Answering complex logical queries on incomplete knowledge graphs is a
challenging task, and has been widely studied. Embedding-based methods require
training on complex queries, and cannot generalize well to out-of-distribution
query structures. Recent work frames this task as an end-to-end optimization
problem, and it only requires a pretrained link predictor. However, due to the
exponentially large combinatorial search space, the optimal solution can only
be approximated, limiting the final accuracy. In this work, we propose QTO
(Query Computation Tree Optimization) that can efficiently find the exact
optimal solution. QTO finds the optimal solution by a forward-backward
propagation on the tree-like computation graph, i.e., query computation tree.
In particular, QTO utilizes the independence encoded in the query computation
tree to reduce the search space, where only local computations are involved
during the optimization procedure. Experiments on 3 datasets show that QTO
obtains state-of-the-art performance on complex query answering, outperforming
previous best results by an average of 22%. Moreover, QTO can interpret the
intermediate solutions for each of the one-hop atoms in the query with over 90%
accuracy.

QTO is an efficient method for optimizing query computation trees in knowledge graphs, which achieves state-of-the-art performance and can interpret intermediate solutions with high accuracy.

通过查询计算树优化在知识图谱上回答复杂逻辑查询

Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization

We address the question of convergence in the loopy belief propagation (LBP)
algorithm. Specifically, we relate convergence of LBP to the existence of a
weak limit for a sequence of Gibbs measures defined on the LBP s associated
computation tree.Using tools FROM the theory OF Gibbs measures we develop
easily testable sufficient conditions FOR convergence.The failure OF
convergence OF LBP implies the existence OF multiple phases FOR the associated
Gibbs specification.These results give new insight INTO the mechanics OF the
algorithm.

通过在 loopy belief propagation 算法中建立 Gibbs 度量的依据，我们探讨了算法的收敛性并开发了易于测试的充分条件。收敛的失败意味着 Gibbs 规范的多相存在。这些结果为算法的机理提供了新的见解。