Abstract
The paper focuses on two pivotal cognitive functions of both natural and AI agents, namely classification and identification. Inspired from the theory of teleosemantics, itself based on neuroscientific results, we show that these two functions are complementary and rely on distinct forms of knowledge representation. We provide a new perspective on well-known AI techniques by categorising them as either classificational or identificational. Our proposed Teleo-KR architecture provides a high-level framework for combining the two functions within a single AI system. As validation and demonstration on a concrete application, we provide experiments on the large-scale reuse of classificational (ontological) knowledge for the purposes of learning-based schema identification.
This paper was partly supported by the InteropEHRate project, co-funded by the European Union (EU) Horizon 2020 programme under grant number 826106.
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Fumagalli, M., Bella, G., Giunchiglia, F. (2019). Towards Understanding Classification and Identification. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_6
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