Propelling the Potential of Linked Data in Enterprises
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Propelling the Potential of Linked Data in Enterprises
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Propelling the Potential of Linked Data in Enterprises
- 1. PROPEL: Topic and trend analysis Javier D. Fernández, Sabrina Kirrane, Axel Polleres
- 2. Interviews & Workshop 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 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 2
- 3. 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 3
- 4. Technologies in need… PROPEL 4 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!
- 5. Business Processes PROPEL 5 “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/thesaur us/taxonomy management Data Quality & Provenance Recommendations Robustness, scalability, optimization and performance Searching, browsing & exploration Security and privacy System engineering
- 6. Human Resources PROPEL 6 “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/thesaur us/taxonomy management Quality& Provenance Recommendations Robustness, scalability, optimization and performance Searching, browsing & exploration Security and privacy System engineering
- 7. Media & Publishing PROPEL 7 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/thesaur us/taxonomy management Data Quality & Provenance Recommendations Robustness, scalability, optimization and performance Searching, browsing & exploration Security and privacy System engineering
- 8. Healthcare & Pharma PROPEL 8 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/thesaur us/taxonomy management Data Quality & Provenance Recommendations Robustness, scalability, optimization and performance Searching, browsing & exploration Security and privacy System engineering
- 9. Let’s take a step back… What can we offer as a community? PROPEL 9 Taking an introspective view
- 12. Monitoring SW community major venues: • ISWC (since 2006), ESWC (since 2006), SEMANTiCS (since 2007), JWS (since 2006), SWJ (since 2010) 3 seminal papers: PROPEL 12 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Community Analysis
- 13. Semantic Web/Linked Data over time… PROPEL 13 Subtopics: Expressing Meaning Knowledge Representation Ontologies Agents Evolution of Knowledge
- 15. Semantic Web/Linked Data over time… PROPEL 15 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
- 16. LD Adopters - Companies PROPEL 16
- 17. LD Adopters - Companies PROPEL 17 0 200 400 600 800 1000 1200 1400 1600 Google Oracle Yahoo SAP IEEE Intelligent Systems Franz Bing Expert System IBM Research Poolparty Occurrences Companies Conference Sponsors that appear in papers 2006-2015
- 18. PROPEL 18
- 19. LD Adopters - Domains PROPEL 19 0 5000 10000 15000 20000 25000 30000 35000 occurrences Domains Topics grouped by domain 2006-2015 Well, they publish in other venues… E.g.:
- 20. Semantic Web/Linked Data over time… PROPEL 20 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”.
- 21. Looking to the future PROPEL 21
- 22. Roadmap for Enterprise LD? 1. Linked Data security and privacy requirements 2. Determining and resolving data quality issues 3. Managing large amounts of data and associated metadata such as provenance and temporal data 4. Visualisation requirements 5. Understand the different application areas and their maturity in terms of real world deployment 6. Showcases in terms of startups, projects, applications and systems based on Linked Data technologies 7. Linked Data and recommender system models 8. Dynamic linked datasets 22 https://www.linked-data.at/
Notas do Editor
- From the interviews we derived approximately 60 user stories and mapped them to the 18 Foundations listed earlier https://docs.google.com/spreadsheets/d/1x3s0r8Wlg5rt9paa5CwoP0MYwHPqlLj66WbClctbV9w/edit#gid=0 I selected 4 and added them here, however there are others incase you need them
- From the interviews we derived approximately 60 user stories and mapped them to the 18 Foundation https://docs.google.com/spreadsheets/d/1x3s0r8Wlg5rt9paa5CwoP0MYwHPqlLj66WbClctbV9w/edit#gid=0 I selected 4 and added them here, however there are others incase you need them Horizontal – applicable to any business Vertical – specific to a domain
- Result of a stakeholder workshop we conducted with those people… These are the foundations that we use for mapping the user stories from WP2 to the topics from WP3, therefore it is good to introduce them here Analytics Computational linguistics & NLP Concept tagging & annotation (******Personally I’m not convinced about this one******) Data integration Data management Dynamic data / streaming Extraction, data mining, text mining, entity extraction (******Personally I’m not convinced the overlap with NLP******) Logic, formal languages & reasoning Human-Computer Interaction & visualization Knowledge representation Machine learning Ontology/thesaurus/taxonomy management Quality Recommendations Robustness, scalability, optimization and performance Searching, browsing & exploration Security and privacy System engineering
- ****TODO Look for images that are relevant for this use case High level conclusion - Generally speaking we don’t need NLP and Analytics for this use case Analytics (Not needed for this use case) Computational linguistics & NLP (Not needed for this use case) Concept tagging & annotation (?) Data integration (Different suppliers have different formats therefore this is essential) Data management (Need to be able to handle provenance, temporal data, updates etc) Dynamic data / streaming (Order may be streamed or batched so not essential) Extraction, data mining, text mining, entity extraction (Entities will need to be extracted from line of business applications) Logic, formal languages & reasoning (Not needed for this use case) Human-Computer Interaction & visualization (This would be a nice to have but not essential, simple searching and browsing would suffice) Knowledge representation (nice to have semantics may not be essential as exchange formats can be agreed upfront) Machine learning (Not needed for this use case) Ontology/thesaurus/taxonomy management (nice to have semantics may not be essential as exchange formats can be agreed upfront) Quality (This is very important for line of business applications) Recommendations (Not needed for this use case) Robustness, scalability, optimization and performance (Not a big data use case but they still need to be able to handle reasonable data sizes) Searching, browsing & exploration (The main use case is around the exchange but searching and browsing would be useful) Security and privacy (This is very important for line of business applications) System engineering (APIs, Services etc will be needed)
- ****TODO Look for images that are relevant for this use case High level conclusion - Generally speaking we don’t Analytics for this usecase Analytics (Not needed for this use case) Computational linguistics & NLP (expertise may be in text form) Concept tagging & annotation (?) Data integration (Extertise profiles may be in many disparate systems) Data management (Need to be able to handle provenance, temporal data, updates etc) Dynamic data / streaming (Not needed for this use case) Extraction, data mining, text mining, entity extraction (Expertise will need to be extracted from structured and unstructured sources) Logic, formal languages & reasoning (Not needed for this use case) Human-Computer Interaction & visualization (This would be a nice to have but not essential, simple searching and browsing would suffice) Knowledge representation (Considering all the different potential data sources a common representation model would be useful) Machine learning (Not needed for this use case) Ontology/thesaurus/taxonomy management (Considering all the different potential data sources a common structure(s) would be useful) Quality (This is very important for line of business applications) Recommendations (Not needed for this use case) Robustness, scalability, optimization and performance (Not a big data use case but they still need to be able to handle reasonable data sizes) Searching, browsing & exploration (Searching and browsing is necessary) Security and privacy (This is very important for line of business applications) System engineering (APIs, Services etc will be needed)
- TODO Look for images that are relevant for this use case - This usecase could really benefit from the technology the community are working on SK to provide reasons for the colouring (Tue AM)
- TODO Look for images that are relevant for this use case - This usecase could really benefit from the technology the community are working on SK to provide reasons for the colouring (Tue AM)
- A not-yet-very-scientific approach… still hope this is interesting, maybe a bit controversial to discuss here!
- Outlines quite clearly what they thought back then the Semantic Web should be…
- Terms to do with Agents and Web Services from conference/journal dictionary – there is no high level foundation so we might need to merge some terms ontologies ontology management ontology engineering ontology languages agents web services software agents services agent-based … agents research definitly not going up, our community has largely been dominated by ontologies. Might need data before 2006, semanttic Web services topic was already on the decline in 2006.
- The year of ”Linked Data” DBPedia A lot of company use cases that have used SW mentioned:
- Top 10 Companies plot from Sponsor Dictionary Can look over companies per year FORM_
- Top 5 OR 10 Domains plot from conference/journal dictionary Here healthcare seems unimportant…
- hmm, I think that I don't have enough data for that as of yet ;-) … but that also gives me a chance to just throw in my own personal tast preferences!!! What I believe to be hot topics/ the (no-brainers, if you want...) * Move into messier data, don’t focus on just structured data and non-structured data ... I think there is a niche! Combining Open and Closed Data Data Scecurity and Privacy Archiving and storage temporal data, efficient indexing and efficient updates “Andreas Braun (CIO Allianz): Hadoop to be faded out, privacy by design and by default top agenda item”