graph query authoring

Existing knowledge graph visualization tools inadequately support the dynamic evolution of user query intent during exploratory search. To address this, we propose an interactive visual query interface grounded in graph-difference computation: it incrementally computes and visualizes structural changes between successive SPARQL query graphs, simultaneously rendering shifts in query logic, natural-language requirement descriptions, and result distributions/instances—thereby enabling tripartite awareness of intent, structure, and outcome evolution. The system integrates natural-language interaction, graph-difference encoding, SPARQL generation, and multi-granularity result comparison, forming a cohesive end-to-end visual analytics pipeline. Case studies across diverse ontologies demonstrate that our approach significantly improves users’ exploration efficiency and semantic comprehension depth when navigating complex knowledge graphs.

本文提出一种将基于本体的属性图查询重写为GQL的方法，解决了现有图查询语言缺乏导航和路径匹配功能的问题。

This paper addresses the lack of a rigorous theoretical foundation for graph query languages (GQL and SQL/PGQ). To this end, it introduces the first lightweight graph pattern calculus (GPC) tailored to property graphs. GPC systematically abstracts the common semantic core of pattern matching across GQL and SQL/PGQ, achieving expressive power while enabling formal verification. The authors develop a sound and complete type system alongside both operational and denotational semantics, rigorously defining syntax, typing rules, and semantics. They establish key algebraic properties—including pattern equivalence and composability—thereby ensuring mathematical robustness. GPC provides a verifiable theoretical basis for correctness proofs, query optimization, and standardized language extensions, effectively bridging a critical gap in the formal modeling of industrial-grade graph query languages.

The data complexity of GQL—the standardized query language for graph databases—has long lacked a rigorous theoretical characterization. Method: We establish a unified relational-logic framework for graph queries, embedding full GQL (including arithmetic extensions) into FO[TC] + ESO. We introduce the *Restricted Quantifier Collapse* (RQC), a general technique grounded in finite model theory, and employ register automata modeling coupled with schema validation to analyze query evaluation. Contribution/Results: Our approach yields tight data complexity bounds for GQL and regular path queries (e.g., NL-completeness), and precisely captures regular path queries within FO[TC] while preserving NL complexity. This work provides the first systematic logical foundation for analyzing graph database query complexity, delivering both a unified formal framework and asymptotically optimal, tight complexity characterizations.

Path matching in graph query languages (e.g., Cypher, SQL/PGQ, GQL) lacks a unified and efficient processing mechanism—particularly when supporting complex path semantics (e.g., shortest paths, simple paths) and regular-expression constraints on edge labels—posing dual challenges in expressive power and performance. This paper introduces the first cross-language, general-purpose path-solving framework. It features a compact symbolic path representation and integrates dynamic-programming-based enumeration, incremental pipelined execution, and regex compilation optimizations to enable unified modeling and efficient evaluation of diverse path semantics and edge-label constraints. Experimental evaluation on real-world datasets and complex queries demonstrates an order-of-magnitude speedup over state-of-the-art graph engines, while maintaining high expressiveness, strong scalability, and behavioral stability.

Existing graph databases lack effective support for the tree-shaped substructures commonly found in property graphs. This work addresses this limitation by treating such tree substructures as first-class citizens and proposes a systematic management framework encompassing modeling, indexing, and query optimization. Drawing inspiration from XML structural indexing techniques, the approach enables efficient path queries within a relational graph database backend. Experimental evaluation demonstrates that the proposed method significantly improves path query performance, thereby validating the potential of structural indexing to enhance graph data management.

This work addresses the limited expressiveness of existing graph query languages—such as GQL and SQL/PGQ—which lack full compositionality and cannot capture complex path queries within the NLOGSPACE complexity class. To overcome this limitation, the paper introduces a novel query language that unifies graph pattern matching with relational querying through two key innovations: regular path queries enriched with variables and data-value comparisons, and a #Datalog-based graph transformation mechanism capable of constructing nodes, edges, and paths. This combination enables, for the first time, a systematically compositional approach to graph querying that precisely captures the full expressive power of NLOGSPACE. The proposed language not only resolves fundamental expressiveness gaps in current standards but also offers a practical and theoretically grounded extension pathway for both GQL and SQL/PGQ.

This work addresses the lack of theoretical foundations and decision procedures for the satisfiability of Basic Graph Patterns (BGPs) in Façade-X, a framework that enables SPARQL querying over heterogeneous data sources. To bridge this gap, the paper presents the first formal theory for BGP satisfiability in Façade-X, introducing a logical analysis framework grounded in an RDF metamodel and an efficient decision algorithm. A prototype system implementing the proposed approach is developed and evaluated. Experimental results demonstrate that the method effectively handles real-world queries and significantly improves query execution efficiency, thereby filling a critical theoretical void in semantic analysis for SPARQL-based heterogeneous data integration.

This work presents the first systematic approach to instance-free schema inference under property graph query transformations. Given a ProGS input schema and a G-CORE query, the authors propose a multi-layer mapping technique that translates property graphs, schemas, and queries into RDF, SHACL, and SPARQL CONSTRUCT representations, respectively, enabling automatic derivation of structural constraints on the output graph via description logic reasoning. By leveraging RDF reification and cross-language semantic bridging, the method establishes a sound and semantically equivalent metatheoretical foundation. This enables generic output schema inference applicable to any input graph conforming to the given schema, while formally verifying both the correctness of the derived constraints and the semantic fidelity of the mappings.

本文解决了GQL标准难以形式化推理的问题，通过提出MGQL，一种基于ISO/IEC 39075标准的小步操作语义，支持大部分GQL查询特性。