From Graph Retrieval to Schema Realization: Counterfactual Validation for Text-to-SPARQL over Heterogeneous Knowledge Graphs
arXiv:2508.01815v2 Announce Type: replace Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs. While standard evaluations often fix the target graph in advance, practical knowledge graph question answering (KGQA) may involve heterogeneous graph collections with different schemas, partial alignments, and incomplete metadata. In this setting, query generation depends on more than SPARQL syntax: the system must identify a graph schema that can support the predicates, entity types, joins, filters, and constraints required by the question.
The increasing complexity and heterogeneity of real-world knowledge graphs necessitate more robust and flexible methods for natural language interaction, moving beyond fixed schemas.
This development addresses a key limitation in current knowledge graph question answering, enabling AI systems to operate effectively across diverse and evolving data landscapes which is crucial for practical agentic applications.
The paradigm shifts from assuming a pre-defined target graph to requiring systems to dynamically identify and relate schemas, thereby enhancing the adaptability and real-world utility of Text-to-SPARQL systems.
- · AI agents developers
- · Knowledge graph providers
- · Data integration platforms
- · Enterprises with complex data
- · Fixed-schema KGQA systems
- · Manual data schema mapping
- · Simple data search solutions
Improved accuracy and flexibility of natural language interfaces for complex data.
Accelerated development and adoption of AI agents that can reason over heterogeneous information sources.
The emergence of new data integration and interaction paradigms, potentially creating more fluid, interconnected digital ecosystems.
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Read at arXiv cs.CL