I am dealing with the task to extract structured information from domain-specific unstructured documents. The end goal is to obtain a reliable, queryable system, i.e. in the form of a chat-bot or Question-Answering application.
During my research I formulated following solution approach:
Parse doc, docx and pdf documents to raw text. Pre-process with common NLP tools.
Consult with client and decide upon several categories for entities.
Train customised NER model to obtain entities from texts.
Populate knowledge base
Migrate knowledge base data to knowledge graph and set up the queryable system.
For the implementation, I was looking into Stanfords Deepdive architecture, however they stopped support in 2017 and I am not sure if it is still up to date.
Alternatively, I intended to use spaCy to train my own NER model, but it is unclear to me if this allows to obtain the entity-level-relations for populating a knowledge base.
Could you advise me to alternatives to Deepdive or any other resources that demonstrate how to build up such a system from scratch?