SAQE: Complex Logical Query Answering via Semantic-Aware Representation Learning

Performing complex First-Order Logic (FOL) queries on knowledge graphs is crucial for advancing knowledge reasoning. Knowledge graphs encapsulate rich semantic interactions among entities, encompassing both explicit structural knowledge represented by triples $(e_{1}, r, e_{2})$ and implicit relational knowledge through multi-hop paths $(e_{1} \stackrel{r_{1}}{\rightarrow } \cdots e_{3} \cdots \stackrel{r_{2}}{\rightarrow } e_{2})$. Traditional models often focus solely on either triple-level or path-level knowledge, overlooking the benefits of integrating both to enhance logic query answering. This oversight leads to suboptimal representation learning and inefficient query reasoning. To overcome these challenges, we introduce a new Semantic-Aware representation learning model for Query-answering Embeddings (SAQE). Specifically, SAQE employs a joint learning approach that integrates triple-level and path-level knowledge semantics and captures both explicit and implicit contextual nuances within the knowledge graph, yielding more accurate and contextually relevant representations. To efficiently handle the large combinatorial search spaces in FOL reasoning, we propose a novel hierarchical reasoning optimization strategy by a multi-hop tree thus optimizing subqueries rooted at variable nodes in a divide-and-conquer manner. Theoretical analysis confirms that SAQE effectively supports various types of FOL reasoning and enhances generalizations for query answering. Extensive experiments demonstrate that our model achieves state-of-the-art performance across several established datasets.