Developing a Legal Form Classification and Extraction Approach for Company Entity Matching

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Title
Developing a Legal Form Classification and Extraction Approach for Company Entity Matching
Subtitle
Benchmark of Rule-Based and Machine Learning Approaches
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Author
License
CC Attribution 4.0 International:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Publisher
Release Date
2021
Language
English

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Subject Area
Abstract
This paper explores the data integration process step record linkage. Thereby we focus on the entity company. For the integration of company data, the company name is a crucial attribute, which often includes the legal form. This legal form is not concise and consistent represented among different data sources, which leads to considerable data quality problems for the further process steps in record linkage. To solve these problems, we classify and ex-tract the legal form from the attribute company name. For this purpose, we iteratively developed four different approaches and compared them in a benchmark. The best approach is a hybrid approach combining a rule set and a supervised machine learning model. With our developed hybrid approach, any company data sets from research or business can be processed. Thus, the data quality for subsequent data processing steps such as record linkage can be improved. Furthermore, our approach can be adapted to solve the same data quality problems in other attributes.
Keywords Record Linkage Company Entity Matching Data Integration Data Quality Data Preparation

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