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This chapter argues for the importance of integrating unstructured textual data into existing data integration systems, which have historically focused on structured data. It highlights the untapped knowledge residing in text and the limitations of current systems in leveraging it. The chapter then outlines the challenges, current approaches, and open research questions related to text data integration.
Unstructured text holds a wealth of untapped knowledge, yet remains largely ignored by existing data integration systems.
Data comes in many forms. From a shallow perspective, they can be viewed as being either in structured (e.g., as a relation, as key-value pairs) or unstructured (e.g., text, image) formats. So far, machines have been fairly good at processing and reasoning over structured data that follows a precise schema. However, the heterogeneity of data poses a significant challenge on how well diverse categories of data can be meaningfully stored and processed. Data Integration, a crucial part of the data engineering pipeline, addresses this by combining disparate data sources and providing unified data access to end-users. Until now, most data integration systems have leaned on only combining structured data sources. Nevertheless, unstructured data (a.k.a. free text) also contains a plethora of knowledge waiting to be utilized. Thus, in this chapter, we firstly make the case for the integration of textual data, to later present its challenges, state of the art and open problems.
