Abstract
There are many different types of instruments and hundreds of different markets for investment, leading to an extremely large and hard-to-define universe of financial data. The related commercial offer is extremely heterogeneous and complex. In this scenario, it is difficult to source the most appropriate financial services providers. In the past, eProcurement mainly focused on using ERP management tools to record and examine previous buying decisions and expenditure data. In recent years, machine learning and artificial intelligence have been applied to procurement workflows, introducing computation of external or thirdparty unstructured data to achieve a higher level of market knowledge and decision automation. To exploit the possibilities provided by these new technologies to the full extent possible, theoretical models for understanding large amounts of unstructured data are essential. In this research-in-progress paper, we propose a taxonomy of financial data services and depict the related prototype ontological model, providing a possible conceptualization and specification of the domain of interest potentially useful for the development of applications based on semantic technologies.
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