Using information in structured text documents

Traditional knowledge management tools relied on structured data often stored in relational databases. Using artificial intelligence (AI), natural language processing (NLP), and information retrieval (IR) technologies it is now possible for automated systems to organize and use information in structured text documents like web pages, PDF files, and word processing documents.

The product

The system is a general purpose document repository with tools to automatically map semantic relationships in documents. These semantic mappings enhance IR by providing both a natural language query interface and enhanced semantic search capabilities.

The system is currently in internal development and a public beta release is planned for the first quarter of 2015.

How does it work?

We are glad you asked! Input documents are "chunked" into document sections, paragraphs, and sentences. Entities (or "things" like people, companies, organizations, and geographic objects) are identified in text and semantic relationships are calculated. These relationships help the system automatically tie different sections in documents (and sections between documents) together and help system users discover all available information for their current work tasks.

How well does it work?

In all honesty, NLP and IR are evolving technologies and the product goals for are to make it easier for users to do their work with some automation but the human user is still the key resource. The system helps but the end user still "does the work."

Please try my NLP demos is my experiments using natural language processing. The KBSportal software is available for purchase. is an experimental Natural Language Processing search system to answer who/where/when questions using the DBPedia linked data.