Motivated by the incompleteness of modern knowledge graphs, a new setup for query answering has emerged, where the goal is to predict answers that do not necessarily appear in the knowledge graph, but are present in its completion. In this paper, we formally introduce and study two query answering problems, namely, query answer classification and query answer retrieval. To solve these problems, we propose AnyCQ, a model that can classify answers to any conjunctive query on any knowledge graph. At the core of our framework lies a graph neural network trained using a reinforcement learning objective to answer Boolean queries. Trained only on simple, small instances, AnyCQ generalizes to large queries of arbitrary structure, reliably classifying and retrieving answers to queries that existing approaches fail to handle. This is empirically validated through our newly proposed, challenging benchmarks. Finally, we empirically show that AnyCQ can effectively transfer to completely novel knowledge graphs when equipped with an appropriate link prediction model, highlighting its potential for querying incomplete data.

翻译：受现代知识图谱不完备性的驱动，一种新的查询应答设置应运而生，其目标是预测那些不一定出现在知识图谱中，但存在于其完备化版本中的答案。在本文中，我们正式提出并研究了两个查询应答问题，即查询答案分类和查询答案检索。为解决这些问题，我们提出了AnyCQ模型，该模型能够对任何知识图谱上的任意合取查询的答案进行分类。我们框架的核心是一个使用强化学习目标训练的图神经网络，用于回答布尔查询。AnyCQ仅在简单、小规模的实例上进行训练，却能泛化到任意结构的大型查询，可靠地对现有方法无法处理的查询答案进行分类和检索。这一点通过我们新提出的、具有挑战性的基准测试得到了实证验证。最后，我们通过实验证明，当配备适当的链接预测模型时，AnyCQ能够有效地迁移到全新的知识图谱上，突显了其在查询不完备数据方面的潜力。