Querying for Paths in Graphs using Context-Free Path Queries

TL;DR

This paper introduces path-based semantics for context-free path queries in graphs, enabling more detailed insights into graph structures by considering all or specific paths, and proposes algorithms for efficient query evaluation.

Contribution

It formalizes all-path and single-path query semantics for context-free path queries and develops algorithms for their evaluation, extending beyond traditional relational semantics.

Findings

Path-based semantics provide richer graph insights.

Finite context-free grammars can represent query results.

Feasible evaluation methods for large query results.

Abstract

Navigational queries for graph-structured data, such as the regular path queries and the context-free path queries, are usually evaluated to a relation of node-pairs $(m, n)$ such that there is a path from $m$ to $n$ satisfying the conditions of the query. Although this relational query semantics has practical value, we believe that the relational query semantics can only provide limited insight in the structure of the graph data. To address the limits of the relational query semantics, we introduce the all-path query semantics and the single-path query semantics. Under these path-based query semantics, a query is evaluated to all paths satisfying the conditions of the query, or, respectively, to a single such path. While focusing on context-free path queries, we provide a formal framework for evaluating queries on graphs using both path-based query semantics. For the all-path query semantics, we show that the result of a query can be represented by a finite context-free grammar annotated with node-information relevant for deriving each path in the query result. For the single-path query semantics, we propose to search for a path of minimum length. We reduce the problem of finding such a path of minimum length to finding a string of minimum length in a context-free language, and for deriving such a string we propose a novel algorithm. Our initial results show that the path-based query semantics have added practical value and that query evaluation for both path-based query semantics is feasible, even when query results grow very large. For the single-path query semantics, determining strict worst-case upper bounds on the size of the query result remains the focus of future work.