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PROPEL . Austrian's Roadmap for Enterprise Linked Data

  1. 1. Austria's Roadmap for Enterprise Linked Data
  2. 2. 14:45 … Hot Drinks 15:00 … Welcome 15:10 … The Project 15:20 … The Findings 15:35 … Conclusions, Roadmap 15:45 … Guest Presentation: Data Market Austria 16:00 … Get your Book and hand over to Vienna Open Data MeetUp
  3. 3. 3 The Austrian Data Eco System
  4. 4. 4 The Austrian Data Eco System
  5. 5. 5 The Austrian Data Eco System 5
  6. 6. Sabrina Kirrane (WU, Privacy and Sustainable Computing Lab) Julia Neuschmidt (IDC Austria) Mihai Lupu (Researchstudios Austria) Elmar Kiesling (TU, Linked Data Lab) Thomas Thurner (SWC + School of Data)
  7. 7. The PROPEL Project Propelling the Potential of Enterprise Linked Data 15.12.2016
  8. 8. PROPEL 8  Emerging concept for data exchange and integration  Based on standard web technologies  Shifting away from a predominantly academic perspective, we conceive Linked Data as a promising disruptive technology for enterprise data management. Source: blog.backand.com Linked Data
  9. 9. The project goal Survey industry and market needs, technological challenges, and open research questions on the use of Linked Data in a business context.  FFG ICT of the Future 2014/2015  Exploratory study  Project duration Nov 2015 – Dec 2016  Consortium: IDC Austria, Technical University of Vienna, University of Economy Vienna, Semantic Web Company PROPEL 9
  10. 10. Approach PROPEL 10 Which industries are the most likely to adopt LD technologies? What are the key drivers, inhibitors and needs in data management from a demand side perspective?
  11. 11. Approach PROPEL 11 What recommendations are necessary for enterprises, policy makers and researchers in order to propel the adoption of LD in enterprises? What are technological and standardisation opportunities and challenges?
  12. 12. Approach PROPEL 12 Stakeholder Workshop Interviews Survey respondents Comprehensive Literature research Internal workshops www.linked- data.at
  13. 13. Findings: Sectoral Analysis of Linked Data Potential
  14. 14. Sectoral Analysis of LD Potential Goal: • Exploratory sectoral assessment of Linked Data adoption potential • Alignment between Linked Data paradigm and industry characteristics • Broad high-level, theoretical perspective Methods: • Industry classification: NACE rev. 2 top level sections, with selective use of more detailed classes • Extensive literature research • Analysis of statistical data on industry characteristics (R&D intensity, ICT spending,..) • Industry expert interviews • Internal validation survey PROPEL 14
  15. 15. Working Hypotheses Sectoral Characteristics → Adoption 15
  16. 16. Sectoral Characteristics - Results 16
  17. 17. High potential sectors 17 ✅ Highly networked ✅ Strong (potential) impact of ICT-based innovation ✅ Data- and ICT-intense ☑ Global scope ☑ Knowledge-intense ☑ Complex operations ☑ Relatively open ☑ Some uptake of web technologies
  18. 18. Medium potential sectors 18 ✅ Highly networked ✅ ICT- and data-intense ✅ Strong (potential) impact of ICT-based innovation ☑ Highly internationalized ☑ Complex operations ❌ Have not embraced openness ❌ Limited uptake of web technologies
  19. 19. Lower potential sectors PROPEL 19 ✅ Structural characteristics mostly favorable ❌ Moderate ICT dynamics ❌ Have not embraced openness ❌ Trailing web technology uptake
  20. 20. Results  Broad potential for ELD across a large spectrum of industries  Focus on ”openness” and “web-centric positioning” in academic discussions may inhibit enterprise adoption  Virtually all sectors in developed economies exhibit structural characteristics that favor LD adoption: • Actors in a highly networked global economy • Increasingly data-driven and knowledge-intense • Cross-organizational operations  However, various sectors • are laggards in the technological dimensions and • have untapped potential for ICT-based innovation PROPEL 20
  21. 21. Findings: Market Forces
  22. 22. Market Forces PROPEL 22  Economy: • Positive economic development in Austria leads to a growth in IT spending and we expect investments solutions for data and information management  Efficiency: • Organizations focus primarily on costs. Data and information management solutions and LD can have positive effects in terms of transforming businesses, increasing efficiency and driving growth  Digital Transformation: • Data and information management is a key asset for digital transformation, and concepts around Linked Data can support the transformation process
  23. 23. Market Forces PROPEL 23  Culture: • Missing innovation culture in some organisations might be inhibitors for the uptake of new technologies  Data driven networked global economy: • Growing need to break up silos, and to share data across organizational boundaries.  Digital life of citizens: • High Internet adoption and user demands for new digital products and services lead to redefinition and expansion of services.
  24. 24. Market Forces PROPEL 24  Technology: • New technologies like cloud, big data, IoT and cognitive computing/machine learning change the way our data is managed.  Data security and privacy: • Common barriers to adoption of new technology; at the same time security concerns provide an opportunity for solution providers to generate revenue out of their security solutions and services.  Regulations: • General Data Protection Regulation forces organizations to take a fresh look on how they manage their data.
  25. 25. Big efforts for data and information management PROPEL 25 Demand-side analysis
  26. 26. PROPEL 26 Demand-side analysis
  27. 27. PROPEL 27 Demand-side analysis
  28. 28. Findings: Technology
  29. 29. 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 29 Note: Instead of explaining them what ELD is, we gathered their technology/research expectations from a more general SW perspective
  30. 30. Technologies in need… PROPEL 30 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
  31. 31.  Monitoring SW community major venues: • ISWC (since 2006), ESWC (since 2006), SEMANTiCS (since 2007), JWS (since 2006), SWJ (since 2010)  3 seminal papers: PROPEL 31 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Community Analysis
  32. 32. Topic Categorisation PROPEL 32
  33. 33. Semantic Web/Linked Data over time… PROPEL 33 Subtopics: Expressing Meaning Knowledge Representation Ontologies Agents Evolution of Knowledge
  34. 34. Knowledge Representation & Reasoning PROPEL 34
  35. 35. Semantic Web/Linked Data over time… PROPEL 35 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
  36. 36. LD Adopters - Companies PROPEL 36
  37. 37. LD Adopters - Companies PROPEL 37 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
  38. 38. PROPEL 38
  39. 39. Semantic Web/Linked Data over time… PROPEL 39 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”.
  40. 40. Looking to the future PROPEL 40
  41. 41. ROADMAP
  42. 42. Roadmap Formulation 1. Austrian perspective SWOT analysis: 1. Awareness and education 2. Technological Innovation and Research 3. Standardization 4. Legal and Policy 5. Funding 2. Development of prioritized recommendations PROPEL 42
  43. 43. SWOT 43
  44. 44. Know the threats 44
  45. 45. See the weaknesses 45
  46. 46. Build on strength 46
  47. 47. Take the opportunities 47
  48. 48. Activities 54 Long-term Support emerging Linked Enterprise Data ecosystems Establish centers of excellence Position Austria as a hotspot for LED research and innovation Awareness and Education Legal and Policy FundingTechnological Innovation Research Medium-term Develop key foundational technologies Institutional and technological focus on key issues and domains Short-term Cluster stakeholders and efforts Get momentum from new funding lines Supporting studies and pilot projects
  49. 49. Take-home messages PROPEL 55
  50. 50. "Use the power of ELD!"  Many industries are facing disruptive change  Even conservative industries see a need for a "two speed IT"  Linked Data can be both a disruptive force and a means to respond to disruptive change  Key ELD technologies are mature and have been successfully applied in many domains  Linked Data is agile and flexible  ELD is a enabler for product, process and business model innovation! PROPEL 56
  51. 51. "ELD is the backbone for the developing content industry"  Linked Data is particularly relevant for online businesses (media, e-commerce, etc.)  ELD provides a platform to generate and leverage economic network effects typical for these industries  Tools to enrich digital products and make them interchangeable within a broader digital environment PROPEL 57
  52. 52. "We need to align research priorities and practical needs" Continued fundamental basic research necessary, but:  Industry needs should be reflected in applied research agendas  More courage to apply cutting-edge technologies in industry needed! PROPEL 58
  53. 53. "ELD has to convince stakeholders to embrace change"  Technological, behavioural and cultural adoption barriers  New skill sets required To instigate change, ELD must  ..make sense from a business perspective → clear business cases, fast returns, tangible, quantifiable benefits  ..lower entry barriers • by playing well with existing infrastructure • through open source/freemium/experimental models  ..address security, privacy, and compliance concerns PROPEL 59
  54. 54. "Need to support [and subsidize] emerging ELD ecosystems"  Prototypical example of a technology with strong economic network effects  Flagship implementations and pioneering projects are key to furthering the growth of ELD in Austria.  Both financial and infrastructural support are necessary in order to accelerate the development of the sector. Core preparatory steps include: • Base infrastructures (stores, services, data) to build solutions on top • Project related funding PROPEL 61
  55. 55. Julia Neuschmid | jneuschmid@idc.com PROPEL 62 Thank you! www.linked-data.at
  56. 56. Backup
  57. 57. Linked Data in a Nutshell PROPEL Workshop May 10, 2016
  58. 58. Linked Data from 10,000 foot... • Best practices for publishing and connecting structured data on the Web • Goal: Creating a global data space Web of Documents Web of Data
  59. 59. ... and up-close  Graph-based data model that captures statements about things in the world  Subject-predicate-object triples  Use of URIs as globally unique identifiers PROPEL 66 http://example.com/ alice http://xmlns.com/foaf/0.1/knows http://example.com/ bob :alice foaf:knows :bob
  60. 60. Principles Anyone can… • publish data • create URIs • choose or create vocabularies to represent their data • refer to Linked Data published by others Result: • Decentralized data infrastructure (> 650.000 datasets) • Machine-readable, and -discoverable data sets • Bottom-up "pay as you go" data integration PROPEL 67
  61. 61. Key ideas PROPEL 68 Explicit SemanticsWeb of data Graph-based Network effects Global data space Bottom-up Flexible Agile Machine readable Interoperable Ad-hoc integration Linking Decentralized Inference Discovery a b cx y Emergent Open
  62. 62. Evolution of the Linked Data Cloud: 2007 http://lod-cloud.net
  63. 63. Evolution of the Linked Data Cloud: 2008 http://lod-cloud.net
  64. 64. Evolution of the Linked Data Cloud: 2009 http://lod-cloud.net
  65. 65. Evolution of the Linked Data Cloud: 2010 http://lod-cloud.net
  66. 66. Evolution of the Linked Data Cloud: 2011 http://lod-cloud.net
  67. 67. Media Geographic Publications Social Networking Government Cross- Domain Life Sciences User Generated Content Linguistics Evolution of the Linked Data Cloud: 2014 http://lod-cloud.net
  68. 68. ELD and LED Enterprise Linked Data: Internal use of LD technologies within organizations, e.g., • to integrate heterogeneous systems at the data level • for advanced content/knowledge/… management • as a basis for innovative products and services Linked Enterprise Data: • Cross-organizational data integration • Data markets and data ecosystems • Decentralized infrastructure for a networked economy PROPEL 75
  69. 69. What's the difference between Linked Data and... ? PROPEL 76
  70. 70. Linked Data vs. Open Data Overlaps: • Openness is a core principle in the design of LD • Many Linked Data sets published under an open license → Linked Open Data and LD often used interchangeably Key differences: • Linked Data technologies can be used without publishing data – e.g., for internal and external data integration. • Not all open data will ever be linked (the majority will remain in formats such as csv, txt etc.) PROPEL 77
  71. 71. Linked Data vs. “The” Semantic Web Overlaps: • "LD is the Semantic Web done right" (Tim Berners-Lee) • Semantic web is made up of Linked Data. • Linked Data is based on Semantic web standards. Key Differences: • Semantic Web was all about "semantifying" the web, Linked Data is based on web standards (URIs, http), but doesn't center around web pages. • LD is a more pragmatic "bottom-up" approach. • "Linked Data is mainly about publishing structured data in RDF using URIs rather than focusing on the ontological level or inference." M. Hausenblas "Exploiting Linked Data For Building Web Applications" IEEE Internet Computing, 2009
  72. 72. Linked Data vs Big Data Overlaps: • LD as a whole is big ( *) • No rigid up-front (e.g., relational) data model • Big Data technologies (e.g., Hadoop) are used to handle LD • LD can represent knowledge extracted from big unstructured data Key Differences: • Individual linked data sets are typically not "big" per se (e.g., English DBPedia dump currently < 5 GB) • LD is structured and semantically explicit, "big data lakes" are typically neither • Big data based on distributed data infrastructures within an organization (e.g., Hadoop clusters), LD creates a decentralized, globally distributed data infrastructure PROPEL 79 http://lodlaundromat.orgasper2016-05-10
  73. 73. Linked Data vs Knowledge Graphs PROPEL 80 Facebook Open Graph Google's knowledge graph Examples:
  74. 74. Linked Data vs Knowledge Graphs Overlaps: • Knowledge Graphs also represent explicit semantics in a graph-based data model • Both are often used to facilitate semantic search • Knowledge graphs can use open standards (e.g., RDFa) Key differences: • Proprietary (data and technologies), closed "ecosystem" • Tightly integrated with services • Typically not published externally → no way to link to
  75. 75. References Videos:  Tim Berners-Lee: The next Web of open, linked data (16:52)  Linked Data (and the Web of Data)  Manu Sporny: What is Linked Data (12:09)  Michael Hausenblas: Quick Linked Data Intro (3:14)  Annenberg Networks Theory Seminar with Tim-Berners-Lee  Metaweb (now defunct): Words vs entities Tutorial:  Linda Project: Linked Data Primer Articles:  C. Bizer, T. Heath, and T. Berners-Lee. Linked Data - The Story So Far. International Journal on Semantic Web and Information Systems, 5(3):1 – 22, 2009. Books:  T. Pellegrini, H. Sack, and S. Auer, Eds., Linked Enterprise Data. Heidelberg: Springer Berlin, 2014.  Tom Heath, Christian Bizer (2011). Linked Data - Evolving the Web into a Global Data Space. Morgan & Claypool, 2011.  EUCLID Project Consortium (2014). Using Linked Data Effectively.  Hitzler, Rudolph, Krötzsch (2009). Foundations of Semantic Web Technologies. Chapman & Hall/CRC PROPEL 82
  76. 76. Linked Data Principles 1. Use URIs to identify things 2. Use HTTP URIs so that people can look up those names 3. When someone looks up a URI, provide useful information, using the standards (RDF, SPARQL) 4. Include links to other URIs so that they can discover more things DesignIssues:LinkedDatanotes,TimBerners- Lee
  77. 77. The Semantic Web Technology Stack http://bnode.org/blog/2009/07/08/the-semantic-web-not-a-piece-of-cake
  78. 78. Selected Linked Data Standards/Technologies  URIs + HTTP: • Web infrastructure that provides global identifiers for all objects  RDF: • provides a generic graph-based data model for describing things • various serializations  RDFS and OWL • Basis for the definition of vocabularies (i.e., collections of classes and properties) • Expressed in RDF • Facilitates inference (using reasoning engines)  SPARQL: • Graph pattern-based query language (and protocol) for RDF data PROPEL 85
  79. 79. Vocabularies  Many vocabularies beyond those defined in the RDF standard  Collections of defined relationships and classes of resources  Vocabulary definition and reuse is a key semantic web principle Adapted from Euclid learning materials by Barry Norton Best practices: • Terms from well-known vocabularies should be reused wherever possible • New terms should be defined only if you can not find required terms in existing vocabularies • Feel free to mix terms from different vocabularies and to extend the vocabularies with additional terms in your own namespace
  80. 80. Examples of common Vocabularies Vocabulary Description Classes and Relationships Friend-of-a-Friend (FOAF) Vocabulary for describing people. foaf:Person, foaf:Agent, foaf:name, foaf:knows, foaf:member Dublin Core (DC) Defines general metadata attributes. dc:FileFormat, dc:MediaType, dc:creator, dc:description Semantically-Interlinked Online Communities (SIOC) Vocabulary for representing online communities. sioc:Community, sioc:Forum, sioc:Post, sioc:follows, sioc:topic Music Ontology (MO) Provides terms for describing artists, albums and tracks. mo:MusicArtist, mo:MusicGroup, mo:Signal, mo:member, mo:record Simple Knowledge Organization System (SKOS) Vocabulary for representing taxonomies and loosely structured knowledge. skos:Concept, skos:inScheme, skos:definition, skos:example Adapted from Euclid learning materials by Barry Norton
  81. 81. Linked Data from an Application Development Perspective  Data is self-describing (applications can dereference URIs that identify vocabulary terms in order to find their definition)  Use of HTTP as standardized data access mechanism and RDF as a standardized data model simplifies data access compared to Web APIs, which rely on heterogeneous data models and access interfaces  Web of Data is open, i.e., applications do not have to be implemented against a fixed set of data sources, but can discover new data sources at run- time by following RDF links. PROPEL 88

Notes de l'éditeur

  • Projektpartner
  • To connect data from different sources
    With the term enterprise linked data we mean to link data from different sources in a business context within an enterprise; this data can be open or closed for external stakeholders.
    E.g. link different information systems such as enterprise resource planning, customer relationship managment, supply chain management, emails, the web, social media, other sources, etc.
    As a basis for innovative products and services, to increase efficiency and productivity, and to drive business.
  • 1: The characterisation of industries/domains according to defined criteria in order to identify the industries with highest potential for the adoption of the (E)LD concept.
    2: An investigation into the data and information management challenges that industries are currently facing, and their formulation as use cases.
  • 3: Analysis of the Linked Data community, its current research and development activities as well as open challenges in regards to LD technologies and standards.
    4: The development of an integrated roadmap based on the industry, market, and Linked Data community analysis.
  • Target private sector enterprises of differnet sizes, the research community and policy makers
  • In this phase of the project, we deliberately did not look into particular practical use cases in various industries, but aimed to assess the general suspectability of various sectors to the Linked Data paradigm.

    The goal was to characterize different sectors along a set of dimensions that indicate how well their structural and technological profile aligns with characteristics of the Linked Data paradigm.

    In later stages of the project, we then narrowed our analysis to particularly promising application domains and use cases, but in this phase the goal was to take a broad high-level perspective

    Alignment between Linked Data paradigm and industry characteristics:
    - Linked Data creates a semantically explicit global information space and is therefore useful for industries that are highly internationalized, networked, knowledge-intense, data-driven etc.

    Based on such rationales, we developed a set of working hypotheses that I'll explain in a minute

    To develop these working hypotheses, we relied extensively on literature research

    The individual industry characterizations were also mostly informed by desk research, but also through statistical data on industry characteristics (such as R&D intensity, IT spending)

  • Explain rationale:

    → The original goal of Linked Data was to create a global information space -> more useful in industries with geographic dispersion

    The Semantic web has a strong tradition in knowledge representation, and of course industries where knowledge is important should be more susceptible towards adopting technologies like linked data that help them to manage it

    Operational complexity: coordination of many activities, cooperative processes, interactions etc.
    → need for common understanding and a joint infrastructure

    Network: inter-organizational information flows, need to exchange data

    Openness: is a core value in the LOD community; a starting point of this project was that these technologies can be applied in a not fully open environment
    Still, industries that are characterized

    Linked Data characteristics:
    Linking, sharing and reuse
    Flexibility and extensibility

  • Networked:
    - information flows across organizational boundaries
    - need to share and integrate information within and between organizations

    - Within and across industries

    Data infrastructure
  • The overall economic development influences the IT investments in Austria, slightly positive outlook
  • The overall economic development influences the IT investments in Austria, slightly positive outlook
  • For 84% of respondents efforts for data and information mng are rather big or even very big. 8% is overlaoded by their efforts.
  • The biggest challenges in data management are the cooperation between IT-departments and Lines of Business (LOBs), inconsistency in the business terminology, immature technology, and low data quality.
  • Future perspectives for growth are to integrate data from different sources, to create consistency between data and eliminate duplication, and to track communication with customers along different streams and channels (e.g. CRM, e-mail, social media, etc.).
  • From the interviews we derived approximately 60 user stories and mapped them to the 18 Foundations listed earlier


    I selected 4 and added them here, however there are others incase you need them
  • 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

    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
    Robustness, scalability, optimization and performance
    Searching, browsing & exploration
    Security and privacy
    System engineering

  • 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”

    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_
  • We identified a number threats for the development of an Enterprise Linked Data ecosystem in the Austrian environment that we have limited control over.

    LEGAL AND Policy: Inconsistent legal standards across the EU can be a major stumbling block for the development of Linked Data Ecosystems. Here, we are somewhat dependent upon policy-making at the European level.

    STANDARDISATION: standardization bodies, most notably of course the W3C, may focus their efforts elsewhere. So far, enterprise-related topics have not ranked particularly high in the priorities.

    TECHNOLOGICAL INNOVATION: Broader developments in the research domain, such as the long-term demographics of the semantic web research community, i.e., the number of suitably qualified science and engineering students entering postgraduate studies.

    FUNDING: And of course, we as researchers are always afraid of funding cuts and changes in funding priorities .

    In a sense, the main weaknesses of the Austrian Enterprise Linked Data space is that it does not really exist
    This can be attributed to a lack of awareness and education
    As this project shows, there is a community in both academia and industry, but both we as researchers and the industry partners in the project are primarily active internationally
    This is quite natural in the scientific domain and can be considered a strength for companies like SWC whose client base is mostly international, but not having a "home market" to address is also a weakness.

    Other weaknesses include the fact that the relevant industry is not headquartered in Austria.

    In terms of funding, there are high access barriers for funding of research projects

    And difficulty to fund industry-wide action and adoption at an international level

  • Now, we also found that there are significant strenghts.

    AWARENESS: Strengths include the small, but very active community.
    This is reflected in the attendance of meetups such as this one.

    In terms of standardization, Austria is I THINK overproportionally represented in W3C groups and bodies, which we consider another strength.

    In terms of technology and research, I think it is fair to say that we are fairly well positioned and that technological innovation in research and practice is a key strength.

    Funding is of course always an issue to be concerned about, but overall Austria is well-positioned to fund basic and applied research related to Linked Data.
    Applied research also fosters collaborations and knowledge transfer between universities and industry.

    LEGAL AND POLICY: ?????????????????????????????????????????????????????

  • A major opportunity is the know-how concentrated available at universities and research institutions.

    - certification scheme: interoperability of tools, vendors etc. (e.g., SPARQL)

    We also consider the EU general data protection regulation with ist strong implications for Linked Data not just a challenge, but an opportunity for the European data infrastructure market in general and the Austrian market in particular
  • In the short term, we recommend measures to ensure the visibility of Austrian Linked Data initiatives in an international context
  • that are necessary to propel the potential of Linked Data in enterprises.   
  • Foster multidisciplinary teams: to make sure the technical research is informed by societal and business requirements

    To drive technological innovation, develop centers of excellence

    Provide a platform for Austrians to showcase technological results from nationally and internationally funded projects
    Support the setup and development of centers, starting with Linked Data and moving beyond to Big Data Analytics and the Internet of Things
  • Flagship projects on ELD legal clearing centers:
    - demonstrators based on national use cases
    - broaden findings and solutions to a transnational blueprint
    - couple outcomes of the flagship projects with the standardization efforts
  • Ability to respond to disruptive threats

    Agile in terms of data modeling and integration

    proven successful in data and information management

    The redevelopment of companies coupled with disruptive developments, present companies with the opportunity to adopt ELD, facilitating new business narratives, new processes and new products.
  • Given that ELD has its strength as an underpinning technology for new business narratives,

    Community has also been actively involved in the development of tools (ontologies, RDF, NLP) that
  • There is a strong history of fundamental theoretic research in logic and knowledge representation.

    Important, but if we want the technologies we develop to be relevant, we need to align research priorities with practical needs

    More customer involvement in business processes and (open) innovation

    needs to be improved to foster adoption
  • In existing environments
     While, well established industries have their own home grown IT environments, ELD does not have this level of specialisation and thus has to be adapted beforehand.

    need to be overcome. For instance, it took a long time to convince industry to use SQL for data management. To the same

    issues which are still a weakness of ELD. extent there is now resistance against “non-relational” models like Linked Data, where new standards and new skills (like SPARQL) are required.
  • This includes centres of excellence
  • Turning web of documents into a Web of Data (or: web of things in the world, described by data on the Web)

    Create links between data from different sources.

    Traditionally, data published on the Web has been made available as raw dumps in formats such as CSV or XML, or marked up as HTML tables, sacrificing much of its structure and semantics

    In the conventional hypertext Web, the nature of the relationships between two linked documents is implicit

    Huge decentralized knowledge base of machine-accessible data

    Uniquely identifying web objects (documents, images, named-entities, facts, …)
    Enabling the discovery & interlinking of web objects through semantic metadata
    Open access to data
  • RDF (subject-predicate-object triples)
    Subjects and object of a triple are URIs that each identify a resource (object may also be a string literal)
    Predicate specifies how the subject and object are related, also represented by a URI

  • Still good practice to use established terminology
    Term mapping, ontology alignment

    Mixture of using common vocabularies together with data source-specific terms that are connected by mappings as deemed necessary

    → Parallel use of arbitrary (self-describing) vocabularies
    → different URIs for same entities, resolved via sameAs links
  • DBPedia:
    One of the first and most prominent nodes on the LOD cloud
    Community effort to extract structured (“infobox”) information from Wikipedia
    provide SPARQL endpoint to the dataset
    Interlink the Dbpedia dataset with other datasets on the web
  • Certain data sets such as DBPedia or

    Geo Names
    Database of 10+ Mio geographical names in various languages

    serve as linking hubs

    Rather than extracting data from Wikipedia, Wikidata is a user curated source for structured information which is included in Wikipedia

    Includes more than 38 Billion facts expressed as triples (we’ll get to that) from over 650.000 datasets.
  • More pragmatic bottom-up approach

    Ontologies still important for data integration and particular applications, but the idea of agents using ontologies to reason about the global knowledge autonomously had to be given up for now.

    We will discuss all that in more detail, but for now you can think of Linked Data as a lightweight, somewhat pragmatic, bottom-up approach as opposed to the grand vision that the Semantic Web is or was.
  • Basic recipe for publishing and connecting data using the infrastructure of the Web while adhering to its architecture and standards.
  • Semantic Web Stack defined as a layer model by the W3C
    Every layer can access the functionality of the layers below
    Every layer extends the functionality
  • Semantic web: one of the reasons the semantic web didn’t come to be as envisioned was that no global schema exists and people cannot be expected to agree on terms completely

    Linked Data Paradigm:
    - You don’t need to agree on all the terms
    - 'term cherry-picking' approach
    - bottom-up
  • Very widely used..