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Current methods for embedding-based query answering over incomplete KnowledgeGraphs (KGs) only focus on inductive reasoning, i.e., predicting answers bylearning patterns from the data, and lack the complementary ability to dodeductive reasoning, which requires the application of domain knowledge toinfer further information. To address this shortcoming, we investigate theproblem of incorporating ontologies into embedding-based query answering modelsby defining the task of embedding-based ontology-mediated query answering. Wepropose various integration strategies into prominent representatives ofembedding models that involve (1) different ontology-driven data augmentationtechniques and (2) adaptation of the loss function to enforce the ontologyaxioms. We design novel benchmarks for the considered task based on the LUBMand the NELL KGs and evaluate our methods on them. The achieved improvements inthe setting that requires both inductive and deductive reasoning are from 20%to 55% in HITS@3.

Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs