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Article type: Position Paper
Authors: Hitzler, Pascala; * | Bianchi, Federicob | Ebrahimi, Monireha | Sarker, Md Kamruzzamana
Affiliations: [a] Kansas State University, Manhattan, KS, USA | [b] University of Milan – Bicocca, Italy
Correspondence: [*] Corresponding author. E-mail: hitzler@ksu.edu.
Abstract: Symbolic Systems in Artificial Intelligence which are based on formal logic and deductive reasoning are fundamentally different from Artificial Intelligence systems based on artificial neural networks, such as deep learning approaches. The difference is not only in their inner workings and general approach, but also with respect to capabilities. Neural-symbolic Integration, as a field of study, aims to bridge between the two paradigms. In this paper, we will discuss neural-symbolic integration in its relation to the Semantic Web field, with a focus on promises and possible benefits for both, and report on some current research on the topic.
Keywords: Neural-symbolic integration, deductive reasoning, artificial neural networks, deep learning
DOI: 10.3233/SW-190368
Journal: Semantic Web, vol. 11, no. 1, pp. 3-11, 2020
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