http://2015.semantics.cc/eelco-kruizinga

1. DNV GL © 2015 SAFER, SMARTER, GREENERDNV GL © 2015
Matchmaking
A proof of concept for
The Climate Technology Centre and Network
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Eelco Kruizinga
Eelco.Kruizinga@dnvgl.com
DNV GL

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Standards, testing, inspection, certification and technical advice
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OIL & GASMARITIME ENERGY BUSINESS
ASSURANCE
SOFTWARE
RESEARCH & INNOVATION
CYBERNETICS

3. DNV GL © 2015
Safeguarding life, property and the environment, since 1864
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dnvgl.com

4. DNV GL © 2015
Strategic research and innovation programmes
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OIL & GAS AND
ENERGY SYSTEMS
ARCTIC TECHNOLOGY
POWER SYSTEMS
ELECTRIFICATION
MATERIALS
CLIMATE CHANGE
MARITIME TRANSPORTINFORMATION
TECHNOLOGY
HEALTHCARE
• Adaptation
• Mitigation
• Super grid
• Smart grid
• Storage
• Materials in energy storage
• Risk management of
corrodible systems
• Advanced materials and
sensors
• Autonomous systems
• Big data analytics of sensor
data
LOW CARBON FUTURE
• Renewable energy
• Green economy
• Transformation process
• Offshore safety
• Safety and reliability of the
subsea factory
• Energy foresight
• Arctic offshore structures
• Arctic ship structures
• Arctic oil spill preparedness
• Safer shipping
• Greener shipping
• Smarter shipping
• Patient safety

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Increased focus on drawing intelligence from data
“Other major developments we have just seen the start of,
and which provide great opportunities for most of our
business areas, are digitalization, connectivity and the
‘Internet of Things’. These will be crucial to ensure that the
components of wider integrated energy and transportation
systems work together as a whole. Digitalization is also
about cyber security and using data in smarter ways to gain
insight for better decisions.
So, we will use the strategy period to become a data
smart company, using digital solutions to innovate
and offer better services to our customers as well as
to increase our own efficiency”
August 2015.
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Group President
and CEO of DNV GL

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DNV GL strategic partnership with CTCN
 What is CTCN?
– UN operational body under UNFCCC
for combating climate change
– Focus on developing countries around the world
– Requests for assistance from developing countries
are screened and routed to the best partner
to provide the assistance
– Focus is on climate technologies: org/hard/software
 Areas of collaboration:
– Knowledge management
– Private sector engagement in climate smart solutions
– Monitoring and evaluation
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ctc-n.org

7. DNV GL © 2015 SAFER, SMARTER, GREENERDNV GL © 2015
CTCN Matchmaking Assistant
Proof of Concept Demonstrator
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Introduction
 CTCN provides a pyramid of services including response plans to requests
for technical assistance from so-called National Designated Entities (NDEs)
 Matching of requests for technical assistance with available consortium and
network partners, solutions and technology types is currently a manual
process
 Amount of requests is expected to increase substantially in the future, as is
the volume of network partners
 To cope with anticipated volumes, a web-based system can help recommend
partners, solutions and technology types with technical assistance requests
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Matchmaking at the heart of CTCN service provision
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Help to find the right solutions to
developing countries’ needs

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Key question
 Is it possible to automatically match incoming requests for assistance with
available expertise throughout the CTCN expertise pool, represented by a growing
number of network member organisations?
 If so, this would then allow CTCN to provide solutions to developing countries’
needs, in a scalable, yet high-quality way.
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Partners in PoC undertaking
 CTCN
 DNV GL
 Semantic Web Company
 REEEP
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Looking to create further impacts and efficiencies
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• Introduction of search
to KMS
• Climate technology
taxonomy and library
• Matchmaking
Assistant

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Demonstrator matchmaking assistant available in-line
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CTCN Climate Technology
Manager can access the
Matchmaking Assistant from
within their web-based
administration system.

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Results – CTCN Matchmaking Assistant – Home Page
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Note: examples provided are for illustration purposes only and do not represent actual decisions by CTCN

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PoC Use Case 1 - Recommender
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On-line
Matchmaking
Assistant
takes TA Request
as input
And suggests
organisations
that have the
expertise
to help
On the basis
of an extracted
‘fingerprint’
of the TA
RequestNote: examples provided are for illustration purposes only and do not represent actual decisions by CTCN

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PoC Use Case 1 – Recommender Content Highlights =
Fingerprint
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Note: examples provided are for illustration purposes only and do not represent actual decisions by CTCN

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PoC Use Case 1 – Background information on Consortium
Partners Recommended
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PoC Use Case 1 – Background information on Network Partners
Recommended
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PoC Use Case 1 – Background information on relevant
initiatives from Case Studies and existing projects
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PoC Use Case 2 – Faceted Search
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The user can filter
through different
areas of the expert
vetted concepts
to display
documents in the
pool of content
with those extracted
concepts
Note: examples provided are for illustration purposes only and do not represent actual decisions by CTCN

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PoC Use Case 2 – Faceted Search
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The user can input
key concepts into
the open search
field and the system
recommends
concepts
from the thesaurus
Note: examples provided are for illustration purposes only and do not represent actual decisions by CTCN

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CTCN specific adaptations to wider climate tagger
thesaurus
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CTCN specific adaptations to the expert-vetted climate
tagger thesaurus
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CTCN Consortium Partner Depth of info example 1
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CTCN Consortium Partner Depth of info example 2
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User Acceptance Testing
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 The agreed benchmark for success was the positive feedback from the
Climate Technology Managers (CTMs)
• Will the Matchmaking Assistant help facilitate your work?
• Do you support this development based on your user testing?
• Can you see the potential in the system based on a live demonstration,
hands on testing, being able to provide feedback and seeing the speed at
which the developers can facilitate requested changes?
• The answer across the board was YES

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PoC Limitations Important to Keep in Mind
The CTCN Matchmaking Assistant is a Proof of Concept
 It demonstrates the potential of technology and approach
The documentation used on Consortium Partners and Network Members
varies in depth:
 Information regarding skills, expertise, regional focus and capacity is limited
 Currently a lot of this information is not on record but in the memory and
experience of CTCN CTMs
Any next phase will focus on integration of CTCN technology types
• From the expert-vetted process consulting with experts across the globe for
consensus, this has not been developed yet and will include approximately 500
new concepts
The scope of documents available for inclusion and analysis was limited
based on access to CTCN documentation thus far
• Solution would be to curate CTCN specific content in a next phase
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Lessons Learned
 Use semi-structured information about organizations (DB & documents)
 More information about organisations will benefit robustness of results
 Enrich assistant by data & info from 3rd party sources (e.g. country Info,
solutions, on-line info about skills and expertise)
 Additional information of experts to enhance the robustness of results
 Record decision rationale
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Key Future Development and Opportunities
 A public facing user interface to provide recommendations for technology
types and solutions to end users who may find what they need and not
submit a request for TA
 Actively crawl pre-determined sources to proactively gather on-line
information for knowledge base and contextual case studies,
solutions, the scope to be finalized in coming discussions
 Use the power of the system as framework to develop new protocols for
on-boarding new network partners in future
 Further development of functionality – ranking, geographic expertise,
balancing selection
 Further customization of search facets and user interface for CTCN
CTMs – i.e. order of displayed sectors, information about partners
 Development and integration of technology types and solutions into
existing Climate Tagger Thesaurus
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Development of a ‘climate problem-language’ and inference via thesaurus
relations

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Future use cases?
• Front end user interface to search for known solutions and technology types
before submitting a request to CTCN open to the public
• Advanced Semantic Search
• CTCN Technology Pages
• News Aggregation and / or Media Monitoring Services
• CTCN Glossary Service
• Use Recommender Services for Climate Technology from Climate Tagger
• Enable Personalization Services
• Expand the use of Relations in the Climate Tagger Thesaurus relating
partners to specific regions, technologies, expertise utilizing existing
technology
• Multiple languages such as French, Spanish, Portuguese and German – can
develop further languages in the thesaurus in future
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31. DNV GL © 2015 SAFER, SMARTER, GREENERDNV GL © 201532
Eelco Kruizinga, Eelco.Kruizinga@dnvgl.com
Knowledge Management Group, DNV GL
Thank you.