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Improving Decision Making Using Semantic Web Technologies

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12739)

Abstract

With the rapid advance of technology, we are moving towards replacing humans in decision making–the employment of robotics and computerised systems for production and delivery and autonomous cars in the travel sector. The focus is placed on the use of techniques, such as machine learning and deep learning. However, despite advances in machine learning and deep learning, they are incapable of modelling the relationships that are present in the real world, which are necessary for making a decision. For example, automating sociotechnical systems requires an understanding of both human and technological aspects and how they influence one another. Using machine learning, we can not model the relationships of a sociotechnical systems. Semantic Web technologies, which is based on the concept of linked-data technology, can represent relationships in a more realistic way like in the real world, and be useful to make better decisions. The study looks at the use of Semantic Web technologies, namely ontologies and knowledge graphs to improve decision making process.

Keywords

  • Semantic Web technologies
  • Artificial Intelligence
  • Decision making

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Fig. 1.
Fig. 2.

Notes

  1. 1.

    https://www.weforum.org.

  2. 2.

    http://www3.weforum.org/docs/WEF_ITTC_PersonalDataNewAsset_Report_2011.pdf.

  3. 3.

    https://eur-lex.europa.eu/eli/reg/2016/679/oj.

  4. 4.

    https://www.specialprivacy.eu/.

  5. 5.

    smashHit Public Report D1.3 Public Innovation Concept March 2021.

  6. 6.

    https://www.smashhit.eu.

  7. 7.

    https://www.reportsanddata.com/report-detail/predictive-maintenance-market.

  8. 8.

    https://www.w3.org/TR/vocab-ssn/.

  9. 9.

    http://streamreasoning.org/resources/c-sparql.

  10. 10.

    https://www.z-bre4k.eu.

  11. 11.

    https://www.z-bre4k.eu/wp-content/uploads/2020/12/Z-BRE4K-semantic-modelling.pdf.

  12. 12.

    http://www.gestamp.com/.

  13. 13.

    https://www.philips.com/.

  14. 14.

    http://www.sacmi.com/.

  15. 15.

    https://projekte.ffg.at/projekt/3314668.

  16. 16.

    https://www.w3.org/TR/prov-o/.

  17. 17.

    http://openscience.adaptcentre.ie/ontologies/GConsent/docs/ontology.

  18. 18.

    https://www.w3.org/community/dpvcg/wiki/Data_Protection_Ontology_by_Bartolini_et._al#Data_Protection_Ontology.

  19. 19.

    https://scch.at/en/das-projects-details/ki-net.

  20. 20.

    https://www.ontotext.com/products/graphdb/.

  21. 21.

    https://cloud.google.com.

  22. 22.

    https://aws.amazon.com.

References

  1. Clinical knowledge graph integrates proteomics data into clinical decision-making. bioRxiv (2020)

    Google Scholar 

  2. Regulation (eu) 2016/679 of the European parliamentand of the council of 27 April 2016 on the protectionof natural persons with regard to the processing of personal data and on the free movement of such data, andrepealing directive 95/46/ec (general data protectionregulation). Official Journal of the European Union, L119, May 2016. https://eur-lex.europa.eu/eli/reg/2016/679/oj

  3. Akhtar, S.M., Nazir, M., Saleem, K., Haque, H.M.U., Hussain, I.: An ontology-driven IoT based healthcare formalism. Int. J. Adv. Comput. Sci. Appl. 11(2), 479–486 (2020)

    Google Scholar 

  4. Ali, N., Hong, J.E.: Failure detection and prevention for cyber-physical systems using ontology-based knowledge base. Computers 7(4), 68 (2018)

    CrossRef  Google Scholar 

  5. Antanas, L., et al.: Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach. Auton. Robot. 43(6), 1393–1418 (2018). https://doi.org/10.1007/s10514-018-9784-8

    CrossRef  Google Scholar 

  6. Antunes, F., Freire, M., Costa, J.P.: Semantic web tools and decision-making. In: Zaraté, P., Kersten, G.E., Hernández, J.E. (eds.) GDN 2014. LNBIP, vol. 180, pp. 270–277. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07179-4_31

    CrossRef  Google Scholar 

  7. Bellamy, R.K., et al.: Think your artificial intelligence software is fair? Think again. IEEE Softw. 36(4), 76–80 (2019)

    CrossRef  Google Scholar 

  8. Bellomarini, L., Sallinger, E., Vahdati, S.: Chapter 6 reasoning in knowledge graphs: an embeddings spotlight. In: Janev, V., Graux, D., Jabeen, H., Sallinger, E. (eds.) Knowledge Graphs and Big Data Processing. LNCS, vol. 12072, pp. 87–101. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53199-7_6

    CrossRef  Google Scholar 

  9. Bohanec, M.: Decision making: a computer-science and information-technology viewpoint. Interdisc. Descrip. Complex Syst. Sci. J. 7, 22–37 (2009)

    Google Scholar 

  10. Bonatti, P.A., Decker, S., Polleres, A., Presutti, V.: Knowledge graphs: new directions for knowledge representation on the semantic web (dagstuhl seminar 18371). In: Dagstuhl Reports vol. 8. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2019)

    Google Scholar 

  11. Das, S.K., Swain, A.K.: An ontology-based framework for decision support in assembly variant design. J. Comput. Inf. Sci. Eng. 21(2), 021007 (2021)

    Google Scholar 

  12. Davari, M., Bertino, E.: Access control model extensions to support data privacy protection based on GDPR. In: IEEE International Conference on Big Data (Big Data), pp. 4017–4024 (2019). https://doi.org/10.1109/BigData47090.2019.9006455

  13. Dehury, C.K., Srirama, S.N., Chhetri, T.R.: CCoDaMiC: a framework for coherent coordination of data migration and computation platforms. Futur. Gener. Comput. Syst. 109, 1–16 (2020)

    CrossRef  Google Scholar 

  14. Dubrova, E.: Fault-Tolerant Design. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-2113-9

    CrossRef  MATH  Google Scholar 

  15. D’Aniello, G., Gaeta, M., Orciuoli, F.: An approach based on semantic stream reasoning to support decision processes in smart cities. Telematics Inform. 35(1), 68–81 (2018)

    CrossRef  Google Scholar 

  16. Futia, G., Melandri, A., Vetrò, A., Morando, F., De Martin, J.C.: Removing barriers to transparency: a case study on the use of semantic technologies to tackle procurement data inconsistency. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10249, pp. 623–637. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58068-5_38

    CrossRef  Google Scholar 

  17. Hedberg, T., Barnard Feeney, A., Camelio, J.: Toward a diagnostic and prognostic method for knowledge-driven decision-making in smart manufacturing technologies. In: Madni, A.M., Boehm, B., Ghanem, R.G., Erwin, D., Wheaton, M.J. (eds.) Disciplinary Convergence in Systems Engineering Research, pp. 859–873. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-62217-0_60

    CrossRef  Google Scholar 

  18. Horrocks, I., Giese, M., Kharlamov, E., Waaler, A.: Using semantic technology to tame the data variety challenge. IEEE Internet Comput. 20(6), 62–66 (2016)

    CrossRef  Google Scholar 

  19. Jaiman, V., Urovi, V.: A consent model for blockchain-based health data sharing platforms. IEEE Access 8, 143734–143745 (2020). https://doi.org/10.1109/ACCESS.2020.3014565

    CrossRef  Google Scholar 

  20. Karray, M.H., Chebel-Morello, B., Zerhouni, N.: A formal ontology for industrial maintenance. Appl. Ontol. 7(3), 269–310 (2012)

    CrossRef  Google Scholar 

  21. Lai, P., Phan, N., Hu, H., Badeti, A., Newman, D., Dou, D.: Ontology-based interpretable machine learning for textual data. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–10. IEEE (2020)

    Google Scholar 

  22. Lecue, F.: On the role of knowledge graphs in explainable AI. Semantic Web (Preprint), 1–11 (2019)

    Google Scholar 

  23. Mahindrakar, A., Joshi, K.P., et al.: Automating GDPR compliance using policy integrated blockchain. In: IEEE 6th International Conference on Big Data Security on Cloud (BigDataSecurity 2020) (2020). https://doi.org/10.1109/BigDataSecurity-HPSC-IDS49724.2020.00026

  24. Nie, K., Zeng, K., Meng, Q.: Knowledge reasoning method for military decision support knowledge graph mixing rule and graph neural networks learning together. In: 2020 Chinese Automation Congress (CAC), pp. 4013–4018. IEEE (2020)

    Google Scholar 

  25. Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., Taylor, J.: Industry-scale knowledge graphs: lessons and challenges. Queue 17(2), 48–75 (2019)

    CrossRef  Google Scholar 

  26. Osoba, O.A., Welser, W., IV.: An intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence. Rand Corporation (2017)

    Google Scholar 

  27. Panasiuk, O., Steyskal, S., Havur, G., Fensel, A., Kirrane, S.: Modeling and reasoning over data licenses. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 11155, pp. 218–222. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98192-5_41

    CrossRef  Google Scholar 

  28. Panigutti, C., Perotti, A., Pedreschi, D.: Doctor XAI: an ontology-based approach to black-box sequential data classification explanations. In: Proceedings of the 2020 Conference On Fairness, Accountability, and Transparency, pp. 629–639 (2020)

    Google Scholar 

  29. Pease, S.G., et al.: An interoperable semantic service toolset with domain ontology for automated decision support in the end-of-life domain. Futur. Gener. Comput. Syst. 112, 848–858 (2020)

    CrossRef  Google Scholar 

  30. Power, D.J., Sharda, R.: Model-driven decision support systems: concepts and research directions. Decis. Support Syst. 43(3), 1044–1061 (2007)

    CrossRef  Google Scholar 

  31. Rahman, H., Hussain, M.I.: A comprehensive survey on semantic interoperability for internet of things: state-of-the-art and research challenges. Trans. Emerg. Telecommun. Technol. 31(12), e3902 (2020)

    Google Scholar 

  32. Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intell. 1(5), 206–215 (2019)

    CrossRef  Google Scholar 

  33. Samizadeh Nikoui, T., Rahmani, A.M., Balador, A., Haj Seyyed Javadi, H.: Internet of things architecture challenges: a systematic review. Int. J. Commun. Syst. 34(4), e4678 (2021)

    Google Scholar 

  34. Sovrano, F., Vitali, F., Palmirani, M.: Modelling GDPR-compliant explanations for trustworthy AI. In: Kő, A., Francesconi, E., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) EGOVIS 2020. LNCS, vol. 12394, pp. 219–233. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58957-8_16

    CrossRef  Google Scholar 

  35. Spoladore, D., Sacco, M.: Semantic and dweller-based decision support system for the reconfiguration of domestic environments: Recaal. Electronics 7(9), 179 (2018)

    CrossRef  Google Scholar 

  36. Tachmazidis, I., Davies, J., Batsakis, S., Antoniou, G., Duke, A., Stincic Clarke, S.: Hypercat RDF: semantic enrichment for IoT. In: Li, Y.-F., et al. (eds.) JIST 2016. LNCS, vol. 10055, pp. 273–286. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50112-3_21

    CrossRef  Google Scholar 

  37. Tao, M., Ota, K., Dong, M.: Ontology-based data semantic management and application in IoT-and cloud-enabled smart homes. Futur. Gener. Comput. Syst. 76, 528–539 (2017)

    CrossRef  Google Scholar 

  38. Vasileva, M.I.: The dark side of machine learning algorithms: how and why they can leverage bias, and what can be done to pursue algorithmic fairness. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3586–3587 (2020)

    Google Scholar 

  39. Wan, G., Pan, S., Gong, C., Zhou, C., Haffari, G.: Reasoning like human: hierarchical reinforcement learning for knowledge graph reasoning. In: International Joint Conference on Artificial Intelligence 2020, pp. 1926–1932. Association for the Advancement of Artificial Intelligence (AAAI) (2020)

    Google Scholar 

  40. Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: The World Wide Web Conference, pp. 3307–3313 (2019)

    Google Scholar 

  41. Wang, Q., Hao, Y., Cao, J.: ADRL: an attention-based deep reinforcement learning framework for knowledge graph reasoning. Knowl. Based Syst. 197, 105910 (2020)

    Google Scholar 

  42. Wang, Z., Chen, T., Ren, J., Yu, W., Cheng, H., Lin, L.: Deep reasoning with knowledge graph for social relationship understanding. arXiv preprint arXiv:1807.00504 (2018)

  43. Zhang, W., Yang, D., Wang, H.: Data-driven methods for predictive maintenance of industrial equipment: a survey. IEEE Syst. J. 13(3), 2213–2227 (2019). https://doi.org/10.1109/JSYST.2019.2905565

    CrossRef  Google Scholar 

  44. Zhong, S., Fang, Z., Zhu, M., Huang, Q.: A geo-ontology-based approach to decision-making in emergency management of meteorological disasters. Nat. Hazards 89(2), 531–554 (2017). https://doi.org/10.1007/s11069-017-2979-z

    CrossRef  Google Scholar 

  45. Zhou, K., Zhao, W.X., Bian, S., Zhou, Y., Wen, J.R., Yu, J.: Improving conversational recommender systems via knowledge graph based semantic fusion. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1006–1014 (2020)

    Google Scholar 

  46. Zhu Sun, J.Y., Zhang, J., Bozzon, A., Huang, L.K., Xu, C.: Recurrent knowledge graph embedding for effective recommendation (2018)

    Google Scholar 

  47. Zonta, T., da Costa, C.A., da Rosa Righi, R., de Lima, M.J., da Trindade, E.S., Li, G.P.: Predictive maintenance in the industry 4.0: a systematic literature review. Comput. Ind. Eng. 106889 (2020)

    Google Scholar 

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Acknowledgements

This research has been supported by the European Union projects funded under Horizon 2020 research and innovation programme (smashHit (see footnote 6), grant agreement 871477 and Interreg Österreich-Bayern 2014–2020 programme project (KI-Net (see footnote 19), grant agreement AB 292). I want to express my gratitude to Assoc.-Prof. Dr. Anna Fensel for her support and insightful comments.

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Chhetri, T.R. (2021). Improving Decision Making Using Semantic Web Technologies. In: , et al. The Semantic Web: ESWC 2021 Satellite Events. ESWC 2021. Lecture Notes in Computer Science(), vol 12739. Springer, Cham. https://doi.org/10.1007/978-3-030-80418-3_29

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