A Natural‐Language‐Based Approach to Intelligent Data Retrieval and Representation for Cloud BIM

Lin, Jia-Rui; Hu, Zhen-Zhong; Zhang, Jianping; Yu, Fangqiang

doi:10.1111/mice.12151

Cited by 120 publications

(45 citation statements)

References 33 publications

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“…In construction literature, POS Tagging has been used as part of NLP pipelines for information extraction for compliance checking (Zhang & El-Gohary, 2014, 2015a, Zhou & El-Gohary, 2017, Li et al, 2016, contract requirements extraction (Zhou & El-Gohary, 2016), IFC schema extension , and information retrieval from Building Information Models (BIM) (Zhang & El-Gohary, 2015b, Lin et al, 2016, Oraee et al, 2017. However, to the best of the authors' knowledge, using approaches inspired by POS tagging to decipher construction activities was never attempted before.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Understanding of Construction Schedules: Part-of-Activity Tagging

Amer¹,

Golparvar-Fard²

2019

Proceedings of the 2019 European Conference on Computing in Construction

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Nowadays, construction planning practices, whether conducted by human planners or artificial intelligence (AI) systems, rely heavily on manually elaborated descriptions of construction means and methods. As part of envisioning a new planning system that automatically learns construction knowledge from previous projects' schedules, this paper introduces Part-of-Activity (POA) Tagging: a construction-specific word-category disambiguation method for decoding the constructional functionalities encoded in activity names. These functionalities represent the roles each token, i.e. word, in an activity name plays in understanding that activity from a construction point of view. The model is built using Bidirectional Long Short Term Memory Recurrent Neural Networks (BI-LSTM RNN). After training on a manually annotated dataset of more than 7000 activities, the model achieved a token accuracy of ~92%. The significance of this method lies in its ability to allow an AI System to decipher construction schedules. This schedule understanding opens the door for further applications such as the automated elaboration of weekly work plans and alignment of master schedules to weekly work plans.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Understanding of Construction Schedules: Part-of-Activity Tagging

Amer¹,

Golparvar-Fard²

2019

Proceedings of the 2019 European Conference on Computing in Construction

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“…However, as the dimensions and consequently the information and data integrated into the BIMs are continuing to increase, the need for automated processing and extraction of desirable and useful information from a massive BIM has become critical for both expert and nonexpert users of the BIM software [19]. Lin et al [19] proposed a Natural Language Processing (NLP) based approach to intelligent data retrieval and representation for Cloud BIM [12]. In the proposed framework, the user inputs (keyword queries) in natural language are extracted and mapped to IFC entities or attributes through the International Framework for Dictionaries (IFD) for retrieving results from an IFC-structured BIM data model and visualized as per user expectations [19].…”

Section: Big Data and Bims: A Surveymentioning

confidence: 99%

“…Lin et al [19] proposed a Natural Language Processing (NLP) based approach to intelligent data retrieval and representation for Cloud BIM [12]. In the proposed framework, the user inputs (keyword queries) in natural language are extracted and mapped to IFC entities or attributes through the International Framework for Dictionaries (IFD) for retrieving results from an IFC-structured BIM data model and visualized as per user expectations [19].…”

Section: Big Data and Bims: A Surveymentioning

confidence: 99%

“…Two kinds of methods have been commonly adopted by the AEC industry for interoperability and collaboration based on BIM, namely domain ontologies and semantic web based methods and methods based on building SMART's Industry Foundation Classes (IFC) [19]. However, as the dimensions and consequently the information and data integrated into the BIMs are continuing to increase, the need for automated processing and extraction of desirable and useful information from a massive BIM has become critical for both expert and nonexpert users of the BIM software [19]. Lin et al [19] proposed a Natural Language Processing (NLP) based approach to intelligent data retrieval and representation for Cloud BIM [12].…”

Section: Big Data and Bims: A Surveymentioning

confidence: 99%

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Big Data in Building Information Modeling Research: Survey and Exploratory Text Mining

Gopalakrishnan¹

2017

MOJCE

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It has been argued that Building Information Modeling (BIM) can transform the landscape of Architecture, Engineering, and Construction (AEC) and Facility Management (FM) industries with its potential to reduce cost, project delivery time and increase productivity. Going beyond traditional Computer Aided Design (CAD), BIM has emerged as a data-rich, object-oriented, shared digital representation of a facility that can serve as a reliable basis for decision making across the entire life cycle of a construction project. Simultaneous advancements in big data analytics, storage, as well as information visualization seem to hold the potential to enable truly n-dimensional BIMs integrated with other dataintensive sources such as Geographic Information Systems (GIS), Building Automation Systems (BAS), Energy Management Systems (EMS), etc. A number of recently published articles in the literature seem to indicate that the AEC industry can significantly benefit from big data analytics and architecture and make data-driven decisions considering the volume and variety of information resulting from BIM integration approaches. This paper presents a concise survey of recently published articles highlighting the big data based BIM challenges as well as some exploratory text analysis using bag-of-words text mining, a Natural Language Processing (NLP) technique.

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“…In addition, since ZESeM relies on Wordnet which is a generic lexicon, the applicability would be limited. Lin et al (2015) [20] developed an IFD based framework for BIM information retrieval. IFD Library (International Framework for Dictionaries library), which is developed and maintained by the international buildingSMART, is a dictionary of BIM data terminology that assigns the same ID to synonyms.…”

Section: Semantic Data Label Matchingmentioning

confidence: 99%

Technical Term Similarity Model for Natural Language Based Data Retrieval in Civil Infrastructure Projects

Le¹,

Jeong²

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Recent advances in data and information technologies have enabled extensive digital datasets to be available to decision makers during the life cycle of a civil infrastructure project. However, much of the data is not yet fully reused due to the challenging and time consuming process of extracting the desired data for a specific purpose. Digital datasets are presented only in computer-readable formats and they are mostly complicated. In order to accurately extract a required subset of data, end users need to have deep understanding of the structure of the data schema, the meaning of each data entity and a query language. Thus, to truly facilitate the reuse of digital project data, a computational platform is needed to allow users to present their data needs in natural language. One of the critical requirements for a computer to perform this task is the ability to understand and interpret users' natural language inputs where keywords are a basic linguistic component. This research aims to collect technical terms commonly used in the civil infrastructure domain and develop a semantic similarity model that can measure the meaning relatedness/similarity between terms. Natural Language Processing (NLP) techniques and C-value method are used to automatically extract terms from text documents. A machine learning model called Skip-gram model is then employed to learn the semantic relatedness between technical terms using an unlabeled highway corpora as the input data. The input corpus includes 10 million words mainly collected from roadway design guidelines across the U.S. The model is evaluated by comparing the mapping results performed by a computer and a human.

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A Natural‐Language‐Based Approach to Intelligent Data Retrieval and Representation for Cloud BIM

Abstract: Abstract:As the information from diverse disciplines continues to integrate during the whole life cycle of an Architecture, Engineering, and Construction (AEC)

Cited by 120 publications

References 33 publications

Automatic Understanding of Construction Schedules: Part-of-Activity Tagging

Automatic Understanding of Construction Schedules: Part-of-Activity Tagging

Big Data in Building Information Modeling Research: Survey and Exploratory Text Mining

Technical Term Similarity Model for Natural Language Based Data Retrieval in Civil Infrastructure Projects

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