Aimed at supporting knowledge-intensive tasks in the design process, populating design knowledge from text documents involves the extraction of triples - head entity :: relationship :: tail entity or h :: r :: t that could be combined into a knowledge graph representation. As relationships are largely chosen from ontological or common-sense alternatives, knowledge graphs built using these depict an approximation or restricted view of design knowledge, rather than what is explicated in text document. In this article, we present a data-driven approach to identify and explicate facts (h :: r :: t) from sentences in patent documents. We create a dataset of 44,227 sentences and facts, encompassing all patent classifications while also capturing the variations among patent document sections. Using this dataset, we train taggers that classify tokens to: 1) identify all entities (h) and relationships (r) and 2) specific relationships (r) for a pair of entities (h :: ___ :: t). While these taggers are built upon transformer-based sequence classification models, we evaluate our proposed method against edge classification approaches that use linear classifiers and graph neural networks, incorporating transformer-based token embeddings and linguistic features. The simplicity and coverage of the proposed method enable its application to patent documents at any scale and variety. Upon deploying an open-source python package, we apply our method to patent documents related to fan systems. From the knowledge graphs thus extracted, we explain how facts could be generalised to domain ontologies as well as be specified to subsystem levels. We also highlight the importance of knowledge graph representations by retrieving and explicating the knowledge of key issues in fan systems, while holding a comparative discussion against opinions from ChatGPT.

翻译：摘要：为支持设计过程中的知识密集型任务，从文本文档中构建设计知识涉及抽取三元组——头实体::关系::尾实体或h::r::t，这些三元组可组合成知识图谱表示。由于关系主要选自本体或常识选项，基于此构建的知识图谱所呈现的是设计知识的近似或受限视图，而非文本文档中明确阐述的内容。本文提出一种数据驱动方法，用于从专利文档的句子中识别并阐明事实（h::r::t）。我们构建了一个包含44,227个句子及其对应事实的数据集，涵盖所有专利分类，同时捕捉专利文档各部分间的差异。利用该数据集，我们训练了标记器，对标记进行分类以：1）识别所有实体（h）和关系（r）；2）为一对实体（h::___::t）识别特定关系（r）。这些标记器基于Transformer序列分类模型构建，我们将所提方法与使用线性分类器和图神经网络、并结合Transformer分词嵌入和语言特征的边分类方法进行了对比评估。所提方法的简洁性与覆盖度使其能够适用于任意规模和类型的专利文档。在部署开源Python包后，我们将方法应用于与风扇系统相关的专利文档。从提取的知识图谱中，我们阐释了事实如何可泛化为领域本体，也可具体化至子系统层级。通过检索并阐明风扇系统中关键问题的知识，并与ChatGPT的观点进行比较讨论，我们进一步强调了知识图谱表示的重要性。