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The Convergence of Open Data, Linked Data, Ontologies, and Large Language Models: Enabling Next-Generation Knowledge Systems

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Metadata and Semantic Research (MTSR 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2331))

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Abstract

This paper explores the convergence of Open Data initiatives, Linked Data technologies, ontological knowledge representation, and Large Language Models (LLMs) in generative Artificial Intelligence (AI). It examines how these complementary approaches can be integrated to create more powerful, flexible, and context-aware knowledge systems. The paper provides an overview of the open data landscape, the Semantic Web and Linked Data vision, ontologies and knowledge organization systems, and recent advances in LLMs. It then discusses how these technologies can be synergistically combined to enable next-generation knowledge systems that leverage both structured knowledge and natural language understanding. Potential applications in areas such as scientific research, government transparency, and intelligent information retrieval are discussed. The paper also addresses key challenges including scalability, data quality, ethical considerations, and the need for explainable AI. A strategic roadmap for realizing this integration is proposed, emphasizing collaboration between academia, industry, and government. While significant technical and ethical challenges remain, the convergence of these technologies has the potential to fundamentally transform how we interact with and derive insights from information, enabling more intelligent and context-aware knowledge systems to address complex real-world problems.

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References

  1. Terzic, R., Majstorovic, M.: Open data concept its application and experiences (2019). https://doi.org/10.5937/vojtehg67-19935

  2. https://openaccess.mpg.de/67605/berlideclaration engl.pdf

  3. https://opengovdata.org/

  4. https://data.gov/

  5. https://www.gov.uk/government/publications/open-data-charter/g8-open-datacharter-and-technical-annex

  6. https://resources.data.gov/PoD/principles/

  7. Young, A., Verhulst, S.: The Global Impact of Open Data. O’Reilly Media, Inc. (2016)

    Google Scholar 

  8. Lange, R., Tian, Y., Tang, Y.: Large language models as evolution strategies (2024)

    Google Scholar 

  9. Liu, T., Xu, C., Qiao, Y., Jiang, C., Chen, W.: News recommendation with attention mechanism. arXiv preprint arXiv:2402.07422 (2024)

  10. https://www.w3.org/RDF/Metalog/docs/sw-easy

  11. Ranaldi, F., et al.: Investigating the impact of data contamination of large language models in text-to-SQL translation. arXiv preprint arXiv:2402.08100 (2024)

  12. Sjöström, J., Cronholm, S.: Meta-requirements for LLM-based knowledge exploration tools in information systems research. Springer (2024)

    Google Scholar 

  13. Chang, Y., et al.: A survey on evaluation of large language models. arXiv preprint arxiv:2307.03109 (2023)

  14. Charalabidis, Y., Zuiderwijk, A., Alexopoulos, C., Janssen, M., Lampoltshammer, T.J., Ferro, E.: The open data landscape: concepts, methods, tools and experiences. In: The World of Open Data (2018)

    Google Scholar 

  15. Machado, L.M.O.: Ontologies in Knowledge Organization. MDPI (2021)

    Google Scholar 

  16. Samani, Z.R., Shamsfard, M.: The state of the art in developing fuzzy ontologies: a survey. arXiv preprint arXiv:1805.02290 (2018)

  17. Hadi, M.U., et al.: Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects. TechRxiv (2023)

    Google Scholar 

  18. Jiang, D., et al.: GenAI arena: an open evaluation platform for generative models arXiv preprint arXiv:2406.04485 (2024)

  19. Doumanas, D., Soularidis, A., Kotis, K., Vouros, G.: Integrating LLMs in the Engineering of a SAR Ontology. Springer (2024)

    Google Scholar 

  20. Nahhas, S., Bamasag, O., Khemakhem, M., Bajnaid, N.: Linked data approach to mutually enrich traditional education resources with global open education. IEEE (2018)

    Google Scholar 

  21. Buchmann, R., et al.: Large language models: expectations for semantics-driven systems engineering. Data Knowl. Eng. 152, 102324 (2024)

    Article  MATH  Google Scholar 

  22. Boparai, N.K., Aggarwal, H., Rani, R.: Analyzing fuzzy semantics of reviews for multi-criteria recommendations. Data Knowl. Eng. 152, 102314 (2024)

    Article  MATH  Google Scholar 

  23. Karagiannis, D., Buchmann, R.A.: Linked open models: extending linked open data with conceptual model information. Inf. Syst. 56, 174–197 (2016)

    Article  MATH  Google Scholar 

  24. Shen, L., et al.: The language barrier: dissecting safety challenges of LLMs in multilingual contexts. arXiv preprint arXiv:2401.13136 (2024)

  25. Babaei Giglou, H., D’Souza, J., Auer, S.: LLMs4OL: large language models for ontology learning. Springer (2023)

    Google Scholar 

  26. Zhai, C.X.: Large language models and future of information retrieval: opportunities and challenges. ACM (2024)

    Google Scholar 

  27. Iga, V.I.R., Silaghi, G.C.: LLMS for knowledge-graphs enhanced task-oriented dialogue systems: challenges and opportunities. Springer (2024)

    Google Scholar 

  28. Patil, R., Gudivada, V.: A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs). MDPI (2024)

    Google Scholar 

  29. Pesl, R.D., Stötzner, M., Georgievski, I., Aiello, M.: Uncovering LLMs for service-composition: challenges and opportunities. Springer (2024)

    Google Scholar 

  30. Ding, B., et al.: Data augmentation using large language models: data perspectives, learning paradigms and challenges. arXiv preprint arXiv:2403.02990 (2024)

  31. Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: can language models be too big?. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 610–623. ACM (2021)

    Google Scholar 

  32. Bostrom, N., Yudkowsky, E.: The ethics of artificial intelligence. In: Frankish, K., Ramsey, W.M. (eds.) The Cambridge Handbook of Artificial Intelligence, pp. 316–334. Cambridge University Press, Cambridge (2014)

    Chapter  MATH  Google Scholar 

  33. Liang, Z., et al.: Unleashing the potential of LLMs for quantum computing: a study in quantum architecture design. arXiv preprint arXiv:2307.08191 (2023)

  34. Liao, Y., Ferrie, C.: GPT on a quantum computer. arXiv preprint arXiv:2403.09418 (2024)

  35. Hohenecker, P, Lukasiewicz, T.: Ontology reasoning with deep neural networks (2020). https://doi.org/10.1613/jair.1.11661

  36. Herron, D., Jiménez-Ruiz, E. , Weyde, T.: On the potential of logic and reasoning in neurosymbolic systems using OWL-based knowledge graphs (2024). https://openaccess.city.ac.uk/id/eprint/32688/

  37. Rubiolo, M., Caliusco, M.L., Stegmayer, G., Coronel, M., Gareli Fabrizi, M.: Knowledge discovery through ontology matching: an approach based on an artificial neural network model. Inf. Sci. 194, 107–119 (2012)

    Google Scholar 

  38. Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE (2011)

    Google Scholar 

  39. Pascala, H., Federicob, B., Monireha, E., Kamruzzamana, S.M.: Neural-symbolic integration and the semantic web. Semantic Web 11(1), 3–11 (2020)

    Google Scholar 

  40. Adegun, A.A, Fonou-Dombeu, J.V., Viriri, S., Odindi, J.: Ontology-Based Deep Learning Model for Object Detection and Image Classification in Smart City Concepts. MDPI (2024)

    Google Scholar 

  41. Bang, D., Lim, S., Lee, S., Kim, S.: Biomedical knowledge graph learning for drug repurposing by extending guilt-by association to multiple layers. Nat. Commun. (2023). https://www.nature.com/articles/s41467-023-39301-y

  42. Puiu, D., et al.: CityPulse: large scale data analytics framework for smart cities. IEEE Access 4, 1086–1108 (2016). https://doi.org/10.1109/ACCESS.2016.2541999

    Article  MATH  Google Scholar 

  43. Halpern, B.S., et al.: Priorities for synthesis research in ecology and environmental science. Ecosphere 14(1), e4342 (2023). https://doi.org/10.1002/ecs2.4342

    Article  MATH  Google Scholar 

  44. Brozek, B.: The Legal Mind: A New Introduction to Legal Epistemology. Cambridge University Press (2019)

    Google Scholar 

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Authors and Affiliations

  1. Guglielmo Marconi University, Department of Engineering Science, 00193, Rome, Italy

    Andrea Cigliano & Francesca Fallucchi

  2. Leibniz Institute for Educational Media, Georg Eckert Institute, 38118, Brunswick, Germany

    Francesca Fallucchi

Authors
  1. Andrea Cigliano
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  2. Francesca Fallucchi
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Corresponding authors

Correspondence to Andrea Cigliano or Francesca Fallucchi .

Editor information

Editors and Affiliations

  1. Ionian University, Corfu, Greece

    Michalis Sfakakis

  2. International Hellenic University, Thessaloniki, Greece

    Emmanouel Garoufallou

  3. Ionian University, Corfu, Greece

    Matthew Damigos

  4. Kent State University, Kent, OH, USA

    Athena Salaba

  5. National and Kapodistrian University of Athens, Athens, Greece

    Christos Papatheodorou

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Cigliano, A., Fallucchi, F. (2025). The Convergence of Open Data, Linked Data, Ontologies, and Large Language Models: Enabling Next-Generation Knowledge Systems. In: Sfakakis, M., Garoufallou, E., Damigos, M., Salaba, A., Papatheodorou, C. (eds) Metadata and Semantic Research. MTSR 2024. Communications in Computer and Information Science, vol 2331. Springer, Cham. https://doi.org/10.1007/978-3-031-81974-2_17

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  • DOI: https://doi.org/10.1007/978-3-031-81974-2_17

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  • Online ISBN: 978-3-031-81974-2

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