Building knowledge graphs of regulatory documentation based on semantic modeling and automatic term extraction

The paper proposes a new complex solution for automatic analysis and terms identification in regulatory and technical documentation (RTD). The task of terms identification in the documentation is one of the key issues in the digitalization dealing with the design and construction of buildings and structures. At the moment, the search and verification of RTD requirements is performed manually, which entails a significant number of errors. Automation of such tasks will significantly improve the quality of computer-aided design. The developed algorithm is based on such methods of natural language analysis as tokenization, search for lemmas and stems, analysis of stop words and word embeddings applied to tokens and phrases, part-of-speech tagging, syntactic annotation, etc. The experiments on the automatic extraction of terms from regulatory documents have shown great prospects of the proposed algorithm and its application for building knowledge graphs in the design domain. The recognition accuracy for 202 documents selected by experts was 79 % for the coincidence of names and 37 % for the coincidence of term identifiers. This is a comparable result with the known approaches to solving this problem. The results of the work can be used in computer-aided design systems based on Building information modeling (BIM) models, as well as to automate the examination of design documentation.