Research Publications

Concepts, Relations, Events

Scientist Profile

Andrew Kuznetsov

(Andrey Kuznetsov, Андрей Кузнецов, А.С. Кузнецов)

research scientist, NanoTechLab Ltd, San Diego (2010 - now)

Education:

MS, Physics, MGU, Moscow, Russia, 2002

Research Interests:

nanotechnology in medicine, smart drugs

Selected Co-authors:

K-Extractor™: See the scientific landscape at a glance

Customer Scenario
NEED

Know the top players in virology and mircobiology fields, what they are working on, who are they affiliated with, and who funds them.

CHALLENGE

Big Data: Volume, Variety & Velocity. Thousands of new papers are published every day, collecting this information and keeping it up to date is no longer feasible to do by hand.

Lymba's Solution
SOLUTION

K-Extractor automatically extracts knowledge centered on authors, their research topics and research funding to generate consolidated interlinked profiles for each person.

INPUT

Thousands of scientific research publications in PDF format, web pages with bio pages, conference schedules.

OUTPUT

Scientific profiles - creating in depth "baseball cards" for each person with the following information:

Employment and affiliation history

Projects, co-workers and co-authors

Funding and awards

Education

Topics of interest

Family and personal information

Contact information

Each item on profile has a direct link to supporting documents from input collections. Profiles are connected to each other with hypertext links.

Key Features

Document structure recognition for PDFs and other complex file formats to detect the following elements:

– title, authors and affiliations

– headers and footers

– section titles and content

– citations and references

– tables and illustrations

Deep semantic processing of the text fragments to extract the concepts and semantic relations of interest.

Resolving aliases of people, organizations and other concepts: spelling variations, short forms, synonyms, etc. Merging concepts together enables the combination of knowledge from multiple sources.

Extracted knowledge is saved into an RDF store that is available to the customer. Lastly, the RDF store is automatically queried to generate connected profiles for each person mentioned in the input collection.

Check out K-Extractor for more details on the product and its features.