A Meta-learning Knowledge Reasoning Framework Combining Semantic Path and Language Model
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摘要: 针对传统推理方法无法兼顾计算能力与可解释性，同时在小样本场景下难以实现知识的快速学习等问题，该文设计一款融合语义路径与双向Transformer编码(BERT)的模型无关元学习(MAML)推理框架，该框架由基训练和元训练两个阶段构成。基训练阶段，将图谱推理实例用语义路径表示，并代入BERT模型微调计算链接概率，离线保存推理经验；元训练阶段，该框架基于多种关系的基训练过程获得梯度元信息，实现初始权值优化，完成小样本下知识的快速学习。实验表明，基训练推理框架在链接预测与事实预测任务中多项指标高于平均水平，同时元学习框架可以实现部分小样本推理问题的快速收敛。
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关键词:
- 知识推理 /
- 语义路径 /
- 双向Transformer编码表示 /
- 模型无关元学习
Abstract: In order to solve the problems that traditional knowledge reasoning methods can not combine computing power and interpretability, and it is difficult to learn quickly in few-shot scenarios, a Model-Agnostic Meta-Learning (MAML) reasoning framework is proposed in this paper, which combines semantic path and Bidirectional Encoder Representations for Transformers (BERT), and consists of two stages: base-training and meta-training. In base-training stage, the graph reasoning instances is represented by semantic path and BERT model, which is used to calculate the link probability and save reasoning experience offline by fine-tuning. In meta-training stage, the gradient meta-information based on the base-training process of multiple relations is obtained by this framework, which realizes the initial weight optimization, and completes the rapid learning of knowledge under few-shot. Experiments show that better performance in link prediction and fact prediction can be achieved by the base-training reasoning framework, and fast convergence of some few-shot reasoning problems can be achieved by the meta-learning framework. -
表 1 部分变量标识
变量种类 变量含义 表示方式 元素标识 实体i Enti 关系j Relj 方向标识 正向标识符 [FWD] 反向标识符 [RVS] 语义标识 语义路径起始符 [CLS] 语义路径分隔符 [SEP] 
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表 2 Task举例与表示
Task#1 (配偶) Support <[CLS]王健林[FWD]配偶林宁[SEP]王健林[RVS]父亲王思聪[FWD]母亲林宁[SEP]>··· Query <[CLS]约瑟夫·拜登[FWD]配偶吉尔·拜登[SEP]约瑟夫·拜登[RVS]父亲亨特·拜登[FWD]母亲吉尔·拜登[SEP]>··· Task#2 (所属地区) Support <[CLS]江汉路[FWD]所属地区武汉[SEP]江汉路 [FWD]所属地区汉口[FWD]所属地区武汉[SEP]>··· Query <[CLS]伊夫岛[FWD]所属地区普罗旺斯[SEP]伊夫岛 [FWD]所属地区马赛[FWD]所属地区普罗旺斯 [SEP]>··· Task#3 (前型级) Support <[CLS]f-35战斗机[FWD]前型级YF-22[SEP]f-35战斗机[RVS]衍生型f-22战斗机[FWD]前型级YF-22 [SEP]> ··· Query <[CLS]歼-16[FWD]前型级苏-27SK[SEP]歼-16 [FWD]前型级歼-11[FWD]前型级苏-27SK [SEP]>··· Task#m
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表 4 不同方案链接预测效果比较
方案 FB15K-237 WN18RR 人际关系图谱 CN-DBpedia子集 MRR MR Hits@10 MRR MR Hits@10 MRR MR Hits@10 MRR MR Hits@10 TransE 0.294 357 0.465 0.226 3384 0.501 0.428 38 0.523 0.269 163 0.482 ComplEx 0.247 339 0.428 0.440 5261 0.510 0.440 34 0.600 0.331 132 0.525 R-GCNs 0.248 – 0.417 – – – 0.452 32 0.623 0.308 134 0.495 KBGAN 0.278 – 0.458 0.214 – 0.472 0.476 32 0.556 0.299 120 0.536 ConvKB 0.243 311 0.421 0.249 3324 0.524 0.436 26 0.621 0.324 146 0.461 VR-GCN 0.248 – 0.432 – – – 0.468 31 0.566 0.292 143 0.531 本文 0.288 321 0.443 0.285 3201 0.532 0.452 31 0.513 0.315 117 0.513
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表 5 不同方案事实预测效果对比
方案 FB15K-237 WN18RR 关系图谱 DB子图 TransE 0.277 0.243 0.310 0.203 ComplEx 0.309 0.210 0.413 0.231 R-GCNs 0.289 0.255 0.382 0.198 KBGAN 0.313 0.243 0.369 0.228 ConvKB 0.301 0.241 0.425 0.235 VR-GCN 0.324 0.236 0.401 0.242 本文 0.317 0.289 0.411 0.225
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