Rule-based information extraction for mechanical-electrical-plumbing-specific semantic web

Wu, Lang-Tao; Lin, Jianhui; Leng, Shuo; Li, Jiu-Lin; Hu, Zhen-Zhong

doi:10.1016/j.autcon.2021.104108

Cited by 36 publications

(14 citation statements)

References 17 publications

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“…Pan et al (2021) proposed a new framework called "video2entities" that extracts entities from videos to update AEC knowledge graphs, which can be deemed as the data layer of the domain ontology. Wu et al (2022) propose a rule-based information extraction approach for the mechanical, electrical, and plumbing (MEP) semantic web. Named entities can be recognized in the NL texts, and relationships can be extracted using a dependency-path-based matching algorithm.…”

Section: Fundamentals Of Ontology Learningmentioning

confidence: 99%

“…Wu et al. (2022) propose a rule‐based information extraction approach for the mechanical, electrical, and plumbing (MEP) semantic web. Named entities can be recognized in the NL texts, and relationships can be extracted using a dependency‐path‐based matching algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, automatic knowledge acquisition is a complex task that combines knowledge representation, logic, philosophy, databases, machine learning, and NLP (Petasis et al., 2011). The current studies on automatic ontology construction and enrichment in the AEC domain deal with tasks in specific subdomains (e.g., utility infrastructure—Xu & Cai, 2021, and MEP—Wu et al., 2022). However, they do not have sufficient generality for BIM ontological modeling.…”

Section: Introductionmentioning

confidence: 99%

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A deep natural language processing‐based method for ontology learning of project‐specific properties from building information models

Yin

Tang

Webster

et al. 2023

Computer aided Civil Eng

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Element property is a crucial aspect of building information modeling (BIM) for almost all BIM‐based engineering tasks. Since there are limited properties predefined in Industry Foundation Classes (IFC) specifications, a vast number of property concepts were customized and stored in BIM models, which lack labor‐intensive data modeling and alignment for effective information management and reuse. To tackle the challenge, this study presents a natural language understanding (NLU)‐based method for the automatic ontological knowledge modeling of project‐specific property concepts from BIM models. A soft pattern matching model was used to acquire contextual definitions of concepts from a domain corpus before applying deep NLU models to transform the concept names and definitions into dense vector representations. These outputs were then fed into two stacking ensemble learning models to carry out two tasks: (a) classifying whether an unseen concept overlaps with the IFC ontology, and (b) aligning the repetitive concepts with the most relevant concepts in the ontology. Finally, all fresh properties were appended to an IFC ontology, either as new objects or new synonyms. The performance was evaluated based on 327 property concepts from real‐life BIM models. The results show that the proposed approach incorporating reading comprehension of definitions outperforms the existing name similarity‐based methods. Finally, a case study on a renovation project demonstrates the effectiveness of this study in automatic ontology modeling of property concepts.

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Section: Fundamentals Of Ontology Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A deep natural language processing‐based method for ontology learning of project‐specific properties from building information models

Yin

Tang

Webster

et al. 2023

Computer aided Civil Eng

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“…This usually involves the use of regular expressions and/or grammar parsing (e.g. Xiao et al, 2013;Torii et al, 2015;Wu et al, 2022). The machinelearning-based approach considers the information extraction task as a classification problem, that is, to classify whether a token belongs to the category of interest or not.…”

Section: Related Workmentioning

confidence: 99%

Band gap information extraction from materials science literature – a pilot study

Ghosh

Lü

2022

AJIM

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PurposeThe purpose of this paper is to present a preliminary work on extracting band gap information of materials from academic papers. With increasing demand for renewable energy, band gap information will help material scientists design and implement novel photovoltaic (PV) cells.Design/methodology/approachThe authors collected 1.44 million titles and abstracts of scholarly articles related to materials science, and then filtered the collection to 11,939 articles that potentially contain relevant information about materials and their band gap values. ChemDataExtractor was extended to extract information about PV materials and their band gap information. Evaluation was performed on randomly sampled information records of 415 papers.FindingsThe findings of this study show that the current system is able to correctly extract information for 51.32% articles, with partially correct extraction for 36.62% articles and incorrect for 12.04%. The authors have also identified the errors belonging to three main categories pertaining to chemical entity identification, band gap information and interdependency resolution. Future work will focus on addressing these errors to improve the performance of the system.Originality/valueThe authors did not find any literature to date on band gap information extraction from academic text using automated methods. This work is unique and original. Band gap information is of importance to materials scientists in applications such as solar cells, light emitting diodes and laser diodes.

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“…The lack of communication between designers may lead to cross-collisions between pipelines in dense areas of MEP components [1]. Additionally, compared to other common domains, MEP codes are not only numerous and varied but also have a more complex information structure and language representation [2]. Therefore, it is expensive to perform rule checking manually.…”

Section: Introductionmentioning

confidence: 99%

Automated Rule Checking for MEP Systems Based on BIM and KBMS

Xie

Zhou

et al. 2022

Buildings

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Due to the growing complexity of mechanical, electrical, and plumbing (MEP) designs and the rules that govern them, performing rule checks manually has become expensive. However, MEP-based rule checking has not received adequate attention compared to automated rule checking in other domains. Based on Knowledge Management and Building Information Modeling (BIM), an automated rule checking system integrated knowledge base management system (KBMS) for model information expansion, information extraction, system integrity checking, and element spacing checking was developed. MEP rules for automated rule checking were collected, optimized, and stored in the MEP knowledge base. The KBMS facilitates the management of MEP rules in the knowledge base. A Revit plug-in of MEP rule checking system was developed including functions of KBMS, Model Integrity Checking, Elements Space Checking, and Locating the non-compliant element in model view. This study integrated both KBMS and BIM technologies to achieve automated rule checking for MEP. This simplifies the process of rule checking of MEP systems in an automated manner.

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Rule-based information extraction for mechanical-electrical-plumbing-specific semantic web

Cited by 36 publications

References 17 publications

A deep natural language processing‐based method for ontology learning of project‐specific properties from building information models

A deep natural language processing‐based method for ontology learning of project‐specific properties from building information models

Band gap information extraction from materials science literature – a pilot study

Automated Rule Checking for MEP Systems Based on BIM and KBMS

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