Final answer:
The transformation in question is a data processing approach utilizing information theoretic principles to handle large volumes of text data by categorizing entities and presenting metadata, crucial for fields with extensive datasets such as astronomy.
Step-by-step explanation:
The transformation used to analyze and process large volumes of text data involves searching entities, assigning them to appropriate types, and presenting metadata in a standard format. This process is essential in handling large scale processes of data, particularly in fields like astronomy, where the volume of data collected can reach sizes comparable to the entirety of the Library of Congress. Utilizing information theoretic approaches, this transformation is highly adaptable, enabling effective organization and interpretation of data. These methods are not only easy to interpret and communicate, but they also allow for the automation of complex tasks such as querying within Markov blankets or comparing Bayesian Network (BN) topologies, which is a subject of ongoing research.