Exposing the Entity Web
Unstructured natural language text found in blogs, news and other web
content is rich with semantic relations linking entities (people, places
and things). At Evri, we are building a system which automatically reads
web content similar to the way humans do. The system can be thought of as
an army of 7th grade grammar students armed with a really large dictionary.
The dictionary, or knowledge base, consists of relatively static
information mined from structured and semi-structured publicly available
information repositories like Crunchbase, Wikipedia, and Amazon.
This large knowledge base is in turn used by a highly distributed search
and indexing infrastructure to perform a deep linguistic analysis of many
millions of documents ultimately culminating in a large set of semantic
relationships expressing grammatical SVO style clause level relationships.
This highly expressive, exacting, and scalable index makes possible a new
generation of information navigation and content recommendation
applications.
Biography:
Deep Dhillon is the CTO of Seattle based startup Evri. He is an
accomplished engineer and systems architect with extensive experience
conceptualizing, architecting and deploying multiple high performance
advanced networking applications. Mr. Dhillon received a B.S. in Electrical
Engineering from the Illinois Institute of Technology and a M.S. in
Electrical Engineering from the University of Wisconsin–Madison.
Evri Background:
Evri is a Seattle based web start-up company funded by Paul Allen’s Vulcan
Ventures and led by Will Hunsinger, former CEO at Adeze and VP at Gap
Online. Evri has multiple products including web based topic pages, a suite
of content publisher widgets, a browser toolbar application, and an
extensive API. Evri recently launched its application on Hearst
Corporation’s LMK.com site, on Yahoo! Sports pages, and on the Washington
Post.