Technical Perspective: Unicorn: A Unified Multi-Tasking Matching Model

Data integration has been a long-standing challenge for data management. It has recently received significant attention due to at least three main reasons. First, many data science projects require integrating data from disparate sources before analysis can be carried out to extract insights. Second, many organizations want to build knowledge graphs, such as Customer 360s, Product 360s, and Supplier 360s, which capture all available information about the customers, products, and suppliers of an organization. Building such knowledge graphs often requires integrating data from multiple sources. Finally, there is also an increasing need to integrate a massive amount of data to create training data for AI models, such as large language models.