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SUD MODEL
RVO Sustainable Urban Delta
Dr. Evgeny Knutov
John Walker
KnowSyms B.V. / Semaku B.V.
18 Aug. 2017
PROBLEM STATEMENT
SUD MODEL
A system for the flexible
management of a dynamic co-
evolving document collection and
knowledge structures in a focused
domain. The work reported here is in
the context of document and
knowledge management activities in
the context of the SUD data
modelling project at RVO.
Within a framework a vast amount of
unstructured information becomes
available in the form of different
reports (primarily PDF) submitted by
different companies, and experts.
There is a need to automate the
processing of these reports and to
help domain experts to find and
analyze the most important
information, and turn this
information into a knowledge base.
KNOWLEDGE AND LINKS
documents
and keywords knowledge
interlinked
documents and
keywords
SUD ONTOLOGY BASICS
• Ontology explains semantics of the
information “how do we convey the meaning”
• Formal naming and definition of types and
interrelationships that formally exist in the
described domain
• Consists of triples or semantic triples
• Triple represents subject-predicate-object
• e.g. (RVO) - (located in) - (Utrecht)
ONTOLOGY BASICS OF THE SUD MODEL
SYSTEM OVERVIEW
TERMS
DOCUMENTS
INDEX
KNOWLEDGE
ONTOLOGY
TRIPLES
QUERIES
CLICKS
FOUND
DOCUMENT
PIECES
FOUND
KNOWLEDGE
AND
RELATIONS
SEARCH BOX
High-level SUD environment overview
SUD ONTOLOGY
• information is represented in
triples
• started with ~300 triples and
counting
• additional energy-related
ontologies with >1000 triples
• RDF format (industry standard)
• “easily” add new instances and
concepts
• interchangeable ontologies
(switch your SUD-related
knowledge base on the fly)
SEARCH AND EXPLORE
FOUND
DOCUMENTS
discovered
documents and
related
places in the
documents
RELATED KNOWLEDGE
discovered
terms/keywords and
relationships/connections
SEARCH AND EXPLORE (CONT.)
• provides search and exploration functionality across
the knowledge base(s) and all the documents
• offers integration of the knowledge terms and
triggers document (re-)search results refinement
• adjustable search and viewing options
• change your knowledge base on the fly
• adjust the viewing option and the knowledge depth
BASIC DOCUMENT VIEW
• basic view of the
text snippets
containing the
found information
• immediately get
access to the
original PDF
document
• highlighted term(s)
and predefined wiki
links
EXTENDED DOCUMENT VIEW
• “enable detailed snippets”
provides extra insight on the
found document and
keywords
• virtually every aspect of the
document presentation is
internally adjustable
• length of textual
information, highlights,
clickable links, etc.
RELATED KNOWLEDGE
RELATED KNOWLEDGE (CONT.)
supplemental ontology
EXAMPLES
AND
SCENARIOS
EXAMPLE 1: MAIN EXPLORATION SCENARIO (CONT.)
Challenges - Agriculture - Greenport - Venlo - Location - Eindhoven - Brainport
EXAMPLE 2: ADDING NEW KNOWLEDGE ELEMENT
• search for
“challenge” (currently
results in 12 challenge
types)
• adding new “security”
challenge (aka new
“Challenge”class individual)
EXAMPLE 2: ADDING NEW ONTOLOGY ELEMENT (CONT.2)
• in the main search you will
have a possibility to explore
more challenges thus
narrow down document
search
• thus the whole new types of
challenges become instantly
discoverable in the whole
document set
EXAMPLE 5: KNOWLEDGE INTEROPERABILITY
• switch the knowledge
on the fly
• use different
knowledge with the
same documents
EXAMPLE 5: KNOWLEDGE INTEROPERABILITY (CONT.)
• or the same knowledge
with a different
document set (not in
the system)
• system is agnostic to
the documents and/or
the knowledge
• can be used throughout
multiple domains
EXAMPLE 6: EXTENDING KNOWLEDGE
• Extending the knowledge beyond
the concerned domain (e.g.
Wikipedia or DBpedia)
• incorporating in the ontology
• using external features
TAKING IT ONE STEP FURTHER
• lots of possibilities to
adjust and enrich the
system functionality
• interchanging
ontologies and
document sets
• user feedback: system
becomes better when
users decide on the
documents relevancy
• automatic
summarization on a
certain topic
• automatic report
generation
• custom features, etc.
etc.
COMBINED AND INTERCHANGEABLE KNOWLEDGE
• Combine knowledge from
multiple sources
• e.g. via federated
queries among multiple
knowledge bases
including “SUDmodel”
• general accepted
knowledge such as
DBpedia (Wikipedia of
concepts)
• easily re-use ontologies
from a different domain
• use current knowledge
with the different
document set
• can be used with the
external document
supplier (with a generic
formatting/schema)
OTHER VERSIONS
❖ Heat and Energy -related
❖ Separation technology -related
❖ Map integration
❖ Plain version (sandbox)
TECHNICAL DETAILS OF THE ENVIRONMENT
• runs on the Ubuntu 16.04
LTS server OS
• uses open-source third
party solutions
• Apache Solr 4.10 - 6.10
• Apache Fuseki 2.4.1
• Apache HTTP2 server
• custom build JavaScript
framework
THANKS TO
Marion Bakker
Tom Monne
QUESTIONS?

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Session 1.4 sustainable urban delta knowledge and semantic search

  • 1. SUD MODEL RVO Sustainable Urban Delta Dr. Evgeny Knutov John Walker KnowSyms B.V. / Semaku B.V. 18 Aug. 2017
  • 2. PROBLEM STATEMENT SUD MODEL A system for the flexible management of a dynamic co- evolving document collection and knowledge structures in a focused domain. The work reported here is in the context of document and knowledge management activities in the context of the SUD data modelling project at RVO. Within a framework a vast amount of unstructured information becomes available in the form of different reports (primarily PDF) submitted by different companies, and experts. There is a need to automate the processing of these reports and to help domain experts to find and analyze the most important information, and turn this information into a knowledge base.
  • 3. KNOWLEDGE AND LINKS documents and keywords knowledge interlinked documents and keywords
  • 4. SUD ONTOLOGY BASICS • Ontology explains semantics of the information “how do we convey the meaning” • Formal naming and definition of types and interrelationships that formally exist in the described domain • Consists of triples or semantic triples • Triple represents subject-predicate-object • e.g. (RVO) - (located in) - (Utrecht)
  • 5. ONTOLOGY BASICS OF THE SUD MODEL
  • 7. SUD ONTOLOGY • information is represented in triples • started with ~300 triples and counting • additional energy-related ontologies with >1000 triples • RDF format (industry standard) • “easily” add new instances and concepts • interchangeable ontologies (switch your SUD-related knowledge base on the fly)
  • 8. SEARCH AND EXPLORE FOUND DOCUMENTS discovered documents and related places in the documents RELATED KNOWLEDGE discovered terms/keywords and relationships/connections
  • 9. SEARCH AND EXPLORE (CONT.) • provides search and exploration functionality across the knowledge base(s) and all the documents • offers integration of the knowledge terms and triggers document (re-)search results refinement • adjustable search and viewing options • change your knowledge base on the fly • adjust the viewing option and the knowledge depth
  • 10. BASIC DOCUMENT VIEW • basic view of the text snippets containing the found information • immediately get access to the original PDF document • highlighted term(s) and predefined wiki links
  • 11. EXTENDED DOCUMENT VIEW • “enable detailed snippets” provides extra insight on the found document and keywords • virtually every aspect of the document presentation is internally adjustable • length of textual information, highlights, clickable links, etc.
  • 15. EXAMPLE 1: MAIN EXPLORATION SCENARIO (CONT.) Challenges - Agriculture - Greenport - Venlo - Location - Eindhoven - Brainport
  • 16. EXAMPLE 2: ADDING NEW KNOWLEDGE ELEMENT • search for “challenge” (currently results in 12 challenge types) • adding new “security” challenge (aka new “Challenge”class individual)
  • 17. EXAMPLE 2: ADDING NEW ONTOLOGY ELEMENT (CONT.2) • in the main search you will have a possibility to explore more challenges thus narrow down document search • thus the whole new types of challenges become instantly discoverable in the whole document set
  • 18. EXAMPLE 5: KNOWLEDGE INTEROPERABILITY • switch the knowledge on the fly • use different knowledge with the same documents
  • 19. EXAMPLE 5: KNOWLEDGE INTEROPERABILITY (CONT.) • or the same knowledge with a different document set (not in the system) • system is agnostic to the documents and/or the knowledge • can be used throughout multiple domains
  • 20. EXAMPLE 6: EXTENDING KNOWLEDGE • Extending the knowledge beyond the concerned domain (e.g. Wikipedia or DBpedia) • incorporating in the ontology • using external features
  • 21. TAKING IT ONE STEP FURTHER • lots of possibilities to adjust and enrich the system functionality • interchanging ontologies and document sets • user feedback: system becomes better when users decide on the documents relevancy • automatic summarization on a certain topic • automatic report generation • custom features, etc. etc.
  • 22. COMBINED AND INTERCHANGEABLE KNOWLEDGE • Combine knowledge from multiple sources • e.g. via federated queries among multiple knowledge bases including “SUDmodel” • general accepted knowledge such as DBpedia (Wikipedia of concepts) • easily re-use ontologies from a different domain • use current knowledge with the different document set • can be used with the external document supplier (with a generic formatting/schema)
  • 23. OTHER VERSIONS ❖ Heat and Energy -related ❖ Separation technology -related ❖ Map integration ❖ Plain version (sandbox)
  • 24. TECHNICAL DETAILS OF THE ENVIRONMENT • runs on the Ubuntu 16.04 LTS server OS • uses open-source third party solutions • Apache Solr 4.10 - 6.10 • Apache Fuseki 2.4.1 • Apache HTTP2 server • custom build JavaScript framework