Entity is a term used to refer to a real-world object that can be identified and distinguished from other objects. This may include things like people, places, organizations, products, and so on.
There are a few reasons why identifying entities in a database are important. First, entities can be used to create features for machine learning models. Second, entities can be used to generate summaries of text documents. Finally, entities can be used to generate targeted queries for search engines.
First Step: Entity Extraction
Entity extraction is the process of identifying and extracting entities from a text document. This can be done using various methods, such as rule-based systems, statistical models, or neural networks.
Statistical models are often used for entity extraction, as they can take into account a variety of features that may be associated with entities. For example, entities may be more likely to occur at the beginning of a document, or they may be associated with certain keywords.
Neural networks can also be used for entity extraction. These models are trained on a large dataset of documents, and they learn to identify entities based on the context in which they appear.
Step 2: Entity Manipulation
There are a few different ways that entities can be manipulated in a data analytics process. For example, entities can be used to create features for machine learning models, or they can be used to generate summaries of text documents.
Entities can also be used to create graphs of relationships between different objects. For example, a graph of entities could be used to show the relationships between different people, places, and organizations in a text document.
Finally, entities can be used to generate targeted queries for search engines. For example, if you are looking for information about a specific person, you could use their name as an entity to generate a query that would only return results about that person.