Current methods for embedding-based query answering over incomplete Knowledge Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by learning patterns from the data, and lack the complementary ability to do deductive reasoning, which requires the application of domain knowledge to infer further information. To address this shortcoming, we investigate the problem of incorporating ontologies into embedding-based query answering models by defining the task of embedding-based ontology-mediated query answering. We propose various integration strategies into prominent representatives of embedding models that involve (1) different ontology-driven data augmentation techniques and (2) adaptation of the loss function to enforce the ontology axioms. We design novel benchmarks for the considered task based on the LUBM and the NELL KGs and evaluate our methods on them. The achieved improvements in the setting that requires both inductive and deductive reasoning are from 20% to 55% in HITS@3.

翻译：当前基于嵌入的不完全知识图谱（KG）查询回答方法仅关注归纳推理，即通过从数据中学习模式来预测答案，缺乏运用领域知识推断更多信息的演绎推理能力。为弥补这一不足，我们通过定义基于嵌入的本体中介查询回答任务，研究将本体整合到基于嵌入的查询回答模型中的问题。我们提出了多种与代表性嵌入模型集成的策略，包括：（1）基于本体的数据增强技术，以及（2）调整损失函数以强制执行本体公理。我们基于LUBM和NELL知识图谱设计了该任务的新型基准，并评估了我们的方法。在需要同时进行归纳与演绎推理的设置中，HITS@3指标提升了20%至55%。