To make sure that all significant contractual obligations are documented and managed, it is essential to have a clear understanding of construction contract agreements. However, the text data that makes up the content in these papers frequently necessitates the use of text mining. The topic modelling method of text mining, which is based on the document's topic, is one possible strategy to address these. The objective of this research is to demonstrate whether meaningful knowledge relationships can be extracted from a sample construction specific document using topic models (i.e. LDA). The research used a contract administration manual for topic modelling which is prepared by the Ethiopian Roads Authority (ERA) for use by the Regional Roads Authorities (RRAs). A total of 3217 unique tokens were available for text analysis. Between 5 and 25 topics were specified for LDA training and the one with 5 topics had concise result. To enhance the interpretability of the topics; topic visualization, relevance metric and filtered noun-types approaches were used. The tuning parameters in LDA Gensim with 5 topics gave the highest coherence score of 0.5163. Topic 1 made up the biggest portion of topics constituting 27% of the tokens. In addition, topics were made more interpretable by adjusting their setting. A total of 24300 bigrams and trigrams were also filtered with noun structures to form a unique concept. Construction companies benefit much from knowing what is under construction documents. An automated domain-specific model is required that can precisely extract all the explicit and implicit criteria from the construction contracts since construction-specific contracts differ from those used in other industries. In order to ensure that all pertinent project requirements are recorded, the research aims to demonstrate how knowledge linkages may be derived from construction-specific documents using topic models (i.e. LDA).
| Published in | International Journal of Engineering Management (Volume 6, Issue 2) |
| DOI | 10.11648/j.ijem.20220602.12 |
| Page(s) | 30-41 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
Artificial Intelligence, Knowledge Management, Topic Modelling, Latent Dirichlet Allocation (LDA), Natural Language Processing (NLP), Construction Document
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APA Style
Ezra Kassa. (2022). Artificial Intelligence in Knowledge Management: A Topic Modeling Approach for Construction Specific Documents. International Journal of Engineering Management, 6(2), 30-41. https://doi.org/10.11648/j.ijem.20220602.12
ACS Style
Ezra Kassa. Artificial Intelligence in Knowledge Management: A Topic Modeling Approach for Construction Specific Documents. Int. J. Eng. Manag. 2022, 6(2), 30-41. doi: 10.11648/j.ijem.20220602.12
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Download@article{10.11648/j.ijem.20220602.12,
author = {Ezra Kassa},
title = {Artificial Intelligence in Knowledge Management: A Topic Modeling Approach for Construction Specific Documents},
journal = {International Journal of Engineering Management},
volume = {6},
number = {2},
pages = {30-41},
doi = {10.11648/j.ijem.20220602.12},
url = {https://doi.org/10.11648/j.ijem.20220602.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijem.20220602.12},
abstract = {To make sure that all significant contractual obligations are documented and managed, it is essential to have a clear understanding of construction contract agreements. However, the text data that makes up the content in these papers frequently necessitates the use of text mining. The topic modelling method of text mining, which is based on the document's topic, is one possible strategy to address these. The objective of this research is to demonstrate whether meaningful knowledge relationships can be extracted from a sample construction specific document using topic models (i.e. LDA). The research used a contract administration manual for topic modelling which is prepared by the Ethiopian Roads Authority (ERA) for use by the Regional Roads Authorities (RRAs). A total of 3217 unique tokens were available for text analysis. Between 5 and 25 topics were specified for LDA training and the one with 5 topics had concise result. To enhance the interpretability of the topics; topic visualization, relevance metric and filtered noun-types approaches were used. The tuning parameters in LDA Gensim with 5 topics gave the highest coherence score of 0.5163. Topic 1 made up the biggest portion of topics constituting 27% of the tokens. In addition, topics were made more interpretable by adjusting their setting. A total of 24300 bigrams and trigrams were also filtered with noun structures to form a unique concept. Construction companies benefit much from knowing what is under construction documents. An automated domain-specific model is required that can precisely extract all the explicit and implicit criteria from the construction contracts since construction-specific contracts differ from those used in other industries. In order to ensure that all pertinent project requirements are recorded, the research aims to demonstrate how knowledge linkages may be derived from construction-specific documents using topic models (i.e. LDA).},
year = {2022}
}
TY - JOUR T1 - Artificial Intelligence in Knowledge Management: A Topic Modeling Approach for Construction Specific Documents AU - Ezra Kassa Y1 - 2022/09/14 PY - 2022 N1 - https://doi.org/10.11648/j.ijem.20220602.12 DO - 10.11648/j.ijem.20220602.12 T2 - International Journal of Engineering Management JF - International Journal of Engineering Management JO - International Journal of Engineering Management SP - 30 EP - 41 PB - Science Publishing Group SN - 2640-1568 UR - https://doi.org/10.11648/j.ijem.20220602.12 AB - To make sure that all significant contractual obligations are documented and managed, it is essential to have a clear understanding of construction contract agreements. However, the text data that makes up the content in these papers frequently necessitates the use of text mining. The topic modelling method of text mining, which is based on the document's topic, is one possible strategy to address these. The objective of this research is to demonstrate whether meaningful knowledge relationships can be extracted from a sample construction specific document using topic models (i.e. LDA). The research used a contract administration manual for topic modelling which is prepared by the Ethiopian Roads Authority (ERA) for use by the Regional Roads Authorities (RRAs). A total of 3217 unique tokens were available for text analysis. Between 5 and 25 topics were specified for LDA training and the one with 5 topics had concise result. To enhance the interpretability of the topics; topic visualization, relevance metric and filtered noun-types approaches were used. The tuning parameters in LDA Gensim with 5 topics gave the highest coherence score of 0.5163. Topic 1 made up the biggest portion of topics constituting 27% of the tokens. In addition, topics were made more interpretable by adjusting their setting. A total of 24300 bigrams and trigrams were also filtered with noun structures to form a unique concept. Construction companies benefit much from knowing what is under construction documents. An automated domain-specific model is required that can precisely extract all the explicit and implicit criteria from the construction contracts since construction-specific contracts differ from those used in other industries. In order to ensure that all pertinent project requirements are recorded, the research aims to demonstrate how knowledge linkages may be derived from construction-specific documents using topic models (i.e. LDA). VL - 6 IS - 2 ER -
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