*Practical Engineering and Real-World Applications of Semiotic Framework*

The semiotic framework for assessing data quality is a theoretical framework that analyses data in terms of its essential components: syntactics, pragmatics, empirics, and semantics. This strategy has several real-world and practical engineering applications, such as data integration, business intelligence, data mining, data governance, and data visualisation. In the construction sector context, the semiotic framework of data quality assessment is used in evaluating building designs. Architects and engineers may use this framework to evaluate the accuracy and completeness of their building designs by analysing the signs and symbols used to represent the different design aspects. This may help them uncover design inconsistencies or errors and make the necessary adjustments before construction begins. In engineering applications of the construction industry, the semiotic framework of data quality may be used in several ways, including quality assurance, risk assessment, and compliance. Throughout the project lifecycle of a construction project, a substantial quantity of data must be gathered and evaluated for quality assurance purposes. The semiotic framework may be used to verify that the obtained data are correct and trustworthy, therefore guaranteeing that the project is on track and satisfies all objectives. The semiotic framework may be used to evaluate the risk associated with specific construction activities. Engineers can make informed decisions and reduce the likelihood of accidents or errors by evaluating the data quality used to evaluate risk. The construction industry is highly regulated, and businesses must adhere to various standards and regulations. The semiotic framework can ensure the accuracy and dependability of the data used to demonstrate compliance, thereby reducing the risk of fines.

In conclusion, the semiotic data quality framework has numerous practical engineering applications in the construction industry. Specific to highways, data quality dimensions are indispensable for planning and design, asset management, safety and emergency response, performance measurement, and policy and decision-making. By ensuring the quality of their data, transportation agencies can make more informed decisions, allocate resources more efficiently, and provide more effective transportation systems. Using this framework, architects, engineers, and other construction professionals can guarantee that the data they use is error-free, resulting in improved project outcomes and reduced risk.

**Author Contributions:** Conceptualisation, methodology, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, C.M.K.; writing—review, K.N.J. and K.R.; writing—editing, C.M.K.; supervision, K.N.J. and K.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical.

**Conflicts of Interest:** The authors declare no conflict of interest.
