Enriching integrated statistical open city data by combining equational knowledge and missing value imputation

Apart from providing a contribution to the growing collection of data available as Linked Data, our enrichment process for missing values also contributes a novel methodology for combining rule-based inference about equational knowledge with inferences obtained from statistical Machine Learning approaches. While most existing works about inference in Linked Data have focused on ontological reasoning in RDFS and OWL, we believe that these complementary methods and particularly their combination could be fruitfully applied also in many other domains for integrating Statistical Linked Data, independent from our concrete use case of integrating city data.