Knowledge Graphs are a widely used method to represent relations between entities in various AI applications, and Graph Embedding has rapidly become a standard technique to represent Knowledge Graphs in such a way as to facilitate inferences and decisions. As this representation is obtained from behavioural data, and is not in a form readable by humans, there is a concern that it might incorporate unintended information that could lead to biases. We propose EXTRACT: a suite of Explainable and Transparent methods to ConTrol bias in knowledge graph embeddings, so as to assess and decrease the implicit presence of protected information. Our method uses Canonical Correlation Analysis (CCA) to investigate the presence, extent and origins of information leaks during training, then decomposes embeddings into a sum of their private attributes by solving a linear system. Our experiments, performed on the MovieLens1M dataset, show that a range of personal attributes can be inferred from a user's viewing behaviour and preferences, including gender, age, and occupation. Further experiments, performed on the KG20C citation dataset, show that the information about the conference in which a paper was published can be inferred from the citation network of that article. We propose four transparent methods to maintain the capability of the embedding to make the intended predictions without retaining unwanted information. A trade-off between these two goals is observed.

翻译：知识图谱是广泛用于表示人工智能应用中实体间关系的方法，图嵌入已迅速成为以促进推理和决策的方式表示知识图谱的标准技术。由于这种表示源于行为数据且非人类可读形式，存在嵌入意外信息从而导致偏差的担忧。我们提出EXTRACT：一套可解释透明的方法套件，用于控制知识图谱嵌入中的偏差，以评估并减少受保护信息的隐式存在。本方法采用典型相关分析（CCA）探究训练过程中信息泄露的存在性、程度及来源，进而通过求解线性系统将嵌入分解为私有属性之和。在MovieLens1M数据集上的实验表明，可从用户观影行为与偏好推断出包括性别、年龄和职业在内的一系列个人信息。在KG20C引文数据集上的拓展实验显示，文章所属会议信息可从其引文网络中推断。我们提出四种透明方法，使嵌入在保持预期预测能力的同时不保留冗余信息，并观察到这两个目标之间的权衡关系。