带证据的知识图谱验证
Evidence-Carrying Validation for Knowledge Graphs
AI总结：
针对知识图谱现有验证接口无法说明检查通过原因的问题，提出带证据的验证接口，在SHACL验证器Shifty中实现，实验表明其开销为仅一致性验证的1.54-2.07倍，可辅助程序诊断并修复知识图谱缺失信息。
AI中文摘要：
使用自身不维护的知识图谱的程序，如应用、创作平台和大语言模型（LLM）智能体，需要知晓该图谱是否包含其任务所需的信息。依据模式（schema）验证图谱可回答该问题，但现有验证接口通常仅返回一致性位或面向失败的报告，未说明检查通过的原因或失败背后的部分匹配情况。我们提出一种带证据的验证接口：每个选定的节点形状（node-shape）检查会返回一致性跟踪（satisfaction trace）或失败见证（failure witness），二者为保留约束、基数决策、路径及支持三元组的互递归对象。我们在实验性SHACL验证器Shifty中实现了该接口。针对两个真实世界的形状图语料库，生成全对证据的中位数开销为仅一致性验证的1.54至2.07倍。案例研究显示，程序可结合通过与失败的证据，诊断缺失信息并指导修复。
英文摘要：
Programs that consume a knowledge graph they do not maintain, such as applications, authoring platforms, and LLM agents, need to know whether the graph contains the information their task requires. Validating the graph against a schema can answer this question, but existing validation interfaces usually return a conformance bit or failure-oriented report without identifying why checks pass or the partial matches behind failures. We present an evidence-carrying validation interface: every selected node-shape check returns either a satisfaction trace or failure witness. These are mutually recursive objects that retain constraints, cardinality decisions, paths, and supporting triples. We implement this interface in Shifty, an experimental SHACL validator. Against two real-world shape graph corpora, materializing all-pair evidence costs a median 1.54-2.07X conformance-only validation. A case study then shows how programs combine passing and failing evidence to diagnose missing information and guide repair.
