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 ...
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...
KG embeddings have been intensively investigated, mostly for KG completion, and have potential also for entity clustering. However, embeddings are latent and do not convey user-interpretable labels for clusters. This work presents ExCut, a novel approach that combines KG embeddings with rule mining ...
1 引出# 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...
3 KNOWLEDGE EMBEDDING BASED QA-KG KEQA通过三步完成目标:1)基于 Q 中的问题和他们的谓词的嵌入表示,KEQA训练一个谓词学习模型,其将一个问题作为输入并且返回一个在KG嵌入空间的 \hat{p}_{l} 作为预测谓词表示。相似的,头实体也能被构建为 \hat{e}_{h} 2)因为实体的数量非常多,KEQA使用一个Head Entity...
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...
We generate two versions of datasets for each pair of KGs to be aligned. V1 is generated by directly using the IDS algorithm. For V2, we first randomly delete entities with low degrees (d <= 5) in the source KG to make the average degree doubled, and then execute IDS to fit the ...
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...
摘要: alignment aims to construct a complete knowledge graph (KG) by matching the same entities in multi-source KGs. Existing researches on entity alignment mainly focuses on static multi-...关键词: Cross-lingual Entity alignment Graph Neural Networks Knowledge graph embedding Temporal knowledge ...
industry.zip |__ attr_triples_1 # attribute triples of KG1 |__ attr_triples_2 # attribute triples of KG2 |__ ent_links # entity links between KGs (ground-truth) |__ rel_triples_1 # relation triples of KG1 |__ rel_triples_2 # relation triples of KG2 ...