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Nestor Rychtyckyj is a senior analytics sci-
entist for global data insight and analytics
at Ford Motor Company in Dearborn,
Michigan. His responsibilities include the
application of machine learning, natural
language processing, semantic computing,
and machine translation for manufactur-
ing, quality, customer interaction and
cybersecurity. Previously, Rychtyckyj was
responsible for the development and
deployment of AI-based systems for vehicle
assembly process planning and ergonomic
analysis in manufacturing. He received his
Ph.D. in computer science from Wayne
State University in Detroit, Michigan.
Rychtyckyj is a senior member of AAAI and
IEEE and a member of ACM.
Venkatesh Raman is a senior data analyst
for global data insight and analytics at Ford
Motor Pvt. Ltd. in Chennai, India. His
responsibilities include leveraging the big
data platform and tools for analyzing and
applying machine learning to connected
vehicle data. Previously, Raman was with
the Enterprise Technology Research group
wherein he was researching the big data
domain and evangelizing it. He received his
master’s degree in computer science from
MS University in India.
Baskaran Sankaranarayanan is a
researcher in the Department of Computer
Science and Engineering, IIT Madras, India.
He has more than 10 years of industry experience in designing, developing, and
deploying large-scale data cleansing, data
integration, and OLAP applications for
retail, banking, financial services, health
care, credit rating, and magazine domains.
He is interested in the application of ontology to real-world problems. His long-term
goal is to develop efficient data integration
frameworks. He holds a master’s degree in
structural engineering from IIT Bombay,
and a bachelor’s degree in civil engineering
from University of Madras.
P. Sreenivasa Kumar is a professor in the
Department of Computer Science and Engineering (CSE), IIT Madras, India. He was
also the head of the Computer Science and
Engineering Department during the years
2013–2015. His research interests include
database systems, semistructured data and
XML, ontologies and semantic web, data
mining, graph algorithms, and parallel
computing. He earned his bachelor’s degree
in electronics and communication engineering from the Sri Venkateswara University College of Engineering, Tirupati, India.
His master’s and Ph.D. degrees are in computer science from the Indian Institute of
Science, Bangalore, India.
Deepak Khemani is a professor in the
Department of Computer Science and Engineering, IIT Madras, India. His long-term
goal is to build articulate problem-solving
systems that can interact with humans, currently looking at contract bridge. He works
in memory-based reasoning, knowledge
representation, planning, constraint satisfaction, and qualitative reasoning. He graduated with three degrees from IIT Bombay,
including two in computer science. He is
the author of A First Course in Artificial Intelligence.
includes a number of benefits that will
pay dividends in the future. The standards and tools built around semantic
technologies make our ontology easily
accessible to other applications and will
reduce future expenses in terms of
maintenance and development costs.
In addition, this project has helped us
build the infrastructure needed to support semantic technology and allow for
the development of other projects that
could benefit from the semantic web.
Our future work will include the
deployment of other ontologies into
production as well as the use of semantic web tools and semantic web architecture for ontology development and
maintenance. However, the real benefit will occur as we leverage semantic
technology across other areas of the
company and integrate this into our
development and manufacturing
1. International Resource Identifier (IRI).
2. International Resource Identifier (IRI).
3. In Pellet, concept classification is done
by a series of subsumption tests. Pellet
reports the execution time for each test,
and we sum up these times to compute
the classification time for a concept.
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