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On-line Access: 2021-12-23

Received: 2021-09-30

Revision Accepted: 2021-10-07

Crosschecked: 2021-11-22

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yi Yang

https://orcid.org/0000-0002-0512-880X

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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.12 P.1551-1558

http://doi.org/10.1631/FITEE.2100463


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


Author(s):  Yi Yang, Yueting Zhuang, Yunhe Pan

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   yangyics@zju.edu.cn, yzhuang@zju.edu.cn, panyh@zju.edu.cn

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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.

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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.

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
浙江大学计算机学院,中国杭州市,310027
摘要:提出一种多重知识表示框架,探讨了其对推动大数据人工智能技术在各个领域中发展的重要意义及深远影响。传统知识表达和现代基于深度学习的知识表达通常着眼于利用特定变换方式,将输入转换为符号编码或者向量。例如,知识图谱关注于描述各个概念之间的语义联系,而深度神经网络更像是感知原始信号输入的工具。多重知识表达是一种更为先进的人工智能表征框架,具备更完整的智能功能,比如原始信号感知、特征提取及向量化、知识符号化和逻辑推断。多重知识表达有如下两点优势:(1)与现有以深度学习为主导的人工智能技术相比,具有更强的解释性以及更好的泛化能力;(2)将多重知识表达集成于现有人工智能技术,有利于各种表征(例如原始信号感知以及符号化编码)发挥互补优势。我们希望多重知识表达相关研究以及应用能够驱动新一代人工智能蓬勃发展。

关键词:多重知识表达;人工智能;大数据

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