yago: A large ontology from Wikipedia and WordNet. Web Semantics: Science, Services and Agents on the World Wide Web 6, 3, 203–217.CrossRefGoogle 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

Balduccini and Leblanc supplementary material

Appendix

Download Balduccini and Leblanc supplementary material(PDF)
PDF 76.6 KB