Knowledge Graphs (KGs) have found many applications in industrial and in academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer from incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among entities already in the KG. Among the recent LP techniques, those based on KG embeddings have achieved very promising performance in some benchmarks. Despite the fast-growing literature on the subject, insufficient attention has been paid to the effect of the design choices in those methods. Moreover, the standard practice in this area is to report accuracy by aggregating over a large number of test facts in which some entities are vastly more represented than others; this allows LP methods to exhibit good results by just attending to structural properties that include such entities, while ignoring the remaining majority of the KG. This analysis provides a comprehensive comparison of embedding-based LP methods, extending the dimensions of analysis beyond what is commonly available in the literature. We experimentally compare the effectiveness and efficiency of 18 state-of-the-art methods, consider a rule-based baseline, and report detailed analysis over the most popular benchmarks in the literature.

中文翻译：

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链接预测的知识图嵌入

知识图谱 (KGs) 在工业和学术环境中发现了许多应用，这反过来又推动了从各种来源进行大规模信息提取的大量研究工作。尽管做出了这些努力，但众所周知，即使是最大的 KG 也存在不完整的问题。链接预测 (LP) 技术通过识别 KG 中已有实体之间缺失的事实来解决这个问题。在最近的 LP 技术中，基于KG 嵌入在一些基准测试中取得了非常有希望的表现。尽管有关该主题的文献快速增长，但对这些方法中设计选择的影响关注不足。此外，该领域的标准做法是通过汇总大量测试事实来报告准确性，其中一些实体比其他实体更具代表性；这允许 LP 方法通过只关注包含此类实体的结构属性来展示良好的结果，同时忽略 KG 的剩余大部分。该分析提供了基于嵌入的 LP 方法的全面比较，将分析的维度扩展到了文献中通常可用的范围之外。我们通过实验比较了 18 种最先进方法的有效性和效率，考虑了基于规则的基线，