Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result in insufficient training of long-tail entities. However, unstructured text encoding entities and relational knowledge can be obtained anywhere in large quantities. Word vectors of entity names estimated from the unlabelled raw text using natural language model encode syntax and semantic properties of entities. Yet since these feature vectors are estimated through minimizing prediction error on unsupervised entity names, they may not be the best for knowledge graphs. We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge representation. Experiments on Freebase show that our method can rely less on the labelled data and outperforms the baselines when the labelled data is relatively less. Especially, it is applicable to zero-shot scenario.

中文翻译：

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具有嵌入子空间的知识图的表示学习

大多数现有的知识图嵌入模型都是有监督的方法，并且在很大程度上依赖于可获得的标记训练数据的质量和数量。获取高质量三元组的成本高，数据源面临严重的数据稀疏问题，可能导致长尾实体的训练不足。但是，可以在任何地方大量获取非结构化文本编码实体和关系知识。使用自然语言模型从未标记的原始文本中估计的实体名称的词向量对实体的语法和语义属性进行编码。然而，由于这些特征向量是通过最小化无监督实体名称的预测误差来估计的，因此它们可能不是知识图谱的最佳选择。我们提出了一种两阶段方法，使无监督实体名称嵌入适应知识图子空间，并共同学习自适应矩阵和知识表示。在 Freebase 上的实验表明，当标记数据相对较少时，我们的方法可以较少依赖标记数据并优于基线。尤其适用于零镜头场景。