从图检索到模式实现：面向异构知识图谱的文本到SPARQL的反事实验证
From Graph Retrieval to Schema Realization: Counterfactual Validation for Text-to-SPARQL over Heterogeneous Knowledge Graphs
AI总结：
提出SchemaForge框架，通过问题条件化的模式切片对齐和反事实验证，在异构知识图谱上提升文本到SPARQL查询生成的执行准确率。
摘要：
文本到SPARQL将自然语言问题映射为RDF知识图谱上的可执行SPARQL查询。标准评估通常预先固定目标图，但实际知识图谱问答（KGQA）可能涉及具有不同模式、部分对齐和不完整元数据的异构图集合。在此设置下，查询生成不仅依赖于SPARQL语法：系统必须识别能够支持问题所需的谓词、实体类型、连接、过滤器和约束的图模式。我们提出SchemaForge，一个面向异构KG集合的文本到SPARQL的基于模式的智能体框架。其核心机制是问题条件化的模式切片对齐：弱图证据首先识别可能的图，而更强的模式证据确定局部模式切片能否实现预期查询。选定的模式切片随后在执行前约束查询生成和验证。当仅有一个图可用时，该公式简化为带有模式基础的标准单KG文本到SPARQL。我们在LC-QuAD 2.0、QALD-9 Plus、QALD-10和Spider4SPARQL上评估SchemaForge。在四个公开基准上，SchemaForge相比最强匹配的智能体基线平均提高执行准确率11.50个百分点。在Spider4SPARQL上，SchemaForge将执行准确率从54.86%提升至64.18%，并达到73.0%的Top-1和97.0%的Top-3图分配准确率。这些结果表明，从弱图证据转向模式特定的查询承诺，结合反事实答案集检查，改进了异构知识图谱上的可执行查询生成。
英文摘要：
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. We present SchemaForge, a schema-grounded agentic framework for text-to-SPARQL over heterogeneous KG collections. Its central mechanism is question-conditioned schema-slice alignment: weak graph evidence first identifies plausible graphs, while stronger schema evidence determines whether a local schema slice can realize the intended query. The selected schema slice then constrains query generation and verification before execution. When only one graph is available, the same formulation reduces to standard single-KG text-to-SPARQL with schema grounding. We evaluate SchemaForge on LC-QuAD 2.0, QALD-9 Plus, QALD-10, and Spider4SPARQL. Across the four public benchmarks, SchemaForge improves execution accuracy over the strongest matched agent baseline by 11.50 percentage points on average. On Spider4SPARQL, SchemaForge improves execution accuracy from 54.86% to 64.18% and achieves 73.0% Top-1 and 97.0% Top-3 graph allocation accuracy. These results show that moving from weak graph evidence to schema-specific query commitments, together with counterfactual answer-set checks, improves executable query generation over heterogeneous knowledge graphs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
