Chinese Journal of Network and Information Security ›› 2022, Vol. 8 ›› Issue (5): 1-25.doi: 10.11959/j.issn.2096-109x.2022063
• Comprehensive Review • Next Articles
Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE
Revised:
2022-06-13
Online:
2022-10-15
Published:
2022-10-01
Supported by:
CLC Number:
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.
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分类 | 部分经典模型 | 推理特点推理准确性(MRR) | 推理准确性(Hit@3) | 解释形式 | 可解释的范围 | 可解释的产生方式 | 可解释的泛化性 | 下游场所适用性 | |
Pellet(2007)[ | |||||||||
基于本体的 | OP(2016)[ | 利用抽象化本体层 | 本体的演 | 全局 | 事前可 | 可靠性 | |||
知识推理 | JOIE(2019)[ | 面的频繁模式、约— | — | 绎关系 | 可解释 | 解释 | 模型无关 | 优先领域 | |
OntoED(2021)[ | 束或路径进行推理 | ||||||||
MLN(2006)[ | 0.098 | 0.103 | |||||||
基于逻辑规则 | AIME+(2015)[52 | 通过挖掘全局或局0.292 | 0.305 | 全局 | 事前可 | 可靠性 | |||
的知识推理 | AnyBURL(2020)[ | 部逻辑规则或特征0.346 | 0.367 | 逻辑规则 | 可解释 | 解释 | 模型无关 | 优先领域 | |
Rule-IC(2021)[ | 进行推理0.355 | 0.374 | |||||||
注:由于符号主义中基于本体的知识推理为概念层的推理,推理准确性不做比较。 |
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分类 | 部分经典模型 | 推理特点 | 推理准确性(MRR) | 推理准确性(Hit@3) | 解释形式 | 可解释的范围 | 可解释的产生方式 | 可解释的泛化性 | 下游式场景适应性 |
PRA(2010)[ | 通过随机游走发现隐 | 0.098 | 0.197 | ||||||
基于随机 | SFE(2015)[ | 式的关联规则,但搜 | 0.232 | 0.307 | 计算推理路径 | ||||
游走的知 | TRWA(2018)[ | 索空间较大,计算效 | 0.359 | 0.359 | 特征的权重为 | 局部 | 事前 | 效率优先领 | |
识推理 | Attnpath(2019)[ | 率低 | 0.346 | 0.391 | 结果进行解释 | 可解释 | 可解释 | 模型无关 | 域 |
DeepPath(2018)[ | 缓解了基于随机游走 | 0.301 | 0.381 | 借助强化学习 | |||||
基于强化 | MINERVA(2018)[ | 搜索空间过大的问 | 0.298 | 0.389 | 的推理路径来 | 局部 | 事前 | 解释特定 | 效率优先领 |
学习的知 | MultiHop(2018)[ | 题,但黑盒模型的引 | 0.363 | 0.413 | 解释智能体的 | 可解释 | 可解释 | 于模型 | 域 |
识推理 | Ruleguider(2022)[ | 入导致可解释性下降 | 0.385 | 0.438 | 行为 |
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分类 | 部分经典模型 | 推理特点 | 推理准确性(MRR) | 推理准确性(MRR) | 解释形式 | 可解释的范围 | 可解释的产生方式 | 可解释的泛化性 | 下游场景适用性 |
基于平 | TransE(2013)[ | 0.326 | 0.363 | ||||||
移距离 | RotateE(2019)[ | 0.338 | 0.487 | 局部 | 事后 | 解释特定 | 效率 | ||
的模型 | DuelE(2021)[ | 利用知识图谱 | 0.492 | 0.423 | 可解释 | 可解释 | 于模型 | 优先领域 | |
MuRP(2020)[ | 表示模型,将实 | 0.418 | 0.447 | 关系模式、 | |||||
体和关系映射 | 逻辑操作、 | ||||||||
基于张 | RESCAL(2015)[ | 为低维向量,数 | 0.227 | 0.255 | 实体的层次 | ||||
量分解 | DisMult(2016)[ | 值化计算进行 | 0.312 | 0.263 | 关系 | 局部 | 事后 | 解释特定 | 效率 |
的模型 | ASNALOGY(2019)[ | 推理预测 | 0.356 | 0.275 | 可解释 | 可解释 | 于模型 | 优先领域 | |
TuckER(2020)[ | 0.409 | 0.356 |
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分类 | 部分经典模型 | 推理特点 | 推理准确性(MRR) | 推理准确性(Hit@3) | 解释形式 | 可解释的范围 | 可解释的产生方式 | 可解释的泛化性 | 下游场景适应性 |
KALE(2016)[ | 充分利用规则的准确 | 0.312 | 0.324 | ||||||
符号规则增强 | PlogicNet(2019)[ | 性高、可解释性强的优 | 0.332 | 0.369 | 逻辑规则 | 局部 | 事后 | 解释特定 | 效率优先 |
神经网络的知 | ExpressGNN(2020)[ | 势,提高神经网络推理 | 0.420 | 0.375 | 推理路径 | 可解释 | 可解释 | 于模型 | 领域 |
识推理 | UniKER(2021)[ | 的透明性及可靠性 | 0.522 | 0.507 | |||||
Neural LP(2017)[ | 充分利用神经网络鲁 | 0.237 | 0.306 | 关系模式、 | |||||
神经网络增强 | RLvLR(2019)[ | 棒性和效率上的优势, | 0.24 | 0.344 | 逻辑操作、 | 全局 | 事前 | 效率优先 | |
符号规则的知 | NLIL(2020)[ | 缓解数据噪声及搜索 | 0.26 | 0.359 | 实体的层 | 可解释 | 可解释 | 模型无关 | 领域 |
识推理 | RNNLogic(2021)[ | 空间大的问题 | 0.344 | 0.380 | 次关系 |
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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)[ | ||||||||
基于本体的 | OP(2016)[ | 利用抽象化本体层面的 | 本体的演绎 | 全局可 | ||||
知识推理 | JOIE(2019)[ | 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)[ | 进行推理 | ||||||
MLN(2006)[ | 0.098 | |||||||
基于逻辑规则 | AIME+(2015)[ | 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)[ | 辑规则或特征进行推理 | 0.346 | 解释 | ||||
Rule-IC(2021)[ | 0.355 | |||||||
PRA(2010)[ | 0.098 | 计算推理路 | ||||||
基于随机游走 | SFE(2015)[ | 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)[ | 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)[ | 空间较大,计算效率低 | 0.346 | 行解释 | ||||
DeepPath(2018)[ | 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)[ | 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)[ | 黑盒模型的引入导致可 | 0.363 | 径来解释智 | 解释 | 于模型 | ||
SparKGR(2022)[ | 解释性下降 | 0.385 | 能体的行为 | |||||
TransE(2013)[ | 0.326 | |||||||
基于平移距离 | RotateE(2019)[ | 0.338 | 局部可 | 事后可解释 | 解释特定 | |||
的模型 | DuelE(2021)[ | 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)[ | 型,将实体和关系映射 | 0.418 | 辑操作、实体 | ||||
RESCAL(2015)[ | 为低维向量,数值化计 | 0.227 | 的层次关系 | |||||
基于张量分解 | DisMult(2016)[ | 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)[ | 0.356 | 解释 | 于模型 | ||||
TuckER(2020)[ | 0.409 | |||||||
KALE(2016)[ | 充分利用规则的准确性 | 0.312 | ||||||
符号规则增强 | PlogicNet(2019)[ | 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)[ | 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)[ | 明性及可靠性 | 0.522 | ||||
Neural LP(2017)[ | 充分利用神经网络鲁棒 | 0.237 | ||||||
神经网络增强 | RLvLR(2019)[ | 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)[ | 解数据噪声及搜索空间 | 0.26 | 辑操作实体 | 解释 | |||
识推理 | RNNLogic(2021)[ | 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。 |
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