Question

In order to build a knowledge graph from a large text repository, we need to perform several tasks including entity extraction. Could you please describe these tasks and their connections?

Answer 1

A knowledge graph is a type of graph which represents different entities, a set of attributes, a relationship between the entities.
Entities are represented in the form of nodes. Edges represent relationships between the nodes. Each node consists of a set of attributes. Knowledge graph plays a significant role in AI tasks, tool for knowledge, information extraction, bridging the gap from data to semantics.

Yes, we need to perform different tasks for knowledge extraction.

1. The information is in normal text. We need to convert that information for NLP. The data is being converted into Annotated text wherein entities, locations, verbs, stop words are being processed.

2. Entity resolution, entity linking and relation extraction is being incorporated for information extraction.

3. Coreference resolution is being incorporated on the entities to differentiate and correlation. For example, Jon is a Person. His wife is Ren. Ren can also be called as Mrs.Jon. Based on the context, referencing is done.

4. Dependency tagging is done based on Parts of Speech and name entity recognition. How one word is being dependent on others is evaluated here.

5. NLP annotation was done to combine the dependency paths which were discovered, to combine tokens as well as entity types.