Abstract
Knowledge Graphs (KGs) are foundational to applications such as search, question answering, and recommendation. Conventional knowledge graph construction methods are predominantly static, rely ing on a single-step construction from a fixed corpus with a prede f ined schema. However, such methods are suboptimal for real-world sce narios where data arrives dynamically, as incorporating new informa tion requires complete and computationally expensive graph reconstruc tions. Furthermore, predefined schemas hinder the flexibility of knowl edge graph construction. To address these limitations, we introduce DIAL KG, a closed-loop framework for incremental KG construction orches trated by a Meta-Knowledge Base (MKB). The framework oper ates in a three-stage cycle: (i) Dual-Track Extraction, which ensures knowledge completeness by defaulting to triple generation and switching to event extraction for complex knowledge; (ii) Governance Adjudica tion, which ensures the fidelity and currency of extracted facts to prevent hallucinations and knowledge staleness; and (iii) Schema Evolution, in which new schemas are induced from validated knowledge to guide subsequent construction cycles, and knowledge from the current round is incrementally applied to the existing KG. Extensive experiments demon strate that our framework achieves state-of-the-art (SOTA) performance in the quality of both the constructed graph and the induced schemas.
Abstract (translated)
知识图谱（KGs）是搜索、问答和推荐等应用的基础。传统的知识图谱构建方法主要是静态的，依赖于从固定语料库中基于预定义模式进行单步构建。然而，在数据动态到达的真实场景中，这类方法并非最优，因为整合新信息需要完整且计算成本高昂的图重建。此外，预定义模式也限制了知识图谱构建的灵活性。为应对这些局限，我们提出了DIAL KG——一个由元知识库（MKB）协调的增量式知识图谱构建闭环框架。该框架运行于三阶段循环：(i) 双轨提取，通过默认生成三元组并在复杂知识场景切换为事件提取来确保知识完整性；(ii) 治理裁决，确保提取事实的真实性与时效性，防止幻觉与知识过时；(iii) 模式演化，从验证知识中归纳新模式以指导后续构建循环，并将当前轮次的知识增量式整合至现有图谱。大量实验表明，该框架在构建图谱质量与归纳模式方面均达到最先进（SOTA）性能。
URL
https://arxiv.org/abs/2603.20059
