CC BY-NC-ND 4.0Müller, AlmuthAlmuthMüllerKuwertz, AchimAchimKuwertz2022-09-262022-09-262022https://publica.fraunhofer.de/handle/publica/426966https://doi.org/10.24406/publica-35410.5220/001130700000326910.24406/publica-354This paper presents a concept for a two-tire semi-automated approach for business data entity resolution. Resolving entity names is generally relevant e.g. in business intelligence. When applied, several difficulties have to be considered, such as name deviations for an organization. Here, two types of deviations can be distinguished. First, names can differ due to typos, native special characters or transformation errors. Second, an organization name can change due to outdated designations or being given in another language. A further aspect is data sovereignty. Analyzed data sources can be under direct control, e.g. in own data storage systems, and thus be kept clean. Yet, other sources of relevant data may only be publicly available. It is in general not recommended to copy such data, due to e.g. its amount and data duplication issues. The proposed two-tire approach for entity resolution thus not only considers different kinds of name derivations, but also data sovereignty issues. Being still work in progress, it yet has the potential to reduce the effort required when compared to manual approaches and can possibly be applied in different areas where there is a significant need for harmonized data and externally curated systems are not feasible.enEntity ResolutionRecord LinkageDeduplicationNatural Language ProcessingFuzzy Matchingconference paper