Abstract
Organizing a large-scale knowledge graph into a typed property graph requires structural decisions -- which entities become nodes, which properties become edges, and what schema governs these choices. Existing approaches embed these decisions in pipeline code or extract relations ad hoc, producing schemas that are tightly coupled to their construction process and difficult to reuse for downstream ontology-level tasks. We present an ontology-oriented approach in which the schema is designed from the outset for ontology analysis, entity disambiguation, domain customization, and LLM-guided extraction -- not merely as a byproduct of graph building. The core mechanism is intrinsic-relational routing, which classifies every property as either intrinsic or relational and routes it to the corresponding schema module. This routing produces a declarative schema that is portable across storage backends and independently reusable. We instantiate the approach on the January 2026 Wikidata dump. A rule-based cleaning stage identifies a 34.6M-entity core set from the full dump, followed by iterative intrinsic-relational routing that assigns each property to one of 94 modules organized into 8 categories. With tool-augmented LLM support and human review, the schema reaches 93.3% category coverage and 98.0% module assignment among classified entities. Exporting this schema yields a property graph with 34.0M nodes and 61.2M edges across 38 relationship types. We validate the ontology-oriented claim through five applications that consume the schema independently of the construction pipeline: ontology structure analysis, benchmark annotation auditing, entity disambiguation, domain customization, and LLM-guided extraction.
Abstract (translated)
将大规模知识图谱组织为类型化属性图需要做出结构性决策——哪些实体成为节点，哪些属性成为边，以及由何种模式来规范这些选择。现有方法将这些决策嵌入流水线代码或临时提取关系，产生的模式与其构建过程紧密耦合，难以在后续本体级任务中复用。我们提出一种面向本体的方法，其模式从一开始就为本体分析、实体消歧、领域定制和LLM引导的提取而设计——而不仅仅是图构建的副产品。核心机制是内嵌-关系路由，它将每个属性分类为内嵌属性或关系属性，并将其路由到相应的模式模块。这种路由产生了一种声明式模式，可跨存储后端移植，并能独立复用。我们在2026年1月的Wikidata数据转储上实现了该方法。基于规则的清洗阶段从完整转储中识别出3460万实体的核心集合，随后进行迭代内嵌-关系路由，将每个属性分配到按8个类别组织的94个模块中。在工具增强的LLM支持和人工审核下，该模式在已分类实体中达到93.3%的类别覆盖率和98.0%的模块分配率。导出此模式可生成一个包含3400万个节点和6120万条边、涵盖38种关系类型的属性图。我们通过五种独立于构建流水线消费该模式的应用来验证面向本体的主张：本体结构分析、基准标注审计、实体消歧、领域定制和LLM引导的提取。
URL
https://arxiv.org/abs/2604.02618
