标题： 基于上下文感知查询表示学习的知识图谱多跳逻辑推理改进 显示英文标题

标题： Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning

摘要： 多跳逻辑推理在知识图谱上是自然语言处理中的关键任务，许多方法旨在回答一阶逻辑（FOL）查询。 最近基于几何（例如，盒子、圆锥）和概率（例如，贝塔分布）的方法已有效解决了复杂的FOL查询。 然而，这些方法面临的一个共同挑战在于确定这些查询的精确几何边界或概率参数。 这一挑战出现的原因是现有方法在其计算图中依赖于线性顺序操作，忽视了查询的逻辑结构以及可以从查询的关系中获得的关系诱导信息，我们称之为查询的上下文。 为了解决这个问题，我们提出了一种模型无关的方法，通过完全整合FOL查询图的上下文，提高现有多跳逻辑推理方法的有效性。 我们的方法独特地区分了（1）查询结构固有的结构上下文，以及（2）查询图中每个节点特有的关系诱导上下文，如相应知识图谱中所界定的。 这种双上下文范式有助于查询图中的节点在多跳推理步骤中获得更精细的内部表示。 通过在两个数据集上的实验，我们的方法持续提升了三种多跳推理基础模型，性能提升最高达19.5%。 我们的代码可在 https://github.com/kjh9503/caqr 获取。 显示英文摘要

摘要： Multi-hop logical reasoning on knowledge graphs is a pivotal task in natural language processing, with numerous approaches aiming to answer First-Order Logic (FOL) queries. Recent geometry (e.g., box, cone) and probability (e.g., beta distribution)-based methodologies have effectively addressed complex FOL queries. However, a common challenge across these methods lies in determining accurate geometric bounds or probability parameters for these queries. The challenge arises because existing methods rely on linear sequential operations within their computation graphs, overlooking the logical structure of the query and the relation-induced information that can be gleaned from the relations of the query, which we call the context of the query. To address the problem, we propose a model-agnostic methodology that enhances the effectiveness of existing multi-hop logical reasoning approaches by fully integrating the context of the FOL query graph. Our approach distinctively discerns (1) the structural context inherent to the query structure and (2) the relation-induced context unique to each node in the query graph as delineated in the corresponding knowledge graph. This dual-context paradigm helps nodes within a query graph attain refined internal representations throughout the multi-hop reasoning steps. Through experiments on two datasets, our method consistently enhances the three multi-hop reasoning foundation models, achieving performance improvements of up to 19.5%. Our code is available at https://github.com/kjh9503/caqr.