propel: topic and trend analysis
TRANSCRIPT
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PROPEL: Topic and trend analysis Javier D. Fernández, Sabrina Kirrane, Axel Polleres
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Interviews
23 interviews:
§ Domains § Consulting, Engineering, Environment, Finance and Insurance, Government,
Healthcare, ICT, IT, Media, Pharmaceutical, Professional Services, Real Estate, Research, Startup, Tourism, Transports & Logistics
§ Roles § Business Intelligence, CEO, Chief Engineer, Data and Systems Architect,
Data Scientist, Director Information Management, Enterprise Architect, Founder, General Secretary, Governance, Risk & Compliance Manager, Head of Communications and Media, Head of Development, Head of HR, Head of R&D, Innovation Manager, Information Architect, IT Project Manager, Management, Managing director, Marketing Analyst, Principle System Analyst, Project Coordinator, Researcher, Technical Specialist
PROPEL 2
Note: Instead of explaining them what ELD is, we gathered their technology/
research expectations from a more general SW perspective
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Workshop
§ Took place on the 10th of May 2016 • private sector (solution providers and users) • research sector (technicians and strategic people)
§ Business barriers and drivers § Technological challenges and opportunities
PROPEL 3
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User Stories
4 out of 60 user stories we collected in the interviews:
§ Horizontals • Business processes (e.g. product logistics and supply chain
management) • Human resources (e.g. expert and resource management)
§ Verticals • Media & Publishing • Healthcare & Pharma
PROPEL 4
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Technologies in need…
PROPEL 5
Analytics Computational linguistics & NLP
Concept tagging & annotation Data integration
Data management Dynamic data / streaming
Extraction, data mining, text mining,
entity extraction
Logic, formal languages &
reasoning
Human-Computer Interaction & visualization
Knowledge representation Machine learning
Ontology/thesaurus/taxonomy
management
Quality & Provenance Recommendations
Robustness, scalability,
optimization and performance
Searching, browsing & exploration
Security and privacy System engineering
We pretty much ended up in all areas that SW touches upon!
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Business Processes
PROPEL 6
“I would like to be able to exchange information and coordinate production and logistics with suppliers and customers…”
“…so that I can improve efficiency, effectiveness and flexibility of my inventory management and operations”
Analytics Computational linguistics & NLP
Concept tagging & annotation Data integration
Data management
Dynamic data / streaming
Extraction, data mining, text
mining, entity extraction
Logic, formal languages &
reasoning
Human-Computer
Interaction & visualization
Knowledge representation Machine learning
Ontology/thesaurus/taxonomy
management
Data Quality &
Provenance Recommendations
Robustness, scalability,
optimization and performance
Searching, browsing & exploration
Security and privacy
System engineering
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Human Resources
PROPEL 7
“I would like identify expertise within our large organisation and be able to pinpoint the relevant experts…”
“…so that I can I can identify top trends within the organisation and expertise for the organisation as a whole”
Knowledge representation
Analytics Computational linguistics & NLP
Concept tagging & annotation Data integration
Data management
Dynamic data / streaming
Extraction, data mining, text
mining, entity extraction
Logic, formal languages &
reasoning
Human-Computer
Interaction & visualization
Knowledge representation Machine learning
Ontology/thesaurus/taxonomy
management
Quality& Provenance Recommendations
Robustness, scalability,
optimization and performance
Searching, browsing & exploration
Security and privacy
System engineering
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Media & Publishing
PROPEL 8
I would like to display personalized content as precise as possible
So that my readers stay as long as possible on my website.
Analytics Computational linguistics & NLP
Concept tagging & annotation Data integration
Data management
Dynamic data / streaming
Extraction, data mining, text
mining, entity extraction
Logic, formal languages &
reasoning
Human-Computer
Interaction & visualization
Knowledge representation Machine learning
Ontology/thesaurus/taxonomy
management
Data Quality &
Provenance Recommendations
Robustness, scalability,
optimization and performance
Searching, browsing & exploration
Security and privacy
System engineering
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Healthcare & Pharma
PROPEL 9
I would like to Integrate disparate systems that are: -Hard to integrate -Widespread -Contain the same data that contradicts each other
So that I can gain insights from other clinical trials
Analytics Computational linguistics & NLP
Concept tagging & annotation Data integration
Data management
Dynamic data / streaming
Extraction, data mining, text
mining, entity extraction
Logic, formal languages &
reasoning
Human-Computer
Interaction & visualization
Knowledge representation Machine learning
Ontology/thesaurus/taxonomy
management
Data Quality &
Provenance Recommendations
Robustness, scalability,
optimization and performance
Searching, browsing & exploration
Security and privacy
System engineering
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§ Monitoring SW community major venues: • ISWC (since 2006), ESWC (since 2006), SEMANTiCS
(since 2007), JWS (since 2006), SWJ (since 2010)
§ 3 seminal papers:
PROPEL 10
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Community Analysis
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Topic Categorisation
PROPEL 11
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Coverage per Foundation
PROPEL 12
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Semantic Web/Linked Data over time…
PROPEL 13
Subtopics: Expressing Meaning Knowledge Representation Ontologies Agents Evolution of Knowledge
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Knowledge Representation & Reasoning
PROPEL 14
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Knowledge Representation & Reasoning
PROPEL 15
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Semantic Web/Linked Data over time…
PROPEL 16
Early adopters: MITRE Chevron British Telecom Boeing Ordnance Survey Eli Lily Pfizer Agfa Food and Drug Administration National Institutes of Health Software adopters/products: Oracle Adobe Altova OpenLink TopQuadrant Software AG Aduna Software Protége SAPHIRE
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LD Adopters - Companies
PROPEL 17
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LD Adopters - Companies
PROPEL 18
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LD Adopters - Companies
PROPEL 19
0
200
400
600
800
1000
1200
1400
1600
Google Oracle Yahoo SAP IEEE7
Intelligent7Systems
Franz Bing Expert7
System
IBM7Research Poolparty
Occurrences
Companies
Conference7Sponsors7that7appear7in7papers72006H2015
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PROPEL 20
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LD Adopters - Domains
PROPEL 21
0
5000
10000
15000
20000
25000
30000
35000
occu
rren
ces
Domains
Topics grouped by domain 2006-2015
Well, they publish in
other venues…
E.g.:
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Semantic Web/Linked Data over time…
PROPEL 22
The authors claim that "early research has transitioned into these larger, more applied systems, today’s Semantic Web research is changing: It builds on the earlier foundations but it has generated a more diverse set of pursuits”.
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Looking to the future
PROPEL 23
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Looking to the future
PROPEL 24