Record linkage is an essential part of nearly all real-world systems that consume structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and data integration processes often have to be completed before any data analytics and further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights into the data at hand. These insights guide further analytic approaches to the data and support data visualization.In this work we focus on company entity matching, where company name, location and industry are taken into account. Our contribution is an end-to-end, highly scalable, enterprise-grade system that uses rule-based linkage algorithms extended with a machine learning approach to account for short company names. Linkage time is greatly reduced by efficient decomposition of the search space using MinHash. High linkage accuracy is achieved by the proposed thorough scoring process of the matching candidates.Based on real-world ground truth datasets, we show that our approach reaches a recall of 91% compared to 73% for baseline approaches. These results are achieved while scaling linearly with the number of nodes used in the system. IntroductionEnterprise artificial intelligence applications require the integration of many data sources. In such applications, one of the most important entity attributes to be linked is often the company name. It acts as a "primary key" across multiple datasets such as company descriptions, marketing intelligence databases, ledger databases, or stock market related data. The technique used to preform such a linkage is commonly referred to as record linkage or entity matching.Record linkage (RL) has been extensively studied in recent decades. RL is in charge of joining various representations of the same entity (e.g., a company, an organization, a product, etc.) residing in structured records coming from different datasets [26]. It was formalized by Fellegi and Sunter in 1969 [12], and the tutorial by Lise Getoor [13] provides an excellent overview of use cases and techniques. Essentially, RL has been used to link entities from different sets or to deduplicate/canonize entities within a given set. To this extent, several approaches have been envisaged ranging from feature matching or rule-based to machine learning approaches.Typically, RL is performed in batch mode to link a large number of entities between two or more databases [13]. A challenge of enterprise applications is the ever-increasing amount of unstructured data such as news, blogs and social media content to be integrated with enterprise data. As a consequence, RL has to be performed between structured records and unstructured documents. This large amount of data may flow in streams for rapid consumption and analysis by enterprise systems. For example, if we consider the analysis of news articles, it is not uncommon to have to process about one million n...

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