Ontology-based knowledge graphs (KG) are desirable for effective knowledge management and reuse in various decision making scenarios, including design. Creating and populating extensive KG based on specific ontological models can be highly labour and time-intensive unless automated processes are developed for knowledge extraction and graph creation. Most research and development on automated extraction and creation of KG is based on extensive unstructured data sets that provide contextual information. However, some of the most useful information about the products and services of a company has traditionally been recorded as structured data. Such structured data sets rarely follow a standard ontology, do not capture explicit mapping of relationships between the entities, and provide no contextual information. Therefore, this research reports a method and digital workflow developed to address this gap. The developed method and workflow employ rule-based techniques to extract and create a Function Behaviour-Structure (FBS) ontology-based KG from legacy structured data, especially specification sheets and product catalogues. The solution approach consists of two main components: a process for deriving context and context-based classification rules for FBS ontology concepts and a workflow for populating and retrieving the FBS ontology-based KG. KG and Natural Language Processing (NLP) are used to automate knowledge extraction, representation, and retrieval. The workflow's effectiveness is demonstrated via pilot implementation in an industrial context. Insights gained from the pilot study are reported regarding the challenges and opportunities, including discussing the FBS ontology and concepts.

翻译：基于本体的知识图谱（KG）对于各类决策场景（包括设计领域）中的有效知识管理与复用具有重要价值。基于特定本体模型创建并填充大规模知识图谱通常需要极高的人力与时间成本，除非开发出自动化的知识提取与图谱构建流程。当前关于知识图谱自动提取与创建的研究开发工作大多基于提供上下文信息的海量非结构化数据集。然而，企业产品与服务中最具价值的信息传统上往往以结构化数据形式记录。此类结构化数据集通常不遵循标准本体规范，未捕获实体间关系的显式映射，且缺乏上下文信息。为此，本研究提出一种填补该空白的数字工作流方法。该方法采用基于规则的技术，从遗留结构化数据（特别是规格说明书和产品目录）中提取并创建基于功能-行为-结构（FBS）本体的知识图谱。该解决方案包含两个核心组件：用于推导FBS本体概念的上下文及基于上下文的分类规则流程，以及用于填充与检索基于FBS本体的知识图谱的工作流。研究运用知识图谱与自然语言处理（NLP）技术实现知识提取、表示与检索的自动化。通过工业场景的试点实施验证了该工作流的有效性，并基于试点研究总结了相关挑战与机遇，包括对FBS本体及其概念的探讨。