Neural-symbolic integration and the Semantic Web

被引：31

作者：

Hitzler, Pascal ^{[1
]}

Bianchi, Federico ^{[2
]}

Ebrahimi, Monireh ^{[1
]}

Sarker, Md Kamruzzaman ^{[1
]}

机构：

[1] Kansas State Univ, Manhattan, KS 66506 USA

[2] Univ Milano Bicocca, Milan, Italy

来源：

SEMANTIC WEB | 2020年 / 11卷 / 01期

关键词：

Neural-symbolic integration; deductive reasoning; artificial neural networks; deep learning;

D O I：

10.3233/SW-190368

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：3 / 11

页数：9

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