The use of automated systems to collect, process and analyse vast amounts of data is now integral to the operations of many corporations and government agencies, in particular it has gained recognition as a strategic tool in the war on crime. Data mining, the technology behind such analysis, has its origins in quantitative sciences. Yet, analysts face important issues of a cognitive nature both in terms of the input for the data mining effort, and in terms of the analysis of the output. Domain knowledge and bias information influence which patterns in the data are deemed as useful and, ultimately, valid. This chapter addresses the role of cognition and context in the interpretation and validation of mined knowledge. We propose the use of ontology charts and norm specifications to map how varying levels of access to information and exposure to specific social norms lead to divergent views of mined knowledge.