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), 2024. Published by Science Publishing Group |
Artificial Intelligence, Knowledge Management, Topic Modelling, Latent Dirichlet Allocation (LDA), Natural Language Processing (NLP), Construction Document
| [1] | E. Arafa, “THE IMPACT OF KNOWLEDGE MANAGEMENT ON PROJECT SUCCESS,” UNIVERSITY OF PORTSMOUTH, 2015. |
| [2] | M. Lindvall, I. Rus, and S. S. Sinha, “Technology Support for Knowledge Management,” 2002. https://www.researchgate.net/publication/2535021_Technology_Support_for_Knowledge_Management (accessed May 30, 2022). |
| [3] | S. J. Choi, S. W. Choi, J. H. Kim, and E. B. Lee, “AI and Text-Mining Applications for Analyzing Contractor’s Risk in Invitation to Bid (ITB) and Contracts for Engineering Procurement and Construction (EPC) Projects,” Energies, vol. 14, no. 15, p. 4632, Jul. 2021, doi: 10.3390/EN14154632. |
| [4] | A. K. Jallow, P. Demian, A. N. Baldwin, and C. Anumba, “An empirical study of the complexity of requirements management in construction projects,” Eng. Constr. Archit. Manag., vol. 21, no. 5, pp. 505–531, Sep. 2014, doi: 10.1108/ECAM-09-2013-0084. |
| [5] | N. Bing Chong, L. Uden, and M. Naaranoja, “Knowledge management system for construction projects in Finland,” Int. J. Knowl. Manag. Stud., vol. 1, no. 3–4, pp. 240–260, 2007, doi: 10.1504/IJKMS.2007.012524. |
| [6] | I. Ö. Arnarsson, O. Frost, E. Gustavsson, M. Jirstrand, and J. Malmqvist, “Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents:,” SAGE Journals, vol. 29, no. 2, pp. 142–152, Mar. 2021, doi: 10.1177/1063293X20982973. |
| [7] | Q. Tang, “Knowledge management using machine learning, natural language processing and ontology,” Cardiff University, 2006. |
| [8] | Z. Zhou, Y. Liu, H. Yu, and L. Ren, “The influence of machine learning-based knowledge management model on enterprise organizational capability innovation and industrial development,” PLoS One, vol. 15, no. 12 December, Dec. 2020, doi: 10.1371/JOURNAL.PONE.0242253. |
| [9] | M. H. Jarrahi, D. Askay, A. Eshraghi, and P. Smith, “Artificial intelligence and knowledge management: A partnership between human and AI,” Bus. Horiz., Mar. 2022, doi: 10.1016/J.BUSHOR.2022.03.002. |
| [10] | A. K. Agrawal, M. Jagannathan, and V. S. K. Delhi, “Control Focus in Standard Forms: An Assessment through Text Mining and NLP,” J. Leg. Aff. Disput. Resolut. Eng. Constr., vol. 13, no. 1, p. 04520040, Oct. 2020, doi: 10.1061/(ASCE)LA.1943-4170.0000441. |
| [11] | K. M. J Harmon, “Resolution Of Construction Disputes: A Review of Current Methodologies,” Leadersh. Manag. Eng., vol. 3, no. 4, pp. 187–201, 2003. |
| [12] | N. Sajadfar, S. Abdollahnejad, U. Hermann, and Y. Mohamed, “Text detection and classification of construction documents,” in 36th International Symposium on Automation and Robotics in Construction, 2019, pp. 446–452. |
| [13] | Ray Jackendoff, Why can’t computers use English? Linguistic Society of America, 2022. |
| [14] | B. Inmon, “Why Do We Call Text ‘Unstructured’?,” May 28, 2016. https://tdwi.org/articles/2016/06/28/text-unstructured.aspx (accessed May 30, 2022). |
| [15] | R. Alghamdi and K. Alfalqi, “A Survey of Topic Modeling in Text Mining,” Int. J. Adv. Comput. Sci. Appl., vol. 6, no. 1, 2015, doi: 10.14569/IJACSA.2015.060121. |
| [16] | P. Xie and E. P. Xing, “Integrating Document Clustering and Topic Modeling,” in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013, pp. 694–703. |
| [17] | H. K. Mohajan, “(2) (PDF) The Roles of Knowledge Management for the Development of Organizations,” J. Sci. Achiev., vol. 2, no. 2, pp. 1–27, 2017, Accessed: May 31, 2022. [Online]. Available: https://www.researchgate.net/publication/314063315_The_Roles_of_Knowledge_Management_for_the_Development_of_Organizations. |
| [18] | P. V Krishna and M. R. Babu, “The Role of ICTs in Knowledge Management (KM) for Organizational Effectiveness,” CCIS, vol. II, pp. 542–549, 2011. |
| [19] | T. Maqsood, A. D. Finegan, and D. H. T. Walker, “Biases and Heuristics in Judgment and Decision Making: The Dark Side of Tacit Knowledge,” Issues Informing Sci. Inf. Technol., pp. 224–301, 2004. |
| [20] | R. M. Grant, “The Development of Knowledge Management in the Oil The Development of Knowledge management in the oil and Gas Industry,” Netw. Sci. Journals from Lat. Am., pp. 92–125, 2013, Accessed: May 31, 2022. [Online]. Available: http://www.redalyc.org/articulo.oa?id=43328679006. |
| [21] | S. Gupta, “Organizational Barriers to Digital Transformation,” KTH ROYAL INSTITUTE OF TECHNOLOGY, STOCKHOLM, SWEDEN, 2018. |
| [22] | A. A. Kornienko, A. V. Kornienko, O. B. Fofanov, and M. P. Chubik, “Knowledge in artificial intelligence systems: searching the strategies for application,” in International Conference on Research Paradigms Transformation in Social Sciences 2014, 2015, pp. 589–594, doi: 10.1016/j.sbspro.2014.12.578. |
| [23] | G. Sucharitha, A. Matta, K. Dwarakamai, and B. Tannmayee, “Theory and Implications of Information Processing,” in Emotion and Information Processing, A Practical approach, Springer International Publishing, 2020, pp. 39–54. |
| [24] | C. Deng, X. Ji, C. Rainey, J. Zhang, and W. Lu, “Integrating Machine Learning with Human Knowledge,” iScience, no. 23, pp. 1–27, 2020, doi: 10.1016/j.isci. |
| [25] | M. Chugh, N. Chugh, D. K. Punia, and A. Agarwal, “THE ROLE OF INFORMATION TECHNOLOGY IN KNOWLEDGE MANAGEMENT MitaliChugh,*,” in Conference on Advances in Communication and Control Systems (CAC2S 2013), 2013, vol. 2013, no. Cac2s, pp. 688–693. |
| [26] | V. Rus, P. M. McCarthy, D. S. McNamara, and A. C. Graesser, “Natural Language Understanding and Assessment,” in Encyclopedia of Artificial Intelligence, IGI Global, 2011. |
| [27] | G. Frisoni, G. Moro, and A. Carbonaro, “Unsupervised descriptive text mining for knowledge graph learning,” IC3K 2020 - Proc. 12th Int. Jt. Conf. Knowl. Discov. Knowl. Eng. Knowl. Manag., vol. 1, pp. 316–324, 2020, doi: 10.5220/0010153603160324. |
| [28] | B. Manaris, “Natural Language Processing: A Human-Computer Interaction Perspective,” Adv. Comput., vol. 47, no. C, pp. 1–66, 1998, doi: 10.1016/S0065-2458(08)60665-8. |
| [29] | M. Martinc, J. Stefan, and M. Robnik-ˇ Sikonja, “Supervised and Unsupervised Neural Approaches to Text Readability,” Assoc. Comput. Linguist., vol. 47, no. 1, 2021, doi: 10.1162/COLI. |
| [30] | M. Jagannathan, D. Roy, V. Santosh, and K. Delhi, “Application of NLP-based topic modeling to analyse unstructured text data in annual reports of construction contracting companies,” CSI Trans. ICT 2022, pp. 1–10, May 2022, doi: 10.1007/S40012-022-00355-W. |
| [31] | R. Albalawi, T. H. Yeap, and M. Benyoucef, “Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis,” Front. Artif. Intell., vol. 3, p. 42, Jul. 2020, doi: 10.3389/FRAI.2020.00042/BIBTEX. |
| [32] | Tony Yiu, “Understanding NLP and Topic Modeling Part 1 - KDnuggets,” KD Nuggets, 2019. https://www.kdnuggets.com/2019/11/understanding-nlp-topic-modeling-part-1.html (accessed May 31, 2022). |
| [33] | Skim AI, “Topic Modeling for Product Managers - A Beginner’s Guide,” 2016. https://skimai.com/topic-modeling-for-product-managers/ (accessed May 31, 2022). |
| [34] | R. M. Snyder and R. Com, “An Introduction to Topic Modeling as an Unsupervised Machine Learning Way to Organize Text Information,” in ASCUE Proceedings, 2015, vol. 86, Accessed: May 31, 2022. [Online]. Available: http://www.robinsnyder.com. |
| [35] | L. Liu, L. Tang, W. Dong, S. Yao, and W. Zhou, “An overview of topic modeling and its current applications in bioinformatics,” Springerplus, vol. 5, no. 1, pp. 1–22, Dec. 2016, doi: 10.1186/S40064-016-3252-8/TABLES/4. |
| [36] | D. M. Blei, A. Y. Ng, and J. B. Edu, “Latent Dirichlet Allocation Michael I. Jordan,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003. |
| [37] | H. Jelodar et al., “Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey,” 2017, Accessed: May 31, 2022. [Online]. Available: https://www.researchgate.net/publication/321069759. |
| [38] | I. R. Putri and R. Kusumaningrum, “Latent Dirichlet Allocation (LDA) for Sentiment Analysis Toward Tourism Review in Indonesia,” J. Phys. Conf. Ser., vol. 801, no. 1, Mar. 2017, doi: 10.1088/1742-6596/801/1/012073. |
| [39] | R. Das, M. Zaheer, and C. Dyer, “Gaussian LDA for Topic Models with Word Embeddings,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Jul. 2015, pp. 795–804, Accessed: Jun. 01, 2022. [Online]. Available: https://code.google.com/p/word2vec/. |
| [40] | A. Daud, J. Li, L. Zhou, and F. Muhammad, “Knowledge discovery through directed probabilistic topic models: A survey,” Front. Comput. Sci. China, vol. 4, no. 2, pp. 280–301, 2010, doi: 10.1007/S11704-009-0062-Y. |
| [41] | I. Saad, “Construction Contracts: From Zero-Sum to Win-Win,” University of Cincinnati, 2020. https://www.researchgate.net/publication/343107480_Construction_Contracts (accessed Jun. 01, 2022). |
| [42] | H. Jelodar et al., “Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey,” 2018, Accessed: Jun. 01, 2022. [Online]. Available: https://www.researchgate.net/publication/321069759. |
| [43] | K. Koc and A. P. Gurgun, “Ambiguity factors in construction contracts entailing conflicts,” Eng. Constr. Archit. Manag., 2021, doi: 10.1108/ECAM-04-2020-0254. |
| [44] | P. Jafari, M. Al Hattab, E. Mohamed, and S. Abourizk, “Automated extraction and time-cost prediction of contractual reporting requirements in construction using natural language processing and simulation,” Appl. Sci., vol. 11, no. 13, Jul. 2021, doi: 10.3390/app11136188. |
| [45] | V. Ivanov, A. Sadovykh, A. Naumchev, A. Bagnato, and K. Yakovlev, “Extracting Software Requirements from Unstructured Documents,” Feb. 2022, Accessed: Jun. 01, 2022. [Online]. Available: http://arxiv.org/abs/2202.02135. |
| [46] | F. ul Hassan and T. Le, “Automated Requirements Identification from Construction Contract Documents Using Natural Language Processing,” J. Leg. Aff. Disput. Resolut. Eng. Constr., vol. 12, no. 2, p. 09, May 2020, doi: 10.1061/(ASCE)LA.1943-4170.0000379. |
| [47] | T. Aksoy, S. Celik, and S. Gulsecen, “DATA PRE-PROCESSING IN TEXT MINING,” in Who Runs the World, Istanbul University Press, 2020, p. 125. |
| [48] | J. Daniel and J. H. Martin, “N-gram Language Models,” in Speech and Language Processing., 2021. |
| [49] | J. Brownlee, “A Gentle Introduction to the Bag-of-Words Model,” Deep Learning for Natural Language Processing, 2019. https://machinelearningmastery.com/gentle-introduction-bag-words-model/ (accessed Jun. 01, 2022). |
| [50] | S. Qaiser and R. Ali, “Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents,” Int. J. Comput. Appl., vol. 181, no. 1, pp. 25–29, Jul. 2018, doi: 10.5120/IJCA2018917395. |
| [51] | R. Řehůřek, “Corpora and Vector Spaces — gensim,” May 06, 2022. https://radimrehurek.com/gensim/auto_examples/core/run_corpora_and_vector_spaces.html (accessed Jun. 01, 2022). |
| [52] | J. Wira, G. Putra, and T. Tokunaga, “Evaluating text coherence based on semantic similarity graph,” in the Workshop on Graph-based Methods for Natural Language Processing, Aug. 2017, pp. 76–85. |
| [53] | E. Zvornicanin, “When Coherence Score is Good or Bad in Topic Modeling? | Baeldung on Computer Science,” Dec. 07, 2021. https://www.baeldung.com/cs/topic-modeling-coherence-score (accessed Jun. 01, 2022). |
| [54] | S. Kleinman and L. Thomas, “WE1S ‘pyldavis’ module,” WE1S Tools and software, Sep. 18, 2020. https://we1s.ucsb.edu/wp-content/uploads/S-22.pdf (accessed Jun. 01, 2022). |
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
Share This Article
Twitter
Linked In
Facebook
Copy
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 -
Email: