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Developing a hybrid approach to extract constraints related information for constraint management
Automation in Construction ( IF 10.3 ) Pub Date : 2021-01-27 , DOI: 10.1016/j.autcon.2021.103563
Chengke Wu , Peng Wu , Jun Wang , Rui Jiang , Mengcheng Chen , Xiangyu Wang

Construction projects face various constraints (e.g., materials and equipment). Constraint management approaches such as advanced working packaging (AWP) can remove constraints and ensure smooth work. However, due to inefficient information extraction, the prerequisite of AWP, i.e., identifying and modelling constraints, are performed manually. Efforts that integrate constraint information into project knowledge bases are also limited. This paper proposes a hybrid approach to automatically extract and integrate constraint information from texts. The approach combines a deep learning model with pre-defined rules. The model extracts constraint entities whereas rules created based on domain knowledge are used to establish relations between these entities. Extracted information is encoded into the original ontologies. The approach can extract both entities and relations with over 90% accuracy. The original ontologies can be successfully enriched and support semantic queries. The approach improves AWP by partially automating constraint identification and modelling as well as ontology development for information integration.



中文翻译:

开发一种混合方法来提取约束相关信息以进行约束管理

建设项目面临各种限制(例如,材料和设备)。诸如高级工作包装(AWP)之类的约束管理方法可以消除约束并确保工作顺利进行。然而,由于信息提取效率低下,AWP的先决条件(即识别和建模约束)是手动执行的。将约束信息集成到项目知识库中的工作也受到限制。本文提出了一种从文本中自动提取和整合约束信息的混合方法。该方法将深度学习模型与预定义规则结合在一起。该模型提取约束实体,而基于领域知识创建的规则用于建立这些实体之间的关系。提取的信息被编码为原始本体。该方法可以以超过90%的精度提取实体和关系。原始本体可以成功丰富并支持语义查询。该方法通过部分自动化约束识别和建模以及用于信息集成的本体开发来提高AWP。

更新日期:2021-01-28
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