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International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems

IPMU 2022: Information Processing and Management of Uncertainty in Knowledge-Based Systems pp 593–606Cite as

Generating Contextual Weighted Commonsense Knowledge Graphs

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1601))

Abstract

There has been a renewed interest in commonsense knowledge and reasoning. To achieve artificial general intelligence, systems must exhibit not only the recognition abilities of humans but also other important aspects of being human, such as commonsense and causality. Recent literature has shown that external commonsense knowledge graphs are beneficial to a variety of systems in multiple ways, including improvements in the commonsense abilities of deep learning models. This paper investigates an auto-generation of weighted commonsense knowledge graphs representing general information, as well as graphs containing contextual information. The method leads to the construction of graphs equipped with frequency based weights associated with nodes and relations. The proposed construction methodology has the advantage of a never-ending learning paradigm. We evaluate the constructed contextual knowledge graphs qualitatively and quantitatively. The commonsense knowledge graphs are inherently explainable and can support commonsense reasoning. We analyze commonsense reasoning approaches using contextual graphs and discuss the results.

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References

  1. Benferhat, S., Dubois, D., Garcia, L., Prade, H.: Possibilistic logic bases and possibilistic graphs. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 57–64. UAI 1999. Morgan Kaufmann Publishers Inc., San Francisco (1999)

    Google Scholar 

  2. Chamorro-Martínez, J., et al.: Referring expression generation from images via deep learning object extraction and fuzzy graphs. In: 2021 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE, pp. 1–6 (2021)

    Google Scholar 

  3. Chen, X., Shrivastava, A., Gupta, A.: NEIL: extracting visual knowledge from web data. In: 2013 IEEE International Conference on Computer Vision, pp. 1409–1416 (2013)

    Google Scholar 

  4. Divvala, S.K., Farhadi, A., Guestrin, C.: Learning everything about anything: webly-supervised visual concept learning. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3270–3277 (2014)

    Google Scholar 

  5. Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Springer, US (1988)

    MATH  Google Scholar 

  6. Dubois, D., Prade, H., Smets, P.: new semantics for quantitative possibility theory. In: Benferhat, S., Besnard, P. (eds.) ECSQARU 2001. LNCS (LNAI), vol. 2143, pp. 410–421. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44652-4_36

    Chapter  MATH  Google Scholar 

  7. Hayes, A.F., Krippendorff, K.: Answering the call for a standard reliability measure for coding data. Commun. Methods Meas. 1, 77–89 (2007)

    Article  Google Scholar 

  8. Hwang, J.D., et al.: COMET-ATOMIC 2020: on symbolic and neural commonsense knowledge graphs. In: The Thirty-Fifth AAAI Conference on Artificial Intelligence, pp. 6384–6392 (2021)

    Google Scholar 

  9. LeCun, Y.: Yann LeCun on a vision to make AI systems learn and reason like animals and humans. https://ai.facebook.com/blog/yann-lecun-advances-in-ai-research/

  10. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, pp. 255–258. MIT Press, Cambridge (1998)

    Google Scholar 

  11. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  12. McCarthy, J., Lifschitz, V.: Formalizing Common Sense. Ablex series in artificial intelligence. Ablex Publishing Corporation (1990)

    Google Scholar 

  13. Prade, H.: Data bases with fuzzy information and approximate reasoning in expert systems. In: IFAC Proceedings, vol. 16, pp. 113–119 (1983)

    Google Scholar 

  14. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2015)

    Article  Google Scholar 

  15. Rezaei, N., Reformat, M.Z., Yager, R.R.: Image-based world-perceiving knowledge graph (WpKG) with imprecision. In: Lesot, M.-J., et al. (eds.) IPMU 2020. CCIS, vol. 1237, pp. 415–428. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50146-4_31

    Chapter  Google Scholar 

  16. Romero, J., Razniewski, S., Pal, K., Z. Pan, J., Sakhadeo, A., Weikum, G.: Commonsense properties from query logs and question answering forums. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1411–1420. Association for Computing Machinery (2019)

    Google Scholar 

  17. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)

    Google Scholar 

  18. Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: an open multilingual graph of general knowledge. In: Thirty-first AAAI Conference on Artificial Intelligence, pp. 4444–4451 (2017)

    Google Scholar 

  19. Tandon, N., de Melo, G., Weikum, G.: WebChild 2.0: fine-grained commonsense knowledge distillation. In: Proceedings of ACL 2017, System Demonstrations, pp. 115–120. Association for Computational Linguistics, Vancouver, July 2017

    Google Scholar 

  20. Tang, K., Niu, Y., Huang, J., Shi, J., Zhang, H.: Unbiased scene graph generation from biased training. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 3713–3722 (2020)

    Google Scholar 

  21. West, P., et al.: Symbolic knowledge distillation: from general language models to commonsense models (2021). arXiv preprint arXiv:2110.07178

  22. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 5987–5995 (2017)

    Google Scholar 

  23. Zellers, R., Bisk, Y., Farhadi, A., Choi, Y.: From recognition to cognition: visual commonsense reasoning. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp. 6713–6724 (2019)

    Google Scholar 

  24. Zellers, R., Yatskar, M., Thomson, S., Choi, Y.: Neural motifs: scene graph parsing with global context. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5831–5840 (2018)

    Google Scholar 

  25. Zhang, H., Khashabi, D., Song, Y., Roth, D.: TransOMCS: from linguistic graphs to commonsense knowledge. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 4004–4010 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Alberta, Edmonton, T6G 1H9, Canada

    Navid Rezaei & Marek Z. Reformat

  2. University of Social Sciences, 90-113, Łódź, Poland

    Marek Z. Reformat

  3. Iona College, New York, 10801, USA

    Ronald R. Yager

  4. King Abdulaziz University, Jeddah, Saudi Arabia

    Ronald R. Yager

Authors
  1. Navid Rezaei
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  2. Marek Z. Reformat
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  3. Ronald R. Yager
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Corresponding author

Correspondence to Navid Rezaei .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Oviedo, Oviedo, Spain

    Inés Couso

  3. University of Cádiz, Cádiz, Spain

    Jesús Medina

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. University of Perugia, Perugia, Italy

    Davide Petturiti

  6. Sorbonne Université, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, NY, USA

    Ronald R. Yager

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Rezaei, N., Reformat, M.Z., Yager, R.R. (2022). Generating Contextual Weighted Commonsense Knowledge Graphs. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1601. Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_49

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  • DOI: https://doi.org/10.1007/978-3-031-08971-8_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08970-1

  • Online ISBN: 978-3-031-08971-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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