Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs
This addresses the limitation of current methods that lack deductive reasoning for query answering in knowledge graphs, though it is incremental as it builds on existing embedding models.
The paper tackles the problem of embedding-based query answering over incomplete knowledge graphs by incorporating ontologies to enable both inductive and deductive reasoning, achieving improvements of 20% to 55% in HITS@3.
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.