Implicit and Explicit Rule Injection for Complex Query Answering over Knowledge Graphs

Abstract

Complex Query Answering over incomplete knowledge graphs is a fundamental yet challenging task. Existing methods based on a pretrained knowledge graph embedding model have achieved good performance. However, they ignore logical rules. Logical rules, as part of the conceptual layer in knowledge graphs, contain rich background information that enhances logical reasoning and improves the performance of models. To address this problem, we propose a model that incorporates logical rules for complex query answering over knowledge graphs called R-CQA (Complex Query Answering with Rules). This model introduces implicit and explicit rule injection modules to the existing CQA models. The implicit rule injection module models logical rules as additional data for training and incorporates rule information into knowledge graph embedding model, which enhances inference. The explicit rule injection module uses logical rules to rewrite queries and constructs a query set for each original query, which avoids missing answers for inference. However, implicit rule injection does not really use logical rules for reasoning which decreases the interpretability and accuracy of logical rules. Explicit rule injection is influenced by the quantity and quality of logical rules. Therefore, we combine both methods to take advantage of their respective strengths. Experiments on 3 datasets demonstrate our model obtains state-of-the-art performance on complex query answering.