Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. However, extracting information from textual rules to convert them to a machine-readable format has been a challenge due to the complexities associated with natural language and the limited resources that can support advanced machine-learning techniques. To address this challenge, we introduce CODE-ACCORD, a unique dataset compiled under the EU Horizon ACCORD project. CODE-ACCORD comprises 862 self-contained sentences extracted from the building regulations of England and Finland. Aligned with our core objective of facilitating information extraction from text for machine-readable rule generation, each sentence was annotated with entities and relations. Entities represent specific components such as "window" and "smoke detectors", while relations denote semantic associations between these entities, collectively capturing the conveyed ideas in natural language. We manually annotated all the sentences using a group of 12 annotators. Each sentence underwent annotations by multiple annotators and subsequently careful data curation to finalise annotations, ensuring their accuracy and reliability, thereby establishing the dataset as a solid ground truth. CODE-ACCORD offers a rich resource for diverse machine learning and natural language processing (NLP) related tasks in ACC, including text classification, entity recognition and relation extraction. To the best of our knowledge, this is the first entity and relation-annotated dataset in compliance checking, which is also publicly available.

翻译：在建筑、工程与施工（AEC）领域，自动合规性检查（ACC）需要自动化解读建筑法规才能充分发挥其潜力。然而，由于自然语言的复杂性以及缺乏支撑先进机器学习技术的资源，从文本规则中提取信息并将其转换为机器可读格式一直是一项挑战。为应对这一难题，我们推出了CODE-ACCORD——一个在欧盟地平线ACCORD项目框架下构建的独特数据集。该数据集包含从英国和芬兰建筑法规中提取的862个独立句子。为契合从文本中提取信息以生成机器可读规则的核心目标，我们对每个句子进行了实体和关系标注。实体代表具体构件（如"窗户""烟雾探测器"），关系则表示这些实体间的语义关联，共同捕捉自然语言中传达的信息。我们组织12名标注员对所有句子进行人工标注，每个句子经多人标注后通过严格的数据整理完成最终标注，确保其准确性和可靠性，从而将数据集确立为可靠的真值基准。CODE-ACCORD为ACC领域中的文本分类、实体识别和关系抽取等多样化机器学习与自然语言处理（NLP）任务提供了丰富资源。据我们所知，这是合规性检查领域首个公开可用的、带有实体和关系标注的数据集。