Preliminary experiments with the proof-of-concept prototype suggest that querying graph data with ontological knowledge may finally be within reach.
Abstract
Ontology-based data access is intended for graph data, but practical support remains limited to SQL-like languages lacking the navigational and path-matching features fundamental to graph querying. Theoretical algorithms for navigational queries have long been available. Still, they have never been implemented, largely because practical graph query languages fell short of their theoretical counterparts and lacked the expressive power needed to support rewriting common ontology languages. The recent standardisation efforts around GQL and SQL/PGQ finally allow us to overcome this barrier and present a practical query rewriting technique for ontology-mediated navigational graph queries. Ontologies are written in a DL-Lite variant tuned to property graphs, and the queries in a GQL fragment with nested two-way regular path queries and which can be evaluated in Cypher. Preliminary experiments with our proof-of-concept prototype suggest that querying graph data with ontological knowledge may finally be within reach.
MGQL is presented, the first mechanized, small-step operational semantics for a substantial read-only fragment of GQL that is grounded in the ISO/IEC 39075 standard, and it is proved that the type system is sound, ensuring an end-to-end guarantee of well-formed queries yielding results that conform to their declared schemas.
OntoExpand is introduced, a new methodology for ontology expansion that uses SPARQL CONSTRUCT queries as an efficient alternative to conventional reasoning techniques that improves performance, reduces computational overhead, is pattern driven allowing a more granular expansion control and seamlessly integrates with SPARQL endpoints.
Vitor Lelis, N. Leite, José Carlos et al.· 0 citations
This paper studies the VKG-QA task, which enables users to interact with the VKGs through a natural language (NL) interface by translating their questions into SPARQL queries, and pro-poses NaVQA (Navigation-based VKG Question Answering), a framework leveraging Large Language Models.
Guohui Xiao, Haohan Xue, Lin Ren et al.· 0 citations
Labeled Property Graphs (LPGs) have gained widespread adoption, supported by the ongoing standardization of the Graph Query Language (GQL). As LPGs are frequently constructed through the integration of heterogeneous data sources, inconsistencies and errors may propagate into query results. To explain and validate such results, it is essential to determine which parts of the input graph contributed to a given answer - an aspect captured by data provenance. In this paper, we present a formal definition of why-provenance tailored specifically to GQL, characterizing the fine-grained subgraph that contribute to a query answer. Building on the Core-GQL algebraic foundation, we further propose an efficient query-rewriting-based algorithm for computing provenance, leveraging the native execution capabilities of existing GQL-compliant graph database systems. Our extensive experimental evaluation demonstrates the feasibility and scalability of our approach, showing that provenance can be computed with moderate overhead that scales effectively with graph size.
Koumudi Ganepola, Maxime Jakubowski, Katja Hose· Proceedings of the VLDB Endo...· 0 citations
A two-stage LLM-assisted workflow for French maintenance regulations is presented: ontology engineering from a SEMLEG-based core ontology, followed by construction of an ontology-grounded French legal knowledge graph.
Génesis Montenegro, M. Billami, Catherine Faron et al.· arXiv.org· 0 citations
