A Schema-aware Logic Reformulation for Graph Reachability
Davide Di Pierro, Stephan Mennicke, Stefano Ferilli
TL;DR
The paper addresses efficient graph reachability in schema-rich LPGs by proposing a schema-aware first-order logic reformulation implemented in GraphBRAIN. It precomputes label-distance between entities, sorts and prunes arcs accordingly (improved_arcs and improved_reach), and translates the setup to Horn clauses that can be processed by Prolog/ASP engines. Empirical results on GraphBRAIN and Twitter LPG datasets show substantial time and backtracking savings in well-structured schemas (e.g., up to 100x speedups and >75% time savings in GB), while still benefiting cases with simpler schemas. The work demonstrates that incorporating schema-level knowledge into the search strategy can significantly improve reachability tasks, with broad applicability across schema-aware graph analytics and reasoning systems.
Abstract
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.