Research data supporting '' Improving the accuracy of schedule information communication between humans and data''

The construction industry is starting to implement intelligent knowledge management systems to support effective project communication. However, current practice in creating construction schedules remains unstandardised, which hinders the effective human-data knowledge exchange and impedes machines from reading and processing the information. This study, therefore, introduces a semi-automatic method to develop a novel ontology that summarises the key constituents of construction activities, and train an online classifier to classify new schedules and extend the schedule dictionary. The outcome is a domain-extensible ontology prototype validated by the selection of 27 completed schedules with a minimum of 500 activities each within a wide range of project types. The experimental results indicate that ontology-based activities improved: (1) schedule’s readability by reducing the cosine similarity of ‘different’ activities reduced from 0.995 to 0.990 (p < 0.01), (2) schedule’s understandability from 75.90% to 85.55%, and (3) schedule’s coherence from -12.04 to -11.44. This approach facilitates quantitative schedule analysis, enables the automatic generation of construction schedules, and improves the human-data exchange of information in the construction industry.