The potential of large language models (LLM) has captured the imagination of the public and researchers alike. In contrast to previous generations of machine learning models, LLMs are general-purpose tools, which can communicate with humans. In particular, they are able to define terms and answer factual questions based on some internally represented knowledge. Thus, LLMs support functionalities that are closely related to ontologies. In this perspective article, I will discuss the consequences of the advent of LLMs for the field of applied ontology.
Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M. & Sherlock, G. (2000). Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics, 25(1), 25–29. doi:10.1038/75556.
2.
Asim, M.N., Wasim, M., Khan, M.U.G., Mahmood, W. & Abbasi, H.M. (2018). A survey of ontology learning techniques and applications. Database, 2018.
3.
Babaei Giglou, H., D’Souza, J. & Auer, S. (2023). LLMs4OL: Large language models for ontology learning. In T.R.Payne, V.Presutti, G.Qi, M.Poveda-Villalón, G.Stoilos, L.Hollink, Z.Kaoudi, G.Cheng and J.Li (Eds.), The Semantic Web – ISWC 2023 (pp. 408–427). Cham: Springer Nature Switzerland. doi:10.1007/978-3-031-47240-4_22.
4.
Bengio, Y., Russell, S. & Musk, E. (2023). https://futureoflife.org/open-letter/pause-giant-ai-experiments/.
5.
Bennett, M. (2013). The financial industry business ontology: Best practice for big data. Journal of Banking Regulation, 14. doi:10.1057/jbr.2013.13.
6.
Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V. & Stappel, M. (2021). Introducing the open energy ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI, 5, 100074. https://www.sciencedirect.com/science/article/pii/S2666546821000288. doi:10.1016/j.egyai.2021.100074.
7.
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M.T. & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4.
8.
Caufield, J.H., Hegde, H., Emonet, V., Harris, N.L., Joachimiak, M.P., Matentzoglu, N., Kim, H., Moxon, S.A.T., Reese, J.T., Haendel, M.A., Robinson, P.N. & Mungall, C.J. (2023). Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning.
9.
Chen, Q., Du, J., Hu, Y., Keloth, V.K., Peng, X., Raja, K., Zhang, R., Lu, Z. & Xu, H. (2023). Large language models in biomedical natural language processing: Benchmarks, baselines, and recommendations. arXiv preprint. arXiv:2305.16326.
10.
Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L. & Zemmel, R. (2023). The economic potential of generative AI. Technical report, Mc Kinsey.
11.
Glauer, M., Memariani, A., Neuhaus, F., Mossakowski, T. & Hastings, J. Interpretable ontology extension in chemistry. Semantic Web Journal, accepted.
12.
Glauer, M., Neuhaus, F., Mossakowski, T., Memariani, A. & Hastings, J. (2023). Neuro-symbolic semantic learning for chemistry. In P.Hitzler, M.K.Sarker and A.Eberhart (Eds.), Compendium of Neurosymbolic Artificial Intelligence. Frontiers in Artificial Intelligence and Applications (pp. 460–484). IOS Press.
13.
Hastings, J. Our own image: What the quest for artificial general intelligence can teach us about being human. Cosmos + Taxis, to appear.
14.
Hastings, J., Glauer, M., Memariani, A., Neuhaus, F. & Mossakowski, T. (2021). Learning chemistry: Exploring the suitability of machine learning for the task of structure-based chemical ontology classification. Journal of Cheminformatics, 13(1), 23. doi:10.1186/s13321-021-00500-8.
15.
Hastings, J., Owen, G., Dekker, A., Ennis, M., Kale, N., Muthukrishnan, V., Turner, S., Swainston, N., Mendes, P. & Steinbeck, C. (2016). ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Research, 44(D1), D1214–D1219. doi:10.1093/nar/gkv1031.
16.
Hunt, T. (2023). Here’s why AI may be extremely dangerous – whether it’s conscious or not. Scientific American, 25 May 2023. https://www.scientificamerican.com/article/heres-why-ai-may-be-extremely-dangerous-whether-its-conscious-or-not/.
17.
Landgrebe, J. & Smith, B. (2022). Why Machines Will Never Rule the World: Artificial Intelligence Without Fear. Taylor & Francis.
18.
Lenat, D.B. (2001). From 2001 to 2001: Common sense and the mind of HAL. In D.G.Stork (Ed.), Hal’s Legacy: 2001 as Dream and Reality (pp. 193–208). Cambridge, MA: MIT Press. doi:10.7551/mitpress/3404.001.0001.
19.
Lopes, A., Carbonera, J., Schmidt, D., Garcia, L., Rodrigues, F. & Abel, M. (2023). Using terms and informal definitions to classify domain entities into top-level ontology concepts: An approach based on language models. Knowledge-Based Systems, 265, 110385. doi:10.1016/j.knosys.2023.110385.
20.
Mateiu, P. & Groza, A. (2023). Ontology engineering with large language models. arXiv preprint. arXiv:2307.16699.
21.
Neuhaus, F. & Hastings, J. (2022). Ontology development is consensus creation, not (merely) representation. Applied Ontology, 1–19.
22.
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J. & Wu, X. (2023). Unifying large language models and knowledge graphs: A roadmap. arXiv preprint. arXiv:2306.08302.
