The results of quantity take-off (QTO) based on building information modeling (BIM) technology rely heavily on the geometry and semantics of 3D objects that may vary among BIM model creation methods. Furthermore, conventional BIM models do not contain all the required information for automatic QTO and the results do not follow the descriptive rules in the standard method of measurement (SMM). This paper presents a new knowledge model-based framework that incorporates the semantic information and SMM rules in BIM for automatic code-compliant QTO. It begins with domain knowledge modeling, taking into consideration QTO-related information, semantic QTO entities and relationships, and SMM logic formulation. Subsequently, linguistic-based approaches are developed to automatically audit the BIM model integrity for QTO purposes, with QTO algorithms developed and used in a case study for demonstration. The results indicate that the proposed new framework automatically identifies the semantic errors in BIM models and obtains code-compliant quantities. •Established a semantic data model for BIM-based QTO.•Formulated the SMM descriptive rules to support automatic and accurate QTO.•Developed a linguistic-based approach to semantic auditing for QTO purposes.•Developed new algorithms for automatic measurement of modeled and unmodeled elements.•Verified the algorithms with >99% compliance with SMM in different scenarios.