提出一套工业级知识图谱构建流程，打通异构数据与专家知识
Procedure Model for Building Knowledge Graphs for Industry Applications
- 基于行业标准流程，分步骤整合业务数据与领域知识
- 通过能力问题驱动，确保图谱覆盖真实业务需求
- 适合企业数据工程师与业务专家协同构建知识图谱
企业知识图谱通过语义网络连接概念、属性、个体与关系，融合业务数据与组织知识，为智能商业应用提供新洞察。然而，知识图谱构建成本高，需领域与技术专家协作。本文提出一种面向工业应用场景的RDF知识图谱构建步骤化流程模型，该模型以跨行业数据挖掘标准流程为基础，全程使用能力问题引导开发。流程始于业务与数据理解，涵盖本体建模、图谱搭建任务，并包含评估与部署阶段，形成可闭环运行的自包含体系。
原文摘要 · Abstract (English)
Enterprise knowledge graphs combine business data and organizational knowledge by means of a semantic network of concepts, properties, individuals and relationships. The graph-based integration of previously unconnected information with domain knowledge provides new insights and enables intelligent business applications. However, knowledge graph construction is a large investment which requires a joint effort of domain and technical experts. This paper presents a practical step-by-step procedure model for building an RDF knowledge graph that interconnects heterogeneous data and expert knowledge for an industry use case. The self-contained process adapts the "Cross Industry Standard Process for Data Mining" and uses competency questions throughout the entire development cycle. The procedure model starts with business and data understanding, describes tasks for ontology modeling and the graph setup, and ends with process steps for evaluation and deployment.
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