Historical research often attempts to describe and understand phenomena of the collective behavior of people, at the level of communities or entire societies, with the aim of revealing the causes, influencing factors, processes, and effects of social change. This type of research can be greatly supported if primary data relating to the entities, events, behaviors, and historical sources of interest are systematically collected, documented, and consolidated. The challenge is to integrate the data extracted from the different primary and secondary sources based on a common model of representation, to then support the quantitative and qualitative analysis of the integrated data and their correlations. Currently, due to the limited use of information technology in historical research, this analysis is mainly carried out considering an a priori hypothesis which greatly reduces the richness of the information collected, thus making future exploitation of the data difficult. Semantic data integration practices are the ideal way to gather and integrate information under a common representation model, allowing the capture and preservation of the wealth of information extracted from the sources and related source data. In this workshop we explored the relevant challenges of historical research data integration and analysis, as well as the potential of available technologies and tools, in order to initiate a discussion on how semantic data integration can contribute to the field of historical research in the digital humanities .