Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies

Yi YANG; Yueting ZHUANG; Yunhe PAN

doi:10.1631/FITEE.2100463

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Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies

Regular Papers | Updated：2022-05-20

- Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies
  Enhanced Publication
- 大数据人工智能下的多重知识表达：框架、应用及案例研究
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 12, Pages: 1551-1558(2021)
- 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：
- DOI：10.1631/FITEE.2100463
  CLC：
- Published：0 December 2021，
  
  Received：30 September 2021，
  
  Revised：22 November 2021，
  
  Accepted：2021-10-07
- Accepted：
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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：

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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