Semantic information alignment of BIMs to computer-interpretable regulations using ontologies and deep learning

Peng, Zhou; El-Gohary, Nora

doi:10.1016/j.aei.2020.101239

Cited by 25 publications

(14 citation statements)

References 52 publications

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“…In order to evaluate our proposed methods, for the experimental setup we collected education dataset from protégé ontology library (http://protegewiki.stanford.edu/wiki/Protege_Ontology_Library) (http://www.danmccreary.com/presentat ions/ semweb/) (www.annuniv.edu) and QALLME datasets. This domain includes information about colleges, university, K-12 students, teachers, schools, districts, enrollments, assessments, food and nutrition programs and on-line courses includes data elements and to compare the performance of four algorithm, we used four performance indicators from the information retrieval system, namely precision, mean average precision, recall and f-measure (6) are adopted in the experiment, a proper threshold values need to be decided to filter the irrelevant concepts for metadata. Next we discuss about four performance indicators as following; Precision is used to measure the preciseness of a search system.…”

Section: Expérimental Results and Discussionmentioning

confidence: 99%

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Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Palaniammal¹

2021

IJST

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Objectives: To refine search performance using semantic web with an improved algorithm to retrieve the information efficiently. Methods: In order to establish the SCBR model and improve the performance of Web search, this paper adopts the Natural Language Processing (NLP) technology and the Quality of Service (QoS) ranking method, and endeavors to develop a relevant reliable and efficient search engine. Findings: Mean average precision tests revealed for quickness and precious of search results, and achieves the values from 82.98% to 99.53%. The experimental results show that the NLP technique improves the performance of SCBR model, and achieves higher average precision and recall values. Novelty: This research focuses to develop a related reliable and an efficient search engine to retrieve the accurate results for the user's complex query. It even bears the human error in typing, and suggests the expected word to search for. It also aims to retrieving the same result for synonym words which prevent the appearance of irrelevant search results.

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Section: Expérimental Results and Discussionmentioning

confidence: 99%

“…In order to extract information. They need heavy computational processes (5,6) . There are some alternative information extraction methods such as pattern / rule-based information extractors against heavy computational costs.…”

Section: Introductionmentioning

confidence: 99%

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Palaniammal¹

2021

IJST

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“…Ontology mapping (Annane et Gao et al, 2018;Fanizzi et al, 2011;Djellali, 2013;Chakraborty et al, 2021;Rico et al, 2018;Rubiolo et al, 2012;Shannon et al, 2021;Zhou and El-Gohary, 2021;Mao et al, 2010;Mohan et al, 2021;Xue et al, 2021) (also called ontology alignment) whose goal is to find links between similar vocabulary terms in two different ontologies while ensuring that the overall structure of the ontology is respected (Kalfoglou and Schorlemmer, 2003). Thus, by bringing together two ontologies that are initially distinct from each other, we can cover a larger terminology and ensure interoperability.…”

Section: Ontology Mappingmentioning

confidence: 99%

“…Machine learning algorithms used for ontology mapping are presented in detail in table 7. In this category, we notice that the most used algorithms are neural networks (Chakraborty et al, 2021;Mao et al, 2010;Rubiolo et al, 2012;Shannon et al, 2021;Gao et al, 2018;Djellali, 2013;Zhou and El-Gohary, 2021;Mohan et al, 2021;Xue et al, 2021). Ensemble methods are frequently used, like Random Forest (bagging) (Mihindukulasooriya et al, 2018;Mitchell et al, 2018;Rico et al, 2018;Annane et al, 2018) or a combination of three classifiers (Fanizzi et al, 2011).…”

Section: Ontology Mappingmentioning

confidence: 99%

Mixed artificial reasoning, closer to human?

Ghidalia

Narsis

Bertaux

et al. 2022

Preprint

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AI is now used in many areas of our daily life, however we still have difficulties to make machines interact with our world as we perceive it. Humans mainly use two types of reasoning to grasp reality: inductive and deductive reasoning. In AI, inductive reasoning is possible thanks to the use of machine learning models, while deductive reasoning can be provided by the use of ontologies. Based on these statements, we have conducted a review of articles combining the use of machine learning and ontologies in order to identify the most recent techniques that combine induction and deduction within an AI. This systematic literature review allowed us, after reading 128 studies, to identify three main groups of hybridization between machine learning and ontologies: learning-enhanced ontologies, semantic data mining and learning and reasoning system. The combination of these two types of reasoning allows machines to be better informed about the specificity of our world.

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“…These studies focus on the "latent semantic structure" categorized by Wang et al [15]. The connection between BIM and regulation focuses on mapping the attributes extracted from each model (BIM and document), rather than document fragments or elements from the ACC perspective [16][17][18]. This results in relatively high costs for extracting the required information, as well as inefficiency if the documents are used as non-geometric reference information instead of a regulatory review.…”

Section: Introductionmentioning

confidence: 99%

Development of Open-Assistant Environment for Integrated Operation of 3D Bridge Model and Engineering Document Information

Park

Kim²,

Goh

et al. 2022

Applied Sciences

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This study proposes a method for assistant environments to integrate 3D bridge model information and engineering document fragments. The engineering document content varies depending on the process. Therefore, we accept a loose coupling concept to support the independence of each information set instead of using a specific data model for effective integration. The engineering document is translated into an Extensible Markup Language (XML)-based structured format based on the explicit and apparent semantic structure of the document. An extended industry foundation classes (IFC) schema is proposed to manage the bridge information model, as well as document fragments. An information document (iMapDoc) is proposed to manage interim data to connect a 3D digital model, an IFC model, and engineering document fragments. Document fragments on a specific component in the 3D bridge model are retrieved to validate the developed integrated assistant module.

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Semantic information alignment of BIMs to computer-interpretable regulations using ontologies and deep learning

Cited by 25 publications

References 52 publications

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Mixed artificial reasoning, closer to human?

Development of Open-Assistant Environment for Integrated Operation of 3D Bridge Model and Engineering Document Information

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