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Building Narrative Structures from Knowledge Graphs

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The Semantic Web: ESWC 2022 Satellite Events (ESWC 2022)

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Abstract

Humans constantly create narratives to provide explanations for how and why something happens. Designing systems able to build such narratives would therefore contribute to building more human-centric systems, and to support uses like decision-making processes. Here, a narrative is seen as a sequence of events. My thesis investigates how a narrative can be built computationally. Four research questions are identified: representation, construction, link prediction and evaluation. A case study on the French Revolution, based upon Wikidata and Wikipedia is presented. This prototype helps identifying the first challenges such as dynamic representation and evaluation of a narrative.

figure a

The work reported in this paper was funded by the European MUHAI project from the Horizon 2020 research and innovation programme under grant number 951846 and the Sony Computer Science Laboratories Paris.

I. Blin—Early Stage Ph.D. (First Year).

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Notes

  1. 1.

    https://www.wikidata.org/wiki/Q6534.

References

  1. Abu-Salih, B.: Domain-specific knowledge graphs: a survey. J. Netw. Comput. Appl. 185, 103076 (2021)

    Google Scholar 

  2. Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)

    Article  Google Scholar 

  3. Bakalara, J., Guyet, T., Dameron, O., Happe, A., Oger, E.: An extension of chronicles temporal model with taxonomies-application to epidemiological studies. In: 14th International Conference on Health Informatics, HEALTHINF 2021, pp. 1–10 (2021)

    Google Scholar 

  4. Camacho Barranco, R., Boedihardjo, A.P., Hossain, M.S.: Analyzing evolving stories in news articles. Int. J. Data Sci. Anal. 8(3), 241–256 (2017). https://doi.org/10.1007/s41060-017-0091-9

    Article  Google Scholar 

  5. Bellan, P., Dragoni, M., Ghidini, C.: Process extraction from text: state of the art and challenges for the future. arXiv preprint arXiv:2110.03754 (2021)

  6. Bloem, P., de Rooij, S.: Large-scale network motif analysis using compression. Data Min. Knowl. Disc. 34(5), 1421–1453 (2020). https://doi.org/10.1007/s10618-020-00691-y

    Article  MathSciNet  MATH  Google Scholar 

  7. de Boer, V., Melgar, L., Inel, O., Ortiz, C.M., Aroyo, L., Oomen, J.: Enriching media collections for event-based exploration. In: Garoufallou, E., Virkus, S., Siatri, R., Koutsomiha, D. (eds.) MTSR 2017. CCIS, vol. 755, pp. 189–201. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70863-8_18

    Chapter  Google Scholar 

  8. Bosselut, A., Rashkin, H., Sap, M., Malaviya, C., Celikyilmaz, A., Choi, Y.: COMET: commonsense transformers for automatic knowledge graph construction. arXiv preprint arXiv:1906.05317 (2019)

  9. Boyd, B.: On the Origin of Stories: Evolution, Cognition, and Fiction. Harvard University Press (2010)

    Google Scholar 

  10. Cai, L., Janowicz, K., Yan, B., Zhu, R., Mai, G.: Time in a box: advancing knowledge graph completion with temporal scopes. In: Proceedings of the 11th on Knowledge Capture Conference, pp. 121–128 (2021)

    Google Scholar 

  11. Campos, R., Jorge, A., Jatowt, A., Bhatia, S.: The 3rd international workshop on narrative extraction from texts: Text2Story 2020. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 648–653. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45442-5_86

    Chapter  Google Scholar 

  12. Caselli, T., Hovy, E., Palmer, M., Vossen, P.: Computational Analysis of Storylines: Making Sense of Events. Cambridge University Press (2021)

    Google Scholar 

  13. Caselli, T., Vossen, P.: The event storyline corpus: a new benchmark for causal and temporal relation extraction. In: Proceedings of the Events and Stories in the News Workshop, pp. 77–86 (2017)

    Google Scholar 

  14. Cochez, M., Ristoski, P., Ponzetto, S.P., Paulheim, H.: Biased graph walks for RDF graph embeddings. In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, pp. 1–12 (2017)

    Google Scholar 

  15. Del Mondo, G., Peng, P., Gensel, J., Claramunt, C., Lu, F.: Leveraging spatio-temporal graphs and knowledge graphs: perspectives in the field of maritime transportation. ISPRS Int. J. Geo Inf. 10(8), 541 (2021)

    Article  Google Scholar 

  16. Devezas, J., Nunes, S.: A review of graph-based models for entity-oriented search. SN Comput. Sci. 2(6), 1–36 (2021)

    Article  Google Scholar 

  17. Elazar, Y., Basmov, V., Goldberg, Y., Tsarfaty, R.: Text-based np enrichment. arXiv preprint arXiv:2109.12085 (2021)

  18. Evans, J.S.B.: Heuristic and analytic processes in reasoning. Br. J. Psychol. 75(4), 451–468 (1984)

    Article  Google Scholar 

  19. Evans, J.S.B.: In two minds: dual-process accounts of reasoning. Trends Cogn. Sci. 7(10), 454–459 (2003)

    Article  Google Scholar 

  20. Evans, J.S.B.: Dual-processing accounts of reasoning, judgment, and social cognition. Annu. Rev. Psychol. 59, 255–278 (2008)

    Article  Google Scholar 

  21. Gottschalk, S., Demidova, E.: EventKG-the hub of event knowledge on the web-and biographical timeline generation. Semant. Web 10(6), 1039–1070 (2019)

    Article  Google Scholar 

  22. Gottschalk, S., Demidova, E.: HapPenIng: happen, predict, infer—event series completion in a knowledge graph. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 200–218. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_12

    Chapter  Google Scholar 

  23. Gottschall, J.: The Storytelling Animal: How Stories Make Us Human. Houghton Mifflin Harcourt (2012)

    Google Scholar 

  24. Han, Z., Chen, P., Ma, Y., Tresp, V.: Explainable subgraph reasoning for forecasting on temporal knowledge graphs. In: International Conference on Learning Representations (2020)

    Google Scholar 

  25. Heist, N., Hertling, S., Ringler, D., Paulheim, H.: Knowledge graphs on the web-an overview (2020)

    Google Scholar 

  26. Huang, C.Y., Huang, T.H.: Semantic frame forecast. arXiv preprint arXiv:2104.05604 (2021)

  27. Hyvönen, E., Rantala, H., et al.: Knowledge-based relation discovery in cultural heritage knowledge graphs. In: Digital Humanities in Nordic Countries Proceedings of the Digital Humanities in the Nordic Countries 4th Conference. CEUR-WS.org (2019)

    Google Scholar 

  28. Jia, Z., Abujabal, A., Saha Roy, R., Strötgen, J., Weikum, G.: TempQuestions: a benchmark for temporal question answering. In: Companion Proceedings of the the Web Conference 2018, pp. 1057–1062 (2018)

    Google Scholar 

  29. Jia, Z., Pramanik, S., Saha Roy, R., Weikum, G.: Complex temporal question answering on knowledge graphs. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 792–802 (2021)

    Google Scholar 

  30. Jung, J., Jung, J., Kang, U.: Learning to walk across time for interpretable temporal knowledge graph completion. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 786–795 (2021)

    Google Scholar 

  31. Kawamura, T., et al.: Report on the first knowledge graph reasoning challenge 2018. In: Wang, X., Lisi, F.A., Xiao, G., Botoeva, E. (eds.) JIST 2019. LNCS, vol. 12032, pp. 18–34. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-41407-8_2

    Chapter  Google Scholar 

  32. Kroll, H., Nagel, D., Balke, W.-T.: Modeling narrative structures in logical overlays on top of knowledge repositories. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds.) ER 2020. LNCS, vol. 12400, pp. 250–260. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62522-1_18

    Chapter  Google Scholar 

  33. Lal, Y.K., Chambers, N., Mooney, R., Balasubramanian, N.: TellMeWhy: a dataset for answering why-questions in narratives. arXiv preprint arXiv:2106.06132 (2021)

  34. Lecue, F.: On the role of knowledge graphs in explainable AI. Semant. Web 11(1), 41–51 (2020)

    Article  Google Scholar 

  35. Li, Z., Ding, X., Liu, T.: Constructing narrative event evolutionary graph for script event prediction. arXiv preprint arXiv:1805.05081 (2018)

  36. Li, Z., et al.: Search from history and reason for future: two-stage reasoning on temporal knowledge graphs. arXiv preprint arXiv:2106.00327 (2021)

  37. Liao, S., Liang, S., Meng, Z., Zhang, Q.: Learning dynamic embeddings for temporal knowledge graphs. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 535–543 (2021)

    Google Scholar 

  38. Meghini, C., Bartalesi, V., Metilli, D.: Representing narratives in digital libraries: the narrative ontology. Semant. Web (Preprint) 1–24 (2021)

    Google Scholar 

  39. Miller, B., Lieto, A., Ronfard, R., Ware, S., Finlayson, M.: Proceedings of the 7th workshop on computational models of narrative. In: 7th Workshop on Computational Models of Narrative (CMN 2016), vol. 53 (2016)

    Google Scholar 

  40. Mori, Y., Yamane, H., Mukuta, Y., Harada, T.: Finding and generating a missing part for story completion. In: Proceedings of the the 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 156–166 (2020)

    Google Scholar 

  41. Mostafazadeh, N., et al.: A corpus and evaluation framework for deeper understanding of commonsense stories. arXiv preprint arXiv:1604.01696 (2016)

  42. Narayanan, S.: Reasoning about actions in narrative understanding. In: IJCAI, vol. 99, pp. 350–357. Citeseer (1999)

    Google Scholar 

  43. Oza, P., Dietz, L.: Which entities are relevant for the story? In: Text2Story@ ECIR, pp. 41–48 (2021)

    Google Scholar 

  44. Powell, J.M., Thyne, C.L.: Global instances of coups from 1950 to 2010: a new dataset. J. Peace Res. 48(2), 249–259 (2011)

    Article  Google Scholar 

  45. Raad, J., Cruz, C.: A survey on ontology evaluation methods. In: Proceedings of the International Conference on Knowledge Engineering and Ontology Development, Part of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (2015)

    Google Scholar 

  46. Radstok, W., Chekol, M., Velegrakis, Y.: Leveraging static models for link prediction in temporal knowledge graphs. In: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1034–1041. IEEE (2021)

    Google Scholar 

  47. Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)

  48. Reese, J.T., et al.: KG-Covid-19: a framework to produce customized knowledge graphs for Covid-19 response. Patterns 2(1), 100155 (2021)

    Google Scholar 

  49. Rospocher, M., et al.: Building event-centric knowledge graphs from news. J. Web Semant. 37, 132–151 (2016)

    Article  Google Scholar 

  50. Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., Bronstein, M.: Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637 (2020)

  51. Rudolph, M., Blei, D.: Dynamic embeddings for language evolution. In: Proceedings of the 2018 World Wide Web Conference, pp. 1003–1011 (2018)

    Google Scholar 

  52. Sap, M., et al.: Atomic: an atlas of machine commonsense for if-then reasoning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3027–3035 (2019)

    Google Scholar 

  53. Sap, M., Rashkin, H., Chen, D., LeBras, R., Choi, Y.: SocialIQA: commonsense reasoning about social interactions. arXiv preprint arXiv:1904.09728 (2019)

  54. Schäfer, B.: Exploiting DBpedia for graph-based entity linking to Wikipedia. Ph.D. thesis (2014)

    Google Scholar 

  55. Shu, T., et al.: Agent: a benchmark for core psychological reasoning. arXiv preprint arXiv:2102.12321 (2021)

  56. Sloman, S.A.: The empirical case for two systems of reasoning. Psychol. Bull. 119(1), 3 (1996)

    Article  Google Scholar 

  57. Steels, L.: Conceptual Foundations of Human-Centric AI (2022)

    Google Scholar 

  58. Teru, K., Denis, E., Hamilton, W.: Inductive relation prediction by subgraph reasoning. In: International Conference on Machine Learning, pp. 9448–9457. PMLR (2020)

    Google Scholar 

  59. Tiddi, I., Daga, E., Bastianelli, E., d’Aquin, M.: Update of time-invalid information in knowledge bases through mobile agents (2016)

    Google Scholar 

  60. Tiddi, I., d’Aquin, M., Motta, E.: Walking linked data: a graph traversal approach to explain clusters (2014)

    Google Scholar 

  61. Traverso-Ribón, I., Palma, G., Flores, A., Vidal, M.-E.: Considering semantics on the discovery of relations in knowledge graphs. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 666–680. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49004-5_43

    Chapter  Google Scholar 

  62. Van Hage, W.R., Malaisé, V., Segers, R., Hollink, L., Schreiber, G.: Design and use of the simple event model (SEM). J. Web Semant. 9(2), 128–136 (2011)

    Article  Google Scholar 

  63. Vilarroya, Ó.: Somos lo que nos contamos. Cómo los relatos construyen el mundo en que vivimos. Editorial Ariel, Barcelona (2019)

    Google Scholar 

  64. Wewer, C., Lemmerich, F., Cochez, M.: Updating embeddings for dynamic knowledge graphs. arXiv preprint arXiv:2109.10896 (2021)

  65. Xu, C., Chen, Y.Y., Nayyeri, M., Lehmann, J.: Temporal knowledge graph completion using a linear temporal regularizer and multivector embeddings. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2569–2578 (2021)

    Google Scholar 

  66. Yang, X., Tiddi, I.: Creative storytelling with language models and knowledge graphs. In: CIKM (Workshops) (2020)

    Google Scholar 

  67. Zeng, C., Li, S., Li, Q., Hu, J., Hu, J.: A survey on machine reading comprehension-tasks, evaluation metrics and benchmark datasets. Appl. Sci. 10(21), 7640 (2020)

    Article  Google Scholar 

  68. Zhang, M., Ye, K., Hwa, R., Kovashka, A.: Story completion with explicit modeling of commonsense knowledge. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 376–377 (2020)

    Google Scholar 

  69. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, vol. 31, pp. 5165–5175 (2018)

    Google Scholar 

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Acknowledgements

I thank Annette ten Teije (VUA), Ilaria Tiddi (VUA), and Remi van Trijp (CSL) for their comments and feedbacks on this paper. I also thank Frank van Harmelen (VUA) for valuable advice and discussion. I thank David Colliaux (CSL), Michael Anslow (CSL), Martina Galletti (CSL) and Adam Dahlgren (Umeå University) for interesting discussion and feedbacks.

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Correspondence to Inès Blin .

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Appendices

A Predicates Selected for Each Narrative Dimension

(See Figs. 3, 4, 5 and 6)

Fig. 3.
figure 3

Selected predicates for each of the narrative dimension in Wikidata.

Fig. 4.
figure 4

Selected predicates for each of the narrative dimension in Wikipedia.

B Example of One Event Construction

Fig. 5.
figure 5

Wikidata and Wikipedia page content used to build an event representation for 13 Vendmiaire. Dashed lines indicates predicates or keys that were used, and full lines values. For clarity in visualisation, not all predicates related to the narrative dimensions were used, but only a subset of them.

Fig. 6.
figure 6

Event representation at different steps: using Wikidata outgoing links of the event (a) and Wikipedia infoboxes (b). On (b), green edges on the right indicate edges and nodes that were newly added with the Infobox. Refer to Figure from [62] for the original example. (Color figure online)

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Blin, I. (2022). Building Narrative Structures from Knowledge Graphs. In: Groth, P., et al. The Semantic Web: ESWC 2022 Satellite Events. ESWC 2022. Lecture Notes in Computer Science, vol 13384. Springer, Cham. https://doi.org/10.1007/978-3-031-11609-4_38

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