论文标题
多跳上推理超过稀疏知识图的动态预期和完成
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph
论文作者
论文摘要
近年来，多跳的推理已被广泛研究，以寻求一种有效且可解释的知识图（KG）完成方法。大多数以前的推理方法都是为实体之间具有足够路径的密集kg设计的，但不能在那些仅包含稀疏路径的稀疏kg上工作。一方面，稀疏的kg包含的信息较少，这使得模型很难选择正确的路径。另一方面，缺乏通往目标实体的证据途径也使推理过程变得困难。为了解决这些问题，我们通过应用新颖的动态预期和完成策略提出了一个名为Dackgr的多跳推理模型：（1）预期策略利用基于嵌入的模型的潜在预测来使我们的模型对稀疏KGS进行更多潜在的路径搜索。 （2）基于预期信息，完成策略在路径搜索过程中动态添加边缘作为其他动作，从而进一步减轻了kgs的稀疏问题。从FreeBase，Nell和Wikidata采样的五个数据集上的实验结果表明，我们的方法的表现优于最先进的基线。我们的代码和数据集可从https://github.com/thu-keg/dackgr获得
Multi-hop reasoning has been widely studied in recent years to seek an effective and interpretable method for knowledge graph (KG) completion. Most previous reasoning methods are designed for dense KGs with enough paths between entities, but cannot work well on those sparse KGs that only contain sparse paths for reasoning. On the one hand, sparse KGs contain less information, which makes it difficult for the model to choose correct paths. On the other hand, the lack of evidential paths to target entities also makes the reasoning process difficult. To solve these problems, we propose a multi-hop reasoning model named DacKGR over sparse KGs, by applying novel dynamic anticipation and completion strategies: (1) The anticipation strategy utilizes the latent prediction of embedding-based models to make our model perform more potential path search over sparse KGs. (2) Based on the anticipation information, the completion strategy dynamically adds edges as additional actions during the path search, which further alleviates the sparseness problem of KGs. The experimental results on five datasets sampled from Freebase, NELL and Wikidata show that our method outperforms state-of-the-art baselines. Our codes and datasets can be obtained from https://github.com/THU-KEG/DacKGR
