大数据人工智能下的多重知识表达：框架、应用及案例研究

杨易; 庄越挺; 潘云鹤

doi:10.1631/FITEE.2100463

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大数据人工智能下的多重知识表达：框架、应用及案例研究

常规文章 | Updated：2022-05-20

- 大数据人工智能下的多重知识表达：框架、应用及案例研究
  Enhanced Publication
- Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies
- 信息与电子工程前沿（英文） 2021年22卷第12期页码：1551-1558
- Affiliations：
  
  College of Computer Science and Technology, Zhejiang University, Hangzhou310027, China
- Author bio：
  
  ‡Corresponding author
  E-mail: yzhuang@zju.edu.cn
  E-mail: panyh@zju.edu.cn
- Funds：
  
  National Key R&D Program of China(2020AAA0108800)
- DOI：10.1631/FITEE.2100463
  中图分类号：
- 收稿：2021-09-30，
  
  修回：2021-11-22，
  
  录用：2021-10-07，
  
  纸质出版：2021-12-0
- Accepted：
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杨易, 庄越挺, 潘云鹤. 大数据人工智能下的多重知识表达：框架、应用及案例研究[J]. 信息与电子工程前沿（英文）, 2021,22(12):1551-1558.

Yi YANG, Yueting ZHUANG, Yunhe PAN. Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(12): 1551-1558.
杨易, 庄越挺, 潘云鹤. 大数据人工智能下的多重知识表达：框架、应用及案例研究[J]. 信息与电子工程前沿（英文）, 2021,22(12):1551-1558. DOI： 10.1631/FITEE.2100463.

Yi YANG, Yueting ZHUANG, Yunhe PAN. Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(12): 1551-1558. DOI： 10.1631/FITEE.2100463.

摘要

Abstract

In this paper

we present a multiple knowledge representation (MKR) framework and discuss its potential for developing big data artificial intelligence (AI) techniques with possible broader impacts across different AI areas. Typically

canonical knowledge representations and modern representations each emphasize a particular aspect of transforming inputs into symbolic encoding or vectors. For example

knowledge graphs focus on depicting semantic connections among concepts

whereas deep neural networks (DNNs) are more of a tool to perceive raw signal inputs. MKR is an advanced AI representation framework for more complete intelligent functions

such as raw signal perception

feature extraction and vectorization

knowledge symbolization

and logical reasoning. MKR has two benefits: (1) it makes the current AI techniques (dominated by deep learning) more explainable and generalizable

and (2) it expands current AI techniques by integrating MKR to facilitate the mutual benefits of the complementary capacity of each representation

e.g.

raw signal perception and symbolic encoding. We expect that MKR research and its applications will drive the evolution of AI 2.0 and beyond.

关键词

Keywords

references

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