[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Semantic Web-Based Interoperability for Intelligent Agents with PSyKE

  • Conference paper
  • First Online:
Explainable and Transparent AI and Multi-Agent Systems (EXTRAAMAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13283))

Abstract

Modern distributed systems require communicating agents to agree on a shared, formal semantics for the data they exchange and operate upon. The Semantic Web offers tools to encode semantics in the form of ontologies, where data is represented in the form of knowledge graphs (KG). Applying such tools to intelligent agents equipped with machine learning (ML) capabilities is of particular interest, as it may enable a higher degree of interoperability among heterogeneous agents. Indeed, inputs and outputs of ML models can be formalised through ontologies, while the data they operate upon can be represented as KG.

In this paper we explore the combination of Semantic Web tools with knowledge extraction—that is, a research line aimed at extracting intelligible rules mimicking the behaviour of ML predictors, with the purpose of explaining their behaviour. Along this line, we study whether and to what extent ontologies and KG can be exploited as both the source and the outcome of a rule extraction procedure. In other words, we investigate the extraction of semantic rules out of sub-symbolic predictors trained upon data as KG—possibly adhering to some ontology. In doing so, we extend our PSyKE framework for rule extraction with Semantic Web support. In practice, we make PSyKE able to (i) train ML predictors out of OWL ontologies and RDF knowledge graphs, and (ii) extract semantic knowledge out of them, in the form of SWRL rules. A discussion among the major benefits and issues of our approach is provided, along with a description of the overall workflow.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • 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

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://owlready2.readthedocs.io [Online; last accessed February 28, 2022].

  2. 2.

    https://archive.ics.uci.edu/ml/datasets/iris [Online; last accessed 5 March 2022].

References

  1. Andrews, R., Diederich, J., Tickle, A.B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl.-Based Syst. 8(6), 373–389 (1995). https://doi.org/10.1016/0950-7051(96)81920-4

    Article  Google Scholar 

  2. Azcarraga, A., Liu, M.D., Setiono, R.: Keyword extraction using backpropagation neural networks and rule extraction. In: The 2012 International Joint Conference on Neural Networks (IJCNN 2012), pp. 1–7. IEEE (2012). https://doi.org/10.1109/IJCNN.2012.6252618

  3. Baesens, B., Setiono, R., De Lille, V., Viaene, S., Vanthienen, J.: Building credit-risk evaluation expert systems using neural network rule extraction and decision tables. In: Storey, V.C., Sarkar, S., DeGross, J.I. (eds.) ICIS 2001 Proceedings, pp. 159–168. Association for Information Systems (2001). http://aisel.aisnet.org/icis2001/20

  4. Baesens, B., Setiono, R., Mues, C., Vanthienen, J.: Using neural network rule extraction and decision tables for credit-risk evaluation. Manage. Sci. 49(3), 312–329 (2003). https://doi.org/10.1287/mnsc.49.3.312.12739

    Article  MATH  Google Scholar 

  5. Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 34–43 (2001). https://www.scientificamerican.com/article/the-semantic-web/

  6. Bologna, G., Pellegrini, C.: Three medical examples in neural network rule extraction. Phys. Med. 13, 183–187 (1997). https://archive-ouverte.unige.ch/unige:121360

  7. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press (1984)

    Google Scholar 

  8. Ciatto, G., Calegari, R., Omicini, A., Calvaresi, D.: Towards XMAS: eXplainability through Multi-Agent Systems. In: Savaglio, C., Fortino, G., Ciatto, G., Omicini, A. (eds.) AI &IoT 2019 - Artificial Intelligence and Internet of Things 2019, CEUR Workshop Proceedings, vol. 2502, pp. 40–53. Sun SITE Central Europe, RWTH Aachen University (2019), http://ceur-ws.org/Vol-2502/paper3.pdf

  9. Ciatto, G., Schumacher, M.I., Omicini, A., Calvaresi, D.: Agent-based explanations in AI: towards an abstract framework. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds.) EXTRAAMAS 2020. LNCS (LNAI), vol. 12175, pp. 3–20. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51924-7_1

    Chapter  Google Scholar 

  10. Craven, M.W., Shavlik, J.W.: Using sampling and queries to extract rules from trained neural networks. In: Machine Learning Proceedings 1994, pp. 37–45. Elsevier (1994). https://doi.org/10.1016/B978-1-55860-335-6.50013-1

  11. Craven, M.W., Shavlik, J.W.: Extracting tree-structured representations of trained networks. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems 8. Proceedings of the 1995 Conference, pp. 24–30. The MIT Press (1996). http://papers.nips.cc/paper/1152-extracting-tree-structured-representations-of-trained-networks.pdf

  12. d’Amato, C.: Machine learning for the semantic web: lessons learnt and next research directions. Semant. Web 11(1), 195–203 (2020). https://doi.org/10.3233/SW-200388

    Article  Google Scholar 

  13. Franco, L., Subirats, J.L., Molina, I., Alba, E., Jerez, J.M.: Early breast cancer prognosis prediction and rule extraction using a new constructive neural network algorithm. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 1004–1011. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73007-1_121

    Chapter  Google Scholar 

  14. Freitas, A.A.: Comprehensible classification models: a position paper. ACM SIGKDD Explor. Newsl. 15(1), 1–10 (2014). https://doi.org/10.1145/2594473.2594475

    Article  Google Scholar 

  15. Glimm, B., Horrocks, I., Motik, B., Stoilos, G., Wang, Z.: HermiT: an OWL 2 reasoner. J. Autom. Reason. 53(3), 245–269 (2014). https://doi.org/10.1007/s10817-014-9305-1

    Article  MATH  Google Scholar 

  16. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5), 1–42 (2018). https://doi.org/10.1145/3236009

    Article  Google Scholar 

  17. Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.: XAI - explainable artificial intelligence. Sci. Robot. 4(37) (2019). https://doi.org/10.1126/scirobotics.aay7120

  18. Hayashi, Y., Setiono, R., Yoshida, K.: A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders. Artif. Intell. Med. 20(3), 205–216 (2000). https://doi.org/10.1016/s0933-3657(00)00064-6

    Article  Google Scholar 

  19. Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P.F., Rudolph, S.: OWL 2 web ontology language primer (second edition). W3C Recommendation 11 December 2012 (2012). https://www.w3.org/TR/owl2-primer

  20. Hoekstra, R.: The knowledge reengineering bottleneck. Semant. Web 1(1–2), 111–115 (2010). https://doi.org/10.3233/SW-2010-0004

    Article  Google Scholar 

  21. Hofmann, A., Schmitz, C., Sick, B.: Rule extraction from neural networks for intrusion detection in computer networks. In: 2003 IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1259–1265. IEEE (2003). https://doi.org/10.1109/ICSMC.2003.1244584

  22. Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: a semantic web rule language combining OWL and RuleML. W3C Member Submission 21 May 2004 (2004). https://www.w3.org/Submission/SWRL

  23. Huysmans, J., Baesens, B., Vanthienen, J.: ITER: an algorithm for predictive regression rule extraction. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 270–279. Springer, Heidelberg (2006). https://doi.org/10.1007/11823728_26

    Chapter  Google Scholar 

  24. Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., Baesens, B.: An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decis. Support Syst. 51(1), 141–154 (2011). https://doi.org/10.1016/j.dss.2010.12.003

    Article  Google Scholar 

  25. Lachiche, N.: Propositionalization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 812–817. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8_680

    Chapter  Google Scholar 

  26. Lamy, J.: Owlready: ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies. Artif. Intell. Med. 80, 11–28 (2017). https://doi.org/10.1016/j.artmed.2017.07.002

    Article  Google Scholar 

  27. Lipton, Z.C.: The mythos of model interpretability. Queue 16(3), 31–57 (2018). https://doi.org/10.1145/3236386.3241340

    Article  Google Scholar 

  28. Maamar, Z., Moulin, B.: Interoperability of distributed and heterogeneous systems based on software agent-oriented frameworks. In: Kandzia, P., Klusch, M. (eds.) CIA 1997. LNCS, vol. 1202, pp. 248–259. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-62591-7_38

    Chapter  Google Scholar 

  29. Manola, F., Miller, E., McBride, B.: Resource description framework (RDF) primer. W3C Recommendation 10 February 2004 (2004). https://www.w3.org/TR/rdf-primer

  30. Motik, B., Shearer, R.D.C., Horrocks, I.: Hypertableau reasoning for description logics. J. Artif. Intell. Res. 36, 165–228 (2009). https://doi.org/10.1613/jair.2811

    Article  MathSciNet  MATH  Google Scholar 

  31. Murphy, P.M., Pazzani, M.J.: ID2-of-3: constructive induction of M-of-N concepts for discriminators in decision trees. In: Machine Learning Proceedings 1991, pp. 183–187. Elsevier (1991). https://doi.org/10.1016/B978-1-55860-200-7.50040-4. 8th International Conference (ML 1991), Evanston, IL, USA

  32. Quinlan, J.R.: Simplifying decision trees. Int. J. Man Mach. Stud. 27(3), 221–234 (1987). https://doi.org/10.1016/S0020-7373(87)80053-6

    Article  Google Scholar 

  33. Quinlan, J.R.: C4.5: Programming for Machine Learning. Morgan Kauffmann (1993). https://dl.acm.org/doi/10.5555/152181

  34. Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019). https://doi.org/10.1038/s42256-019-0048-x

    Article  Google Scholar 

  35. Sabbatini, F., Ciatto, G., Calegari, R., Omicini, A.: On the design of PSyKE: a platform for symbolic knowledge extraction. In: Calegari, R., Ciatto, G., Denti, E., Omicini, A., Sartor, G. (eds.) WOA 2021–22nd Workshop “From Objects to Agents”. CEUR Workshop Proceedings, vol. 2963, pp. 29–48. Sun SITE Central Europe, RWTH Aachen University (2021). http://ceur-ws.org/Vol-2963/paper14.pdf. 22nd Workshop “From Objects to Agents” (WOA 2021), Bologna, Italy, 1–3 September Proceedings (2021)

  36. Sabbatini, F., Ciatto, G., Omicini, A.: GridEx: an algorithm for knowledge extraction from black-box regressors. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds.) EXTRAAMAS 2021. LNCS (LNAI), vol. 12688, pp. 18–38. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-82017-6_2

    Chapter  Google Scholar 

  37. Saleem, A., Honeth, N., Nordström, L.: A case study of multi-agent interoperability in IEC 61850 environments. In: IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Europe 2010, 11–13 October 2010, Gothenburg, Sweden, pp. 1–8. IEEE (2010). https://doi.org/10.1109/ISGTEUROPE.2010.5638876

  38. Setiono, R., Baesens, B., Mues, C.: Rule extraction from minimal neural networks for credit card screening. Int. J. Neural Syst. 21(04), 265–276 (2011). https://doi.org/10.1142/S0129065711002821

    Article  Google Scholar 

  39. Shearer, R.D.C., Motik, B., Horrocks, I.: HermiT: A highly-efficient OWL reasoner. In: Dolbear, C., Ruttenberg, A., Sattler, U. (eds.) Proceedings of the Fifth OWLED Workshop on OWL: Experiences and Directions, Collocated with the 7th International Semantic Web Conference (ISWC-2008), Karlsruhe, Germany, 26–27 October 2008. CEUR Workshop Proceedings, vol. 432. CEUR-WS.org (2008). http://ceur-ws.org/Vol-432/owled2008eu_submission_12.pdf

  40. Siorpaes, K., Hepp, M.: OntoGame: towards overcoming the incentive bottleneck in ontology building. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2007, Part II. LNCS, vol. 4806, pp. 1222–1232. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76890-6_50

    Chapter  Google Scholar 

  41. Sirin, E., Parsia, B.: Pellet: an OWL DL reasoner. In: Haarslev, V., Möller, R. (eds.) Proceedings of the 2004 International Workshop on Description Logics (DL2004), Whistler, British Columbia, Canada, 6–8 June 2004. CEUR Workshop Proceedings, vol. 104. CEUR-WS.org (2004). http://ceur-ws.org/Vol-104/30Sirin-Parsia.pdf

  42. Sirin, E., Parsia, B., Cuenca Grau, B., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. J. Web Semant. 5(2), 51–53 (2007). https://doi.org/10.1016/j.websem.2007.03.004

    Article  Google Scholar 

  43. Steiner, M.T.A., Steiner Neto, P.J., Soma, N.Y., Shimizu, T., Nievola, J.C.: Using neural network rule extraction for credit-risk evaluation. Int. J. Comput. Sci. Netw. Secur. 6(5A), 6–16 (2006). http://paper.ijcsns.org/07_book/200605/200605A02.pdf

Download references

Acknowledgments

This paper is partially supported by the CHIST-ERA IV project CHIST-ERA-19-XAI-005, co-funded by EU and the Italian MUR (Ministry for University and Research).

Author information

Authors and Affiliations

  1. Dipartimento di Scienze Pure e Applicate (DiSPeA), Università degli Studi di Urbino “Carlo Bo”, Urbino, Italy

    Federico Sabbatini

  2. Dipartimento di Informatica – Scienza e Ingegneria (DISI), Alma Mater Studiorum—Università di Bologna, Cesena, Italy

    Federico Sabbatini, Giovanni Ciatto & Andrea Omicini

Authors
  1. Federico Sabbatini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giovanni Ciatto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Omicini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Federico Sabbatini .

Editor information

Editors and Affiliations

  1. University of Applied Sciences and Arts of Western Switzerland, Sierre, Switzerland

    Davide Calvaresi

  2. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Amro Najjar

  3. Victoria University of Wellington, Wellington, New Zealand

    Michael Winikoff

  4. Umeå University, Umeå, Sweden

    Kary Främling

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

Sabbatini, F., Ciatto, G., Omicini, A. (2022). Semantic Web-Based Interoperability for Intelligent Agents with PSyKE. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds) Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2022. Lecture Notes in Computer Science(), vol 13283. Springer, Cham. https://doi.org/10.1007/978-3-031-15565-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15565-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15564-2

  • Online ISBN: 978-3-031-15565-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation