Skip to main content
SpringerLink
Log in
Book cover

International Conference on Database and Expert Systems Applications

DEXA 2022: Database and Expert Systems Applications pp 133–146Cite as

Reconciliation of Mental Concepts with Graph Neural Networks

  • Conference paper
  • First Online:

  • 756 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13427))

Abstract

In the digital age, knowledge processes can be formalized and simplified using task management systems. As they evolve, so must the underlying schemata to retain harmony and concurrency with the real world. In this work we present a graph neural network model that can help in reconciling these data. It can do so by leveraging a novel propagation rule that does not presume reciprocal dependency but is able to represent it still. Thereby it can predict structures in the form of usage links with high accuracy and assist in the reconstruction of missing information. We evaluate this model on a new knowledge management dataset and show that it is superior to traditional embedding methods. Further, we show that it outperforms related work in an established general link prediction task.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Publicly available at https://github.com/wendli01/abres_gcn/blob/master/team_ip_1.zip.

References

  1. Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)

    Article  Google Scholar 

  2. Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore art thou R3579X? Anonymized social networks, hidden patterns, and structural steganography. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 181–190. Association for Computing Machinery (2007)

    Google Scholar 

  3. Bergman, M.K.: Common sense view of knowledge graphs (2019)

    Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  5. Bresson, X., Laurent, T.: Residual gated graph convnets. arXiv preprint arXiv:1711.07553 (2017)

  6. Bürstenbinder, J., et al.: Auf dem Weg in die Wissens- und Informationsgesellschaft. In: Jung, V., Warnecke, H.J. (eds.) Handbuch für die Telekommunikation, pp. 1273–1410. Springer, Heidelberg (2002). https://doi.org/10.1007/978-3-642-55450-6_6

    Chapter  Google Scholar 

  7. Drucker, P.F.: Landmarks of Tomorrow: A Report on the New “Post-Modern’’ World. Harper & Brothers, New York (1959)

    Google Scholar 

  8. Drucker, P.F.: Knowledge-worker productivity: the biggest challenge. Calif. Manag. Rev. 41(2), 79–94 (1999). https://doi.org/10.2307/41165987

    Article  Google Scholar 

  9. Giles, C.L., Bollacker, K.D., Lawrence, S.: CiteSeer: an automatic citation indexing system. In: Proceedings of the Third ACM Conference on Digital Libraries, pp. 89–98 (1998)

    Google Scholar 

  10. Greco, P.: The knowledge society. J. Sci. Commun. 06(04), C01 (2007). https://doi.org/10.22323/2.06040301. https://jcom.sissa.it/archive/06/04/Jcom0604(2007)C01

  11. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  13. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

  14. Hübscher, G., et al.: Graph-based managing and mining of processes and data in the domain of intellectual property. Inf. Syst. 106, 101844 (2022). https://doi.org/10.1016/j.is.2021.101844

    Article  Google Scholar 

  15. Hübscher, G., Geist, V., Auer, D., Hübscher, N., Küng, J.: Integration of knowledge and task management in an evolving, communication-intensive environment. In: ACM (ed.) The 22nd International Conference on Information Integration and Web-Based Applications & Services (iiWAS 2020), pp. 407–416. ACM (2020). https://doi.org/10.1145/3428757.3429260

  16. Hübscher, G., Geist, V., Auer, D., Hübscher, N., Küng, J.: Representation and presentation of knowledge and processes - an integrated approach for a dynamic communication-intensive environment. Int. J. Web Inf. Syst. 17(6), 669–697 (2021). https://doi.org/10.1108/IJWIS-03-2021-0031

    Article  Google Scholar 

  17. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  18. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  19. Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)

  20. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

  21. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119 (2013)

    Google Scholar 

  22. Nonaka, I.: The knowledge-creating company: reprint of the 1991 article, managing for the long term, best of HBR, Nov.–Dec. 1991. Harv. Bus. Rev. 162–171 (2007)

    Google Scholar 

  23. Nonaka, I., Takeuchi, H.: The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, Oxford (1995)

    Google Scholar 

  24. Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)

    Google Scholar 

  25. Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization: unifying DeepWalk, LINE, PTE, and node2vec. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 459–467 (2018)

    Google Scholar 

  26. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    Article  Google Scholar 

  27. Salha, G., Limnios, S., Hennequin, R., Tran, V.A., Vazirgiannis, M.: Gravity-inspired graph autoencoders for directed link prediction. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 589–598 (2019)

    Google Scholar 

  28. Steinbicker, J.: Zur Theorie der Informationsgesellschaft: Ein Vergleich der Ansätze von Peter Drucker, Daniel Bell und Manuel Castells. Lehrtexte Soziologie, Leske + Budrich, Opladen (2001)

    Google Scholar 

  29. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)

  30. Wang, X., Vinel, A.: Benchmarking graph neural networks on link prediction. arXiv preprint arXiv:2102.12557 (2021)

  31. Yu, Y., Wang, X.: Link prediction in directed network and its application in microblog. Math. Probl. Eng. 2014 (2014)

    Google Scholar 

  32. Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)

    Article  Google Scholar 

  33. Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009). https://doi.org/10.1140/epjb/e2009-00335-8

    Article  MATH  Google Scholar 

Download references

Acknowledgements

The research reported in this paper has been supported by the FFG BRIDGE project KnoP-2D (grant no. 871299).

Author information

Authors and Affiliations

  1. Universität Passau, Passau, Germany

    Lorenz Wendlinger & Michael Granitzer

  2. Hübscher & Partner Patentanwälte GmbH, Linz, Austria

    Gerd Hübscher

  3. SBA Research, Floragasse 7, Vienna, Austria

    Andreas Ekelhart

Authors
  1. Lorenz Wendlinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerd Hübscher
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andreas Ekelhart
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Granitzer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lorenz Wendlinger .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wendlinger, L., Hübscher, G., Ekelhart, A., Granitzer, M. (2022). Reconciliation of Mental Concepts with Graph Neural Networks. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13427. Springer, Cham. https://doi.org/10.1007/978-3-031-12426-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-12426-6_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12425-9

  • Online ISBN: 978-3-031-12426-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation