Engineering design knowledge is embodied in natural language text through
intricate placement of entities and relationships. Ontological constructs of
design knowledge often limit the performances of NLP techniques to extract
design knowledge. Also, large-language models could be less useful for
generating and explicating design knowledge, as these are trained predominantly
on common-sense text. In this article, we present the constituents of design
knowledge based on empirical observations from patent documents. We obtain a
sample of 33,881 patents and populate over 24 million facts from the sentences
in these. We conduct Zipf distribution analyses using the frequencies of unique
entities and relationships that are present in the facts thus populated. While
the literal entities cannot be generalised from the sample of patents, the
relationships largely capture attributes ('of'), structure ('in', 'with'),
purpose ('to', 'for'), hierarchy ('include'), exemplification ('such as'), and
behaviour ('to', 'from'). The analyses reveal that over half of entities and
relationships could be generalised to 64 and 24 linguistic syntaxes
respectively, while hierarchical relationships include 75 syntaxes. These
syntaxes represent the linguistic basis of engineering design knowledge. We
combine facts within each patent into a knowledge graph, from which we discover
motifs that are statistically over-represented subgraph patterns. Across all
patents in the sample, we identify eight patterns that could be simplified into
sequence [->...->], aggregation [->...<-], and hierarchy [<-...->] that form
the structural basis of engineering design knowledge. We propose regulatory
precepts for concretising abstract entities and relationships within subgraphs,
while also explicating hierarchical structures. These precepts could be useful
for better construction and management of knowledge in a design environment.

基于专利文件的经验观察，该研究揭示了工程设计知识的构成成分，包括基于句子的事实中存在的实体和关系，以及具有过度表达关系的统计上过表现的子图模式，为设计环境中更好地构建和管理知识提供了有用的指导原则。

工程设计知识的实证基础

Empirical Basis of Engineering Design Knowledge

Aiming to populate generalizable engineering design knowledge, we propose a
method to extract facts of the form head entity :: relationship :: tail entity
from sentences found in patent documents. These facts could be combined within
and across patent documents to form knowledge graphs that serve as schemes for
representing as well as storing design knowledge. Existing methods in
engineering design literature often utilise a set of predefined relationships
to populate triples that are statistical approximations rather than facts. In
our method, we train a tagger to identify both entities and relationships from
a sentence. Given a pair of entities thus identified, we train another tagger
to identify the relationship tokens that specifically denote the relationship
between the pair. For training these taggers, we manually construct a dataset
of 44,227 sentences and corresponding facts. We also compare the performance of
the method against typically recommended approaches, wherein, we predict the
edges among tokens by pairing the tokens independently and as part of a graph.
We apply our method to sentences found in patents related to fan systems and
build a domain knowledge base. Upon providing an overview of the knowledge
base, we search for solutions relevant to some key issues prevailing in fan
systems. We organize the responses into knowledge graphs and hold a comparative
discussion against the opinions from ChatGPT.

本文介绍一种从专利文件中提取事实的方法，使用这些事实填充知识图谱来表示和存储设计知识，其中训练了一个标记器来识别实体和关系并组合这些知识点进行比较，建立了一个与风扇系统相关的领域知识库。