将知识图谱中的实体和关系进行向量化表达(KG Embedding),可以更加方便的处理知识图谱。大量的算法从不同的角度对知识图谱进行了表征。可以将他们分成三个类别:triplet-based,GCN-based和path-based。 triplet-based是基于知识图谱中单个的三元组进行表征,比如经典的TransE[1]模型。给定一个三元组(s, r, 0),TransE模...
1.基于距离的打分函数(distance-based) 通过计算实体间的距离来衡量三元组置信度,其中基于加性平移的关系模型应用最广:\bold{h}+\bold{r} \approx \bold{t}。 最符合直觉的方法是使用实体的关系映射之间欧氏距离作为度量。结构化嵌入模型SE(Structural Embedding)使用两个映射矩阵和L_1距离来学习表征:f_r(h,t...
In this paper, KGEs are reconciled with a specific loss function (Soft Margin) and examined with respect to their performance for co-authorship link prediction task on scholarly KGs. The results show a significant improvement in the accuracy of the experimented KGE models on the considered ...
基于Embedding的实体对齐前瞻 摘录论文:Sun, Zequn, et al. “A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs.” arXiv preprint arXiv:2003.07743 (2020). 无监督实体对齐 Unsupervised entity alignment 目前大多数方法需要seed alignment作为监督。因此,研究无监督的实体对齐是一个有...
当KGE训练完成时, 实体和关系的表示将会固定下来, 这样才能保存住KG的信息. 若继续在后续训练时更新Embedding, 将会对原有信息扰动. 所以KGE只是做了空间指示作用.Neural Network Based Predicate Representation Learning有了KGE中获取的 P, E, 接着需要将自然语言中的谓词表示(在三元组中也是关系)与KGE空间相对齐...
论文主要考虑过去的TKGC(Temporal KG Completion)模型学习模式的三个问题: 1、之前的方法并没有显式地将TKGC构造为一种增量学习问题——使模型能够适应训练数据的变化并能有效保留之前所学。它们将TKGC任务简单构造成KGC任务,仅仅在新的KG快照上对模型进行微调,导致灾难性遗忘。这点我不能苟同,既然在新的时间步下...
real KGs. Hence, we propose a new KG sampling algorithm, with which we generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation. This study also produces an open-source library, which includes 12 representative embedding-based entity ...
Flow-based 生成模型 那么,如何无监督情况下充分利用BERT表示中的语义信息?为了解决上述存在的问题,作者提出了一种将BERT embedding空间映射到一个标准高斯隐空间的方法(如下图所示),并称之为“BERT-flow”。而选择 Gaussian 空间的动机也是因为其自身的特点: ...
However, two main challenges persist: i) Neglecting complex higher-order interactions in the KG-based user-item network, potentially leading to sub-optimal recommendations, and ii) Dealing with the heterogeneous modalities of input sources, such as user-item bipartite graphs and KGs, which may intr...
In knowledge graph embedding, multidimensional representations of entities and relations are learned in vector space. Although distance-based graph embeddi