At the end of the design phase, information models (IM) should incorporate information from other domains such as cost and scheduling in addition to geometric and technical data. These data are currently managed through separate and unstructured documents, lacking explicit relations to the digital model. A structured integration would help to reduce information fragmentation and enable a more complete and consistent representation of the project, facilitating cross-domain data access and querying by all stakeholders. This issue becomes even more critical during the construction phase, where a significant gap emerges in the semantic integration between design data, progress monitoring data, and their operational consultation. This misalignment compromises the interpretability of the IM and contributes to the growing discrepancy between the planned and the actual state of construction activities. This research proposes an integrated framework to detect and assess the consistency between design data and actual construction progress, combining SPARQL queries with computer vision (CV) techniques. These queries are used to extract and structure information from design models and related documents (e.g., BIM models, cost estimates, time schedules), while CV techniques analyse construction site images to support visual progress verification. The proposed approach enables an integrated validation of construction progress by combining temporal and visual data, enhancing transparency and traceability. The framework was tested on a real-world case study and supports intelligent and contextual queries on the project’s execution status, supporting semi-automatic decision-making for progress verification. Future work will focus on AI-driven methods to achieve fully automated data validation.