Advertisement

Using the Formal Representations of “Elementary Events” to Set Up Computational Models of Full “Narratives”

  • Gian Piero ZarriEmail author
Chapter
Part of the Multimedia Systems and Applications book series (MMSA)

Abstract

In this chapter, we describe the conceptual tools that, in an NKRL context (NKRL = Narrative Knowledge Representation Language), allow us to obtain a (computer-usable) description of full “narratives” as logically structured associations of the constituting (and duly formalized) “elementary events.” Dealing with this problem means, in practice, being able to formalize those “connectivity phenomena”—denoted, at “surface level,” by logico-semantic coherence links like causality, goal, co-ordination, subordination, indirect speech, etc.—that assure the conceptual unity of a whole narrative. The second-order, unification based solutions adopted by NKRL in this context, “completive construction” and “binding occurrences,” allow us to take into account the connectivity phenomena by “reifying” the formal representations used to model the constitutive elementary events. These solutions, which are of interest from a general digital humanities point of view, are explained in some depth making use of several illustrating examples.

Keywords

Elementary events Narratives Connectivity phenomena Reification Completive construction Binding occurrences Inference rules 

References

  1. N. Ayari, A. Chibani, Y. Amirat, in Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA). Semantic Management of Human-Robot Interaction in Ambient Intelligence Environments Using N-Ary Ontologies (IEEEXplore, Piscataway, 2013), pp. 1164–1171Google Scholar
  2. M. Bal, Narratology: Introduction to the Theory of Narrative, 2nd edn. (University Press, Toronto, 1997)Google Scholar
  3. S. Bechhofer, F. van Harmelen, J. Hendler, I. Horrocks, D.L. McGuinness, P.F. Patel-Schneider, L.A. Stein (eds.), OWL Web Ontology Language Reference (W3C Recommendation 10 February 2004). W3C (2004), http://www.w3.org/TR/owl-ref/. Accessed 28 Feb 2016
  4. S. Ceccato (ed.), Linguistic Analysis and Programming for Mechanical Translation (Technical Report RADC-TR-60-18) (Feltrinelli, Milano, 1961)Google Scholar
  5. S. Ceccato, Automatic translation of languages. Inf. Storage Retr. 2, 105–158 (1964)CrossRefzbMATHGoogle Scholar
  6. D. Corbett, Reasoning and Unification Over Conceptual Graphs (Kluwer Academic/Plenum Publishers, New York, 2003)CrossRefzbMATHGoogle Scholar
  7. M.G. Dyer, In-Depth Understanding (The MIT Press, Cambridge, 1983)Google Scholar
  8. G. Ellis, Compiling conceptual graph. IEEE Trans. Knowl. Data Eng. 7, 68–81 (1995)CrossRefGoogle Scholar
  9. C.J. Fillmore, in Universals in Linguistic Theory, ed. by E. Bach, R.T. Harms. The Case for Case (Holt, Rinehart and Winston, New York, 1968), pp. 1–88Google Scholar
  10. R.V. Guha, D.B. Lenat, Enabling agents to work together. Commun. ACM 37(7), 127–142 (1994)CrossRefGoogle Scholar
  11. M.A.K. Halliday, R. Hasan, Cohesion in English (Longman, London, 1976)Google Scholar
  12. P. Hayes, IKL Guide (Florida Institute for Human & Machine Cognition (IHMC), Pensacola, 2006), http://www.ihmc.us/users/phayes/IKL/GUIDE/GUIDE.html. Accessed 28 Feb 2016
  13. P. Hayes, C. Menzel, IKL Specification Document (Florida Institute for Human & Machine Cognition (IHMC), Pensacola, 2006), http://www.ihmc.us/users/phayes/IKL/SPEC/SPEC.html. Accessed 28 Feb 2016
  14. Z. Hu, E. Rahimtoroghi, L. Munishkina, R. Swanson, M.A. Walker, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP ‘13), ed. by T. Baldwin, A. Korhonen. Unsupervised Induction of Contingent Event Pairs from Film Scenes (ACL, Stroudsburg, 2013), pp. 369–379Google Scholar
  15. International Organization for Standardization, ISO, Information Technology-Common Logic (CL): A Framework for a Family of Logic-based Languages (ISO/IEC 24707:2007) (ISO, Geneva, 2007)Google Scholar
  16. R. Jackendoff, Semantic Structures (The MIT Press, Cambridge, 1990)Google Scholar
  17. M. Jahn, Narratology: A Guide to the Theory of Narrative (version 1.8) (English Department of the University, Cologne, 2005), http://www.uni-koeln.de/~ame02/pppn.htm. Accessed 28 Feb 2016
  18. H. Kamp, U. Reyle, From Discourse to Logic. Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation (Kluwer, Dordrecht, 1993)Google Scholar
  19. S. Klarman, V. Gutiérrez-Basulto, in Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, ed. by W. Burgard, D. Roth. Two-Dimensional Description Logics for Context-Based Semantic Interoperability (AAAI Press, Menlo Park, 2011), pp. 215–220Google Scholar
  20. D. Klein, C.D. Manning, in Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, ed. by E. W. Hinrichs, D. Roth. Accurate Unlexicalized Parsing (ACL, Stroudsburg, 2003), pp. 423–430Google Scholar
  21. J.L. Kolodner, Retrieval and Organizational Strategies in Conceptual Memory: A Computer Model (Lawrence Erlbaum Associates, Hillsdale, 1984)Google Scholar
  22. K. Kozaki, E. Sunagawa, Y. Kitamura, R. Mizoguchi, in Proceedings of the 2nd Workshop on Roles and Relationships in Object Oriented Programming, Multiagent Systems and Ontologies, co-located with ECOOP 2007 (Technical Report 2007-9), ed. by G. Boella, S. Goebel, F. Steimann, S. Zschaler, M. Cebulla. Role Representation Model Using OWL and SWRL (Technische Universität Berlin, Berlin, 2007), pp. 39–46Google Scholar
  23. F. Lehmann (ed.), Semantic Networks in Artificial Intelligence (Pergamon Press, Oxford, 1992)Google Scholar
  24. D.B. Lenat, R.V. Guha, Building Large Knowledge Based Systems (Addison-Wesley, Reading, 1990)Google Scholar
  25. D.B. Lenat, R.V. Guha, K. Pittman, D. Pratt, M. Shepherd, CYC: toward programs with common sense. Commun. ACM 33(8), 30–49 (1990)CrossRefGoogle Scholar
  26. B. Levin, English Verb Classes and Alternation, A Preliminary Investigation (University Press, Chicago, 1993)Google Scholar
  27. W. Liu, Z. Liu, J. Fu, R. Hu, Z. Zhong, in Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems, ed. by L. Barolli, F. Xhafa, S. Vitabile, H.-H. Hsu. Extending OWL for Modeling Event-Oriented Ontology (IEEE Computer Society Press, Los Alamitos, 2010), pp. 581–586CrossRefGoogle Scholar
  28. I. Mani, J. Pustejovsky, in Proceedings of the ACL Workshop on Discourse Annotation, ed. by B. Webber, D. Byron. Temporal Discourse Models for Narrative Structure (Stroudsburg, ACL, 2004), pp. 57–64Google Scholar
  29. S. Matsuyoshi, M. Eguchi, C. Sao, K. Murakami, K. Inui, Y. Matsumoto, in Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, ed. by N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, D. Tapias. Annotating Event Mentions in Text with Modality, Focus, and Source Information (European Language Resources Association (ELRA), Paris, 2010), pp. 1456–1463Google Scholar
  30. J. McCarthy, in Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence – IJCAI/93, ed. by R. Bajcsy. Notes on Formalizing Context (Morgan Kaufmann, San Francisco, 1993), pp. 555–562Google Scholar
  31. R. Mizoguchi, E. Sunagawa, K. Kozaki, Y. Kitamura, A model of roles within an ontology development tool: Hozo. J. Appl. Ontol. 2, 159–179 (2007)Google Scholar
  32. Morris, J. and G. Hirst, G. (1991). Lexical cohesion computed by Thesaural relations as an indicator of the structure of text. Comput. Linguist. 17: 21-48.Google Scholar
  33. S. Nirenburg, V. Raskin, Ontological Semantics (The MIT Press, Cambridge, 2004)Google Scholar
  34. N.F. Noy, R.W. Fergerson, M.A. Musen, in Knowledge Acquisition, Modeling, and Management – Proceedings of EKAW 2000, ed. by R. Dieng, O. Corby. The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility, vol 1937 (Springer LNCS, Berlin, 2000), pp. 17–32Google Scholar
  35. M. Palmer, G. Gildea, N. Xue, Semantic Role Labeling (Morgan and Claypool Publishers, San Rafael, 2010)Google Scholar
  36. S. Pepper, The TAO of Topic Maps: Finding the Way in the Age of Infoglut (Ontopia AS, Oslo, 2000), http://www.ontopia.net/topicmaps/materials/tao.html. Accessed 28 Feb 2016
  37. A.G. Salguero, C. Delgado, F. Araque, in Computer Aided Systems Theory, 12th International Conference, EUROCAST 2009, ed. by R. Moreno Díaz, F. Pichler, A. Quesada Arencibia. Easing the Definition of N-Ary Relations for Supporting Spatio-Temporal Models in OWL, vol 5717 (Springer LNCS, Berlin, 2009), pp. 271–278CrossRefGoogle Scholar
  38. R.C. Schank, in Computer Models of Thought and Language, ed. by R. C. Schank, K. M. Colby. Identification of Conceptualizations Underlying Natural Language (W.H. Freeman and Co., San Francisco, 1973), pp. 187–247Google Scholar
  39. R.C. Schank, R.P. Abelson, Scripts, Plans, Goals and Understanding: An Inquiry into Human Knowledge Structures (Lawrence Erlbaum, Oxford, 1977)zbMATHGoogle Scholar
  40. A. Scherp, T. Franz, C. Saathoff, S. Staab, in Proceedings of the Fifth International Conference on Knowledge Capture, K-CAP ’09, ed. by Y. Gil, N. Noy. F – A Model of Events Based on the Foundational Ontology DOLCE+DnS Ultralite (ACM, New York, 2009), pp. 137–144CrossRefGoogle Scholar
  41. L.K. Schubert, C.H. Hwang, in Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language, ed. by L. Iwanska, S. C. Shapiro. Episodic Logic Meets Little Red Riding Hood: A Comprehensive, Natural Representation for Language Understanding (MIT/AAAI Press, Cambridge & Menlo Park, 2000), pp. 111–174Google Scholar
  42. S.C. Shapiro, in Associative Networks: Representation and Use of Knowledge by Computers, ed. by N. V. Findler. The SNePS Semantic Network Processing System (Academic Press, New York, 1979), pp. 179–203CrossRefGoogle Scholar
  43. J.F. Sowa, Conceptual Structures: Information Processing in Mind and Machine (Addison-Wesley, Reading, 1984)zbMATHGoogle Scholar
  44. J.F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations (Brooks Cole Publishing Co., Pacific Grove, 1999)Google Scholar
  45. J.F. Sowa, Review of “Computational Semantics” by Sergei Niremburg and Victor Raskin. Comput. Linguist. 31, 147–152 (2005)CrossRefGoogle Scholar
  46. W3C OWL Working Group (eds.), OWL 2 Web Ontology Language Document Overview, 2nd edn (W3C Recommendation 11 December 2012). W3C (2012), http://www.w3.org/TR/owl2-overview/. Accessed 28 Feb 2016
  47. G.P. Zarri, in Proceedings of the First International Conference on Applied Natural Language Processing, ed. by I. M. Kameny, B. T. Oshika. Automatic Representation of the Semantic Relationships Corresponding to a French Surface Expression (ACL, Stroudsburg, 1983), pp. 143–147CrossRefGoogle Scholar
  48. G.P. Zarri, Integrating the two main inference modes of NKRL, transformations and hypotheses. J. Data Semant. (JoDS) 4, 304–340 (2005)zbMATHGoogle Scholar
  49. G.P. Zarri, Representation and Management of Narrative Information, Theoretical Principles and Implementation (Springer, London, 2009)CrossRefGoogle Scholar
  50. G.P. Zarri, in Computational Models of Narratives – Papers from the AAAI 2010 Fall Symposium (Technical Report FS-10-04), ed. by M. A. Finlayson, P. Gervás, E. Mueller, S. Narayanan, P. Winston. Representing and Managing Narratives in a Computer-Suitable Form (AAAI Press, Menlo Park, 2010), pp. 73–80Google Scholar
  51. G.P. Zarri, in Proceedings of the 24th International Florida AI Research Society Conference, FLAIRS-24, ed. by R. C. Murray, P. M. McCarthy. Differentiating Between “Functional” and “Semantic” Roles in a High-Level Conceptual Data Modeling Language (AAAI Press, Menlo Park, 2011a), pp. 75–80Google Scholar
  52. G.P. Zarri, Knowledge representation and inference techniques to improve the management of gas and oil facilities. Knowl.-Based Syst. (KNOSYS) 24, 989–1003 (2011b)CrossRefGoogle Scholar
  53. G.P. Zarri, Advanced computational reasoning based on the NKRL conceptual model. Expert Syst. Appl. (ESWA) 40, 2872–2888 (2013)CrossRefGoogle Scholar
  54. G.P. Zarri, in Special Issue on Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis, ed. by A. Hussain, E. Cambria, B. Schuller, N. Howard. Sentiments Analysis at Conceptual Level Making Use of the Narrative Knowledge Representation Language, vol 58 (Neural Networks (NEUNET), 2014), pp. 82–97Google Scholar
  55. G.P. Zarri, in Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, FLAIRS-28, ed. by I. Russell, W. Eberle. The “Qua-Entities” Paradigm versus the Notion of “Role” in NKRL (Narrative Knowledge Representation Language) (AAAI Press, Menlo Park, 2015), pp. 97–102Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Sorbonne University, STIH LaboratoryParisFrance

Personalised recommendations