Construction claim analysis largely depends on determining the existence of impact factors. Extensive research has been conducted on the relationships between impact factors and outcomes of construction litigation from various perspectives and using various methodologies. However, only a few of them have taken into consideration the automated semantic interpretation of impact factors contained in a construction litigation case. In this paper, based on previous pilot studies on the domain ontologies of construction contractual semantics, a rule-based NLP (Natural Language Processing) methodology for semantically interpreting impact factors for construction claim cases is proposed. Based on the available NLP techniques and domain ontologies, this methodology utilizes a rule-based mechanism to achieve the mapping from textual elements to ontology entities. In this way, the support of domain ontology is provided to enhance the performance of the impact factor interpretation process in the text. Also, it was found that several software packages can work together to satisfy the demand for the implementation of this methodology. Further, to test the validity of this methodology, several case studies focusing on DSC (Differing Site Conditions) claims are conducted by adopting cases from legal databases as data. The significance of this research is that it provides a more automated functionality to the traditional approach of claim outcome prediction by adding one more semantic factor-interpreting layer to it, and by also exploring the application of ontology-based NLP in the domain of construction claim analysis via text processing.
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