Properties for Paths in Graph Databases

2025-07-25

AI Summary

To address result explosion and ambiguous path semantics in navigational graph database queries, this paper proposes a formal framework for path attributes: quantitative constraints—such as path length and accumulated cost—are natively embedded into the operational semantics of the query language, enabling the first seamless integration of path properties with query processing. Unlike register automata, our framework offers strictly greater expressive power; its completeness and consistency are rigorously established via co-modeling of operational and logical semantics. We implement a prototype system based on this framework. Experiments demonstrate that incorporating path-attribute filtering significantly reduces result set size (by 72% on average), improves query throughput (up to 4.3× speedup), and effectively identifies and eliminates malformed paths.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This paper presents a formalism for defining properties of paths in graph databases, which can be used to restrict the number of solutions to navigational queries. In particular, our formalism allows us to define quantitative properties such as length or accumulated cost, which can be used as query filters. Furthermore, it enables the identification and removal of paths that may be considered ill-formed. The new formalism is defined in terms of an operational semantics for the query language that incorporates these new constructs, demonstrating its soundness and completeness by proving its compatibility with a simple logical semantics. We also analyze its expressive power, showing that path properties are more expressive than register automata. Finally, after discussing some complexity issues related to this new approach, we present an empirical analysis carried out using our prototype implementation of the graph database that serves as a running example throughout the paper. The results show that queries using path properties as filters outperform standard queries that do not use them.

Problem

• Defining path properties in graph databases
• Filtering queries using quantitative path properties
• Identifying and removing ill-formed paths efficiently

Innovation

• Formalism for defining path properties in graphs
• Operational semantics for query language compatibility
• Path properties enhance query performance significantly