Graph reachability is the task of understanding whether two distinct points in a graph are interconnected by arcs to which in general a semantic is attached. Reachability has plenty of applications, ranging from motion planning to routing. Improving reachability requires structural knowledge of relations so as to avoid the complexity of traditional depth-first and breadth-first strategies, implemented in logic languages. In some contexts, graphs are enriched with their schema definitions establishing domain and range for every arc. The introduction of a schema-aware formalization for guiding the search may result in a sensitive improvement by cutting out unuseful paths and prioritising those that, in principle, reach the target earlier. In this work, we propose a strategy to automatically exclude and sort certain graph paths by exploiting the higher-level conceptualization of instances. The aim is to obtain a new first-order logic reformulation of the graph reachability scenario, capable of improving the traditional algorithms in terms of time, space requirements, and number of backtracks. The experiments exhibit the expected advantages of the approach in reducing the number of backtracks during the search strategy, resulting in saving time and space as well.

翻译：图可达性任务旨在判断图中两个不同节点是否通过通常具有语义的弧相互连接。可达性具有广泛的应用，从运动规划到路由选择。改进可达性需要关系结构知识，以避免在逻辑语言中实现的传统深度优先和广度优先策略的复杂性。在某些场景中，图通过模式定义得到增强，为每条弧建立定义域和值域。引入模式感知的形式化方法来指导搜索，可通过剔除无效路径并优先选择原则上能更早抵达目标的路径，实现显著改进。本文提出一种策略，通过利用实例的高层概念化来自动排除和排序特定图路径。目标是获得图可达性场景的新一阶逻辑重构，能够在时间、空间需求和回溯次数方面改进传统算法。实验结果表明，该方法在减少搜索策略中的回溯次数方面具有预期优势，同时实现了时间和空间的节约。