可解释的知识图谱推理方法综述

doi:10.11959/j.issn.2096-109x.2022063

摘要/Abstract

摘要：

近年来，以深度学习模型为基础的人工智能研究不断取得突破性进展，但其大多具有黑盒性，不利于人类认知推理过程，导致高性能的复杂算法、模型及系统普遍缺乏决策的透明度和可解释性。在国防、医疗、网络与信息安全等对可解释性要求严格的关键领域，推理方法的不可解释性对推理结果及相关回溯造成较大影响，因此，需要将可解释性融入这些算法和系统中，通过显式的可解释知识推理辅助相关预测任务，形成一个可靠的行为解释机制。知识图谱作为最新的知识表达方式之一，通过对语义网络进行建模，以结构化的形式描述客观世界中实体及关系，被广泛应用于知识推理。基于知识图谱的知识推理在离散符号表示的基础上，通过推理路径、逻辑规则等辅助手段，对推理过程进行解释，为实现可解释人工智能提供重要途径。针对可解释知识图谱推理这一领域进行了全面的综述。阐述了可解释人工智能和知识推理相关概念。详细介绍近年来可解释知识图谱推理方法的最新研究进展，从人工智能的3个研究范式角度出发，总结了不同的知识图谱推理方法。提出对可解释的知识图谱推理研究前景和未来研究方向。

关键词: 知识推理, 知识图谱, 可解释人工智能, 信息安全

Abstract:

In recent years, deep learning models have achieved remarkable progress in the prediction and classification tasks of artificial intelligence systems.However, most of the current deep learning models are black box, which means it is not conducive to human cognitive reasoning process.Meanwhile, with the continuous breakthroughs of artificial intelligence in the researches and applications, high-performance complex algorithms, models and systems generally lack the transparency and interpretability of decision making.This makes it difficult to apply the technologies in a wide range of fields requiring strict interpretability, such as national defense, medical care and cyber security.Therefore, the interpretability of artificial intelligence should be integrated into these algorithms and systems in the process of knowledge reasoning.By means of carrying out explicit explainable intelligence reasoning based on discrete symbolic representation and combining technologies in different fields, a behavior explanation mechanism can be formed which is an important way for artificial intelligence to realize data perception to intelligence perception.A comprehensive review of explainable knowledge graph reasoning was given.The concepts of explainable artificial intelligence and knowledge reasoning were introduced briefly.The latest research progress of explainable knowledge graph reasoning methods based on the three paradigms of artificial intelligence was introduced.Specifically, the ideas and improvement process of the algorithms in different scenarios of explainable knowledge graph reasoning were explained in detail.Moreover, the future research direction and the prospect of explainable knowledge graph reasoning were discussed.

Key words: knowledge reasoning, knowledge graph, explainable artificial intelligence, information security

中图分类号:

TP309

夏毅, 兰明敬, 陈晓慧, 罗军勇, 周刚, 何鹏. 可解释的知识图谱推理方法综述[J]. 网络与信息安全学报, 2022, 8(5): 1-25.

Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE. Survey on explainable knowledge graph reasoning methods[J]. Chinese Journal of Network and Information Security, 2022, 8(5): 1-25.

图/表 19

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图5

表1

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表4

一阶逻辑格式的转换Table 4 The form of first-order logic"

三元组和基本规则	一阶逻辑的格式
$(h, r, t)$	$r (h) = > t$
$(h, r_{1}, t) = > (h, r_{2}, t)$	$\begin{array}{l} [(h \in C) \land [r_{1} (h) = > t]] \\ = > [r_{2} (h) = > t] \end{array}$
$\begin{array}{l} (e_{1}, r_{1}, e_{2}) + (e_{2}, r_{2}, e_{3}) \\ = > (e_{1}, r_{3}, e_{3}) \end{array}$	$\begin{array}{l} [[r_{1} (e_{1}) = > e_{2}] \land [r_{2} (e_{2}) = > e_{3}]] \\ = > [r_{3} (e_{1}) = > e_{3}] \end{array}$
$(h, r_{1}, t) < = > (t, r_{2}, h)$	$\begin{array}{l} [[r_{1} (h) = > t] = > [r_{2} (t) = > h]] \land \\ [[r_{2} (t) = > h] = > [r_{1} (h) = > t]] \end{array}$

表4

表5

一阶逻辑的数学表达Table 5 Mathematical expression of first-order logic"

一阶逻辑	数学表达
$r (h)$	$r + h$
$a = > b$	$a - b$
$h \in C$	$h \cdot C (C 是一个矩阵)$
$a \land b$	$a \otimes b$
$a < = > b$	$(a - b) \otimes (a - b)$

表5

图10

表6

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表7

可解释的知识推理方法及特点Table 7 Explainable knowledge reasoning methods and their characteristics"

分类	子分类	部分经典模型	推理特点	推理准确性（MRR）	解释形式	可解释的范围	可解释的产生方法	可解释的泛化性
		Pellet(2007)^[38]
	基于本体的	OP(2016)^[43]	利用抽象化本体层面的		本体的演绎	全局可
	知识推理	JOIE(2019)^[46]	频繁模式、约束或路径	—	关系	解释	事前可解释	模型无关
符号主义中的知识推理		OntoED(2021)^[44]	进行推理
符号主义中的知识推理		MLN(2006)^[54]		0.098
	基于逻辑规则	AIME+(2015)^[52]	通过挖掘全局或局部逻	0.292	逻辑规则	全局可	事前可解释	模型无关
	的知识推理	AnyBURL(2020)^[64]	辑规则或特征进行推理	0.346		解释
		Rule-IC(2021)^[66]		0.355
		PRA(2010)^[66]		0.098	计算推理路
	基于随机游走	SFE(2015)^[69]	通过随机游走发现隐式	0.232	径特征的权	局部可	事前可解释	模型无关
	的知识推理	TRWA(2018)^[70]	的关联规则，但是搜索	0.359	重为结果进	解释
行为主义中的知识推理		Attnpath(2019)^[71]	空间较大，计算效率低	0.346	行解释
行为主义中的知识推理		DeepPath(2018)^[73]	缓解了基于随机游走搜	0.301	借助强化学
	基于强化学习	MINERVA(2018)^[74]	索空间过大的问题，但	0.298	习的推理路	局部可	事前可解释	解释特定
	的知识推理	MultiHop(2018)^[76]	黑盒模型的引入导致可	0.363	径来解释智	解释		于模型
		SparKGR(2022)^[80]	解释性下降	0.385	能体的行为
		TransE(2013)^[87]		0.326
	基于平移距离	RotateE(2019)^[91]		0.338		局部可	事后可解释	解释特定
	的模型	DuelE(2021)^[94]	利用知识图谱表示模	0.492	关系模式、逻	解释		于模型
连接主义中的知识推理		MuRP(2020)^[99]	型，将实体和关系映射	0.418	辑操作、实体
连接主义中的知识推理		RESCAL(2015)^[100]	为低维向量，数值化计	0.227	的层次关系
	基于张量分解	DisMult(2016)^[101]	算进行推理预测	0.312		局部可	事后可解释	解释特定
	的模型	ASNALOGY(2019)^[103]		0.356		解释		于模型
		TuckER(2020)^[104]		0.409
		KALE(2016)^[114]	充分利用规则的准确性	0.312
	符号规则增强	PlogicNet(2019)^[124]	高、可解释性强的优势，	0.332	逻辑规则推	局部可	事后可解释	解释特定
	神经网络的知	ExpressGNN(2020)^[125]	提高神经网络推理的透	0.420	理路径	解释		于模型
新型混合的知识推理	识推理	UniKER(2021)^[118]	明性及可靠性	0.522
新型混合的知识推理		Neural LP(2017)^[129]	充分利用神经网络鲁棒	0.237
	神经网络增强	RLvLR(2019)^[137]	性和效率上的优势，缓	0.24	关系模式逻	全局可	事前可解释	模型无关
	符号规则的知	NLIL(2020)^[131]	解数据噪声及搜索空间	0.26	辑操作实体	解释
	识推理	RNNLogic(2021)^[140]	大的问题	0.344	的层次关系
注：由于符号主义中基于本体的知识推理为概念层的推理，推理准确率不以比较。

表7

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