Semi-structured data does not follow any data model because it does not have a fixed schema. Unlike structured/tabular data, it lacks any rigid form. This type of data typically contains elements of both structured and unstructured data and is often found in databases and data warehouses. Examples of semi-structured data include JSON and XML files, which have both structured elements, such as key-value pairs, and unstructured elements, such as text and multimedia data. Processing and analyzing this type of data requires specialized techniques and solutions capable of handling both structured and unstructured data.
To effectively process and analyze semi-structured data for machine learning purposes, it is often necessary to first pre-process and convert the data into a format that can be used as input for machine learning algorithms. This may involve techniques such as data labeling. Semi-structured data can provide insights and opportunities for machine learning models, as it often contains rich and diverse information.