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A*-Based Similarity Assessment of Semantic Graphs

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Case-Based Reasoning Research and Development (ICCBR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12311))

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Abstract

The similarity assessment of graphs is a fundamental problem that is particularly challenging if efficiency is of core importance. In this paper, we focus on a similarity measure for semantically labeled graphs whose labels are composed in an object-oriented manner. The measure is based on A* search and is particularly suited for case-based reasoning as it can be combined with knowledge-intensive local similarity measures and outputs similarities and corresponding mappings usable for explanation and adaptation. However, particularly for large graphs, the search space must be pruned to improve efficiency of A* search at the cost of sacrificing global optimality. We address this issue and present complementary improvements of the measure, which we systematically evaluate for the similarity assessment of semantic workflow graphs. The experimental results demonstrate that the new measure considerably reduces the computation time and memory consumption while increasing the accuracy.

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Notes

  1. 1.

    For implementation details, please refer to  [3].

References

  1. Bergmann, R. (ed.): Experience Management. LNCS, vol. 2432. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45759-3

    Book  MATH  Google Scholar 

  2. Bergmann, R., Gil, Y.: Similarity assessment and efficient retrieval of semantic workflows. Inf. Syst. 40, 115–127 (2014)

    Article  Google Scholar 

  3. Bergmann, R., Grumbach, L., Malburg, L., Zeyen, C.: ProCAKE: aprocess-oriented case-based reasoning framework. In: Case-Based Reasoning Research and Development: 27th ICCBR 2019, Workshop Proceedings (2019)

    Google Scholar 

  4. Bergmann, R., Lenz, M., Ollinger, S., Pfister, M.: Similarity measures for case-based retrieval of natural language argument graphs in argumentation machines. In: Barták, R., Brawner, K.W. (eds.) Proceedings of the Thirty-Second FLAIRS Conference, 2019, pp. 329–334. AAAI Press (2019)

    Google Scholar 

  5. Bergmann, R., Müller, G.: Similarity-based retrieval and automatic adaptation of semantic workflows. In: Nalepa, G.J., Baumeister, J. (eds.) Synergies Between Knowledge Engineering and Software Engineering. AISC, vol. 626, pp. 31–54. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-64161-4_2

    Chapter  Google Scholar 

  6. Burkhard, H.D., Richter, M.M.: On the notion of similarity in case-based reasoning and fuzzy theory. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.) Soft Computing in Case-Based Reasoning, pp. 29–45. Springer, London (2001). https://doi.org/10.1007/978-1-4471-0687-6_2

    Chapter  MATH  Google Scholar 

  7. Champin, P.-A., Solnon, C.: Measuring the similarity of labeled graphs. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 80–95. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45006-8_9

    Chapter  Google Scholar 

  8. Hoffmann, M., Malburg, L., Klein, P., Bergmann, R.: Using siamese graph neural networks for similarity-based retrieval in process-oriented case-based reasoning. In: Case-Based Reasoning Research and Development: 28th ICCBR 2020. LNCS, Springer (2020). Accepted for publication

    Google Scholar 

  9. Kendall-Morwick, J., Leake, D.: A study of two-phase retrieval for process-oriented case-based reasoning. Stud. Comput. Intell. 494, 7–27 (2014)

    Google Scholar 

  10. Klein, P., Malburg, L., Bergmann, R.: Learning workflow embeddings to improve the performance of similarity-based retrieval for process-oriented case-based reasoning. In: Bach, K., Marling, C. (eds.) ICCBR 2019. LNCS (LNAI), vol. 11680, pp. 188–203. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29249-2_13

    Chapter  Google Scholar 

  11. Li, Y., Gu, C., Dullien, T., Vinyals, O., Kohli, P.: Graph matching networks for learning the similarity of graph structured objects. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019. Proceedings of Machine Learning Research, vol. 97, pp. 3835–3845. PMLR (2019)

    Google Scholar 

  12. Ontañón, S.: An overview of distance and similarity functions forstructured data. CoRR abs/2002.07420 (2020). http://arxiv.org/abs/2002.07420

  13. Shemshadi, A., Sheng, Q.Z., Qin, Y.: Efficient pattern matching for graphs with multi-labeled nodes. Knowl. Based Syst. 109, 256–265 (2016)

    Article  Google Scholar 

  14. Sorlin, S., Solnon, C.: Reactive tabu search for measuring graph similarity. In: Brun, L., Vento, M. (eds.) GbRPR 2005. LNCS, vol. 3434, pp. 172–182. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31988-7_16

    Chapter  MATH  Google Scholar 

  15. Zhu, L., Ng, W.K., Cheng, J.: Structure and attribute index for approximate graph matching in large graphs. Inf. Syst. 36(6), 958–972 (2011)

    Article  Google Scholar 

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Acknowledgements

This work is funded by the German Research Foundation (DFG) under grant No. BE 1373/3-3 and grant No. 375342983.

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Authors and Affiliations

  1. Business Information Systems II, University of Trier, 54286, Trier, Germany

    Christian Zeyen & Ralph Bergmann

  2. German Research Center for Artificial Intelligence (DFKI), Branch University of Trier, Behringstraße 21, 54296, Trier, Germany

    Ralph Bergmann

Authors
  1. Christian Zeyen
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  2. Ralph Bergmann
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Corresponding author

Correspondence to Christian Zeyen .

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Editors and Affiliations

  1. School of Computer Science, University of Auckland, Auckland, New Zealand

    Ian Watson

  2. College of Computing and Informatics, Drexel University, Philadelphia, PA, USA

    Rosina Weber

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Zeyen, C., Bergmann, R. (2020). A*-Based Similarity Assessment of Semantic Graphs. In: Watson, I., Weber, R. (eds) Case-Based Reasoning Research and Development. ICCBR 2020. Lecture Notes in Computer Science(), vol 12311. Springer, Cham. https://doi.org/10.1007/978-3-030-58342-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-58342-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58341-5

  • Online ISBN: 978-3-030-58342-2

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