Abstract

Data fusion is the process of combining data and information from two or more sources. One of its application areas is market research, where it is used to combine data sets from different surveys, yielding a joint data set. Most data fusion studies use statistical matching as their fusion algorithm, which has several drawbacks. Therefore, we propose a novel approach to data fusion, based on knowledge discovery and knowledge representation with probabilistic conditional logic. We evaluate our approach on synthetic and real-world data, demonstrating its feasibility