KESW2012 Linked Data for Enterprises and Governments (5 Oct 2012)
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The document summarizes a presentation on Linked Data given at KESW school on October 5, 2012 in St. Petersburg. The morning session included talks on the evolution of Linked Open Data, its current status and challenges, and the LOD lifecycle. The afternoon session covered using Linked Data for enterprises and included case studies. Hands-on sessions on working with LOD were also part of the agenda. The objectives of the presentation were to understand the basic concepts of LOD, how to publish and consume LOD, relevant tools and use cases, and benefits and limitations.
KESW2012 Linked Data for Enterprises and Governments (5 Oct 2012)
- 1. Linked Data @ KESW school Knowledge Engineering and Semantic Web (KESW), 5 Oct 2012, St-Petersburg Dr Sören Auer „Linked Open Data“ Senior scientist and head of the research group Agile Knowledge Engineering and Semantic Web at University of Leipzig Daniel Hladky, MBA „Enterprise Linked Data“ Researcher at NRU HSE “Semantic Lab”, Deputy Director W3C Russia Office Board member at Ontos, Avicomp Services, Intecor, MatchCode Software
- 2. Agenda (morning) Time Topic Speaker 10:00 Welcome, Intro and Objectives Daniel Essentials and W3C View 10:15 Evolution of LOD Sören Status Quo and Current Challenges 11:30 Break 12:00 LOD Lifecycle Sören 13:30 Lunch-Break © AKSW (LOD2) – NRU HSE / W3C Slide 2
- 3. Agenda (afternoon) Time Topic Speaker 14:30 Linked Data for Enterprises Daniel Use Cases 15:30 Hands-On LOD “Students” 16:00 Break 16:30 Hands-On continuation 17:30 Team presentation of hands-on Wrap-Up Daniel 18:00 End © AKSW (LOD2) – NRU HSE / W3C Slide 3
- 4. Objectives • Understand the building blocks – URI, RDF, RDFa, SPARQL … • Know how to «Publish» and «Consume» Linked Open Data • Tools, use cases and references • Understand benefits and limitations © AKSW (LOD2) – NRU HSE / W3C Slide 4
- 5. The Vision of the new Internet Linked Data realizes the vision of evolving the Web into a global data commons, allowing applications to operate on top of an unbounded set of data sources, via standardised access mechanisms. I expect that Linked Data will enable a significant evolutionary step in leading the Web to its full potential. CC-BY-SA von campuspartybrasil (flickr) © AKSW (LOD2) – NRU HSE / W3C Slide 5
- 6. 5 Stars for Open Data by Tim Berners Lee © AKSW (LOD2) – NRU HSE / W3C Slide 6
- 7. W3C View A new wave of transformations Working Groups (W3C Standards) (http://www.w3.org/standards/semanticweb/data) Just as the Web has transformed - RDF, RDFa, SPARQL, RDB2RDF, OWL, RIF, SKOS everything… …It will transform everything again © AKSW (LOD2) – NRU HSE / W3C Slide 7
- 8. Some statistic HTML/CSS Validation Markup Validation © AKSW (LOD2) – NRU HSE / W3C Slide 8
- 9. The Semantic Web is already there! ~30 bio. triples http://bit.ly/d37p4i © AKSW (LOD2) – NRU HSE / W3C Slide 9
- 10. Put the «L» in front of Open Data Publish Data! •Organise Data! •License Data! •Raw Data now! Use Web-Technologies •Provide an API! • The web is an Ecosystem Use Linked Data! • Networked Data creates Network Effects • Give things an URI! • Lowers Costs of Data • Use RDF for Publishing! Integration • Link your Data to other Data (as well as the data models)! • Provide a Standard-API on top © AKSW (LOD2) – NRU HSE / W3C Slide 10
- 11. Linked Open Data Dr Sören Auer 11
- 12. LOD for Enterprise and Government LINKED ENTERPRISE DATA DANIEL HLADKY HTTP://WWW.W3.ORG/2001/SW/SWEO/PUBLIC/USECASES/ HTTP://WWW.W3.ORG/2012/LDP/WIKI/USE_CASES_AND_REQUIREMENTS © AKSW (LOD2) – NRU HSE / W3C Slide 12
- 13. What are Enterprise Data Legacy (ERP) System CRM System E-Mail (Outlook) Wiki (MediaWiki) CMS System © AKSW (LOD2) – NRU HSE / W3C 13
- 14. Data managed in silos Equipment and assets Own schemas – DB structures Finance Student affairs Institutions, organizations and departments create and store their own data Departments do not effectively share information; they exchange data Data inconsistencies, redundancies, and errors affect business results and increase costs © AKSW (LOD2) – NRU HSE / W3C Slide 14
- 15. Connect the silos Equipment & Assets Enterprise-Wide Reusable Information Finance Student Affairs © AKSW (LOD2) – NRU HSE / W3C Slide 15
- 16. Data Integration by SAP SAP MDM MDM Load master data from multiple transactional systems (SAP & non-SAP) into a single, unified EMPLOYEE PRODUCT SUPPLIER CUSTOMER repository Identify and consolidate similar master data values to eliminate duplicates Enrich master data values centrally for enterprise wide purposes (such as reporting) SAP BI (BW) Integrate data from any SAP or non-SAP data source for analytics or business-transaction processing Extract, transform, and load (ETL) data in batch or real time © AKSW (LOD2) – NRU HSE / W3C Slide 16
- 17. Next generation SAP Real-time Data Platform and “EIM” SAP SAP SAP Big Data SAP SAP Custom Business Business 3rd Party Applications Analytics Mobile Apps Suite Warehouse BI Client SAP NetWeaver (On Premise / Cloud) SAP Real-time Data Platform Open Developer APIs and Protocols Common Landscape Management SAP Sybase SQLA Sybase PowerDesigner 3rd Party DB Scale-Out Common Modeling HADOOP MPP SAP Sybase ASE SAP HANA Platform SAP Sybase IQ SAP Sybase ESP SAP Sybase SAP Data SAP MDG, MDM Replication Server Services SAP Smart Data Services Platform © AKSW (LOD2) – NRU HSE / W3C Slide 17
- 18. Approach using LOD technology (W3C) © AKSW (LOD2) – NRU HSE / W3C Slide 18
- 19. Linked Data in Enterprise Information Integration Ref.: P. Frischmuth et al. © AKSW (LOD2) – NRU HSE / W3C Slide 19
- 20. LED principles (or W3C LOD Cookbook) Publishing Consuming LOD • Analyse Data • Specify use cases • Clean your Data • Evaluate relevant data • Model your Data (Vocab.) sources and data sets • Choose vocabularies • Check licenses • Specify license(s) • Create consumption • Convert to RDF patterns • Link Data to other Data • Manage alignment • Publish and promote • Create Mashup, GUIs, serrvices and applications on top © AKSW (LOD2) – NRU HSE / W3C Slide 20
- 21. LED Best Practice - Vocabularies • Prerequisites Linked Data Vocabs – Terms must be referencable (e.g. via URI) – References have to be unambiguous – Terms have to be mappable (maybe using SKOS) • Vocabularies (co-existence) – UDEF, AGROVOC, folksonomies (del.icio.us), Company Data Dictionaries – Apply SKOS (W3C standard) © AKSW (LOD2) – NRU HSE / W3C Slide 21
- 22. Example of Ontology/Vocab Repository http://ontowiki.net/Projects/OntoWiki http://protege.stanford.edu/ © AKSW (LOD2) – NRU HSE / W3C Slide 22
- 23. LED Best Practice – Data Curation • The Business Need for Curation – Complete, Accurate, Consistent, Provenance, Timeliness • Leads to a process: > Identify data you need > Who will curate it > Define curation process > Define tools, processes needed to support the curation. • How? Which Community approach: – Internal (privat data) – (External) Pre-competitive – External – Crowd-sourcing © AKSW (LOD2) – NRU HSE / W3C Slide 23
- 24. Data Curation Examples • WikiPedia (crowd-sourcing) > DBPedia • NYT Index (Started in 1913) • Print «Index» once a year – What about Online business? © AKSW (LOD2) – NRU HSE / W3C Slide 24
- 25. NYT Index (Online) WorkFlow at NYT (simplified) 1. Editor writes articles 2. Process article using autom. Tagging (rNews) with NLP 3. Publish article online 4. Data curator review tagging and correct manually © AKSW (LOD2) – NRU HSE / W3C Slide 25
- 26. Demo of possible data curation process RDFaCE PlugIn - Various NLP - RDFa in HTML - rNews/schema.org - RDF to EKB/IKB - Data Curation Ontos Framework © AKSW (LOD2) – NRU HSE / W3C Slide 26
- 27. A possible framework (LED) CRM Media- E-Gov Predictive ... Int. News Eco(API) Analysis Apps Eventos – Filter, Categorize, Visualise Scalable Search in Linked Data Manag. Quality & Extraction Coherence Base Technology Knowledge Triple Store Co- Linking Unstructured User-Interface Evolution Scalability Semi- Curation Matching sructured Orchas- Data-Quality Structured tration Sources Linked Docs RDBMS Social Op.Data (Org.Data) (HTML) Networks © AKSW (LOD2) – NRU HSE / W3C Slide 27
- 28. Tool Box (excerpt) • W3C – Guides and charters (http://www.w3.org/standards/semanticweb/data) – Validator suite (http://www.w3.org/QA/Tools/) • LOD2 Technology Stack • Sindice Based on EU FPx Often Open Source • Silk • LIMES • NLP: OntosMiner, OpenCalais, GATE, UIMA • RDF Store: Ontos, Virtuoso, AllegroGraph, 4Store http://www.garshol.priv.no/blog/231.html © AKSW (LOD2) – NRU HSE / W3C Slide 28
- 29. Early adopters LED – USE CASES © AKSW (LOD2) – NRU HSE / W3C Slide 29
- 30. Digital News and Semantics Early adopters of RDF(a), SPARQL etc – NYTIMES, BBC, Guardien, AP etc. © AKSW (LOD2) – NRU HSE / W3C 30
- 31. rNews (vocab/ontology) RDF triple subject – predicat - object http://dev.iptc.org/rNews Intro by Evan Sandhaus/NYT: http://vimeo.com/22891051 © AKSW (LOD2) – NRU HSE / W3C 31
- 32. References to RDF(a) http://www.w3.org/TR/2011/WD-rdfa-primer- 20110419/ http://www.w3.org/TR/rdfa-lite/ http://www.w3.org/TR/rdf-primer/ http://dev.iptc.org/Introduction-To-RDFa © AKSW (LOD2) – NRU HSE / W3C Slide 32
- 33. rNews Guideline Artikel http://dev.iptc.org/rNews-Sample-Story Guideline: http://dev.iptc.org/rNews-10-Implementation- Guide-Introduction Using schema.org (namespace) http://dev.iptc.org/rNews-10-Implementation- Guide-HTML-5-Microdata Using IPTC (namespace) http://dev.iptc.org/Implementation-Guide-HTML- 5-Microdata-in-IPTC-namespace Example http://www.nytimes.com/2012/09/19/world/asia/n ato-curbs-joint-operations-with-afghan- troops.html?_r=3 Validation: http://www.w3.org/RDF/Validator/ http://www.google.com/webmasters/tools/richsnippets © AKSW (LOD2) – NRU HSE / W3C 33
- 35. Why rNews With structured data No structured data By understanding the structured data on a web page, search engines can better present that web page to users. Source: schema.org 2011 rNews markup allows you to describe the content on your site in a machine-understandable way using RDFa. © AKSW (LOD2) – NRU HSE / W3C
- 36. Cash/Ringier © AKSW (LOD2) – NRU HSE / W3C
- 37. Cash Project Objectives • Similarity of articles • Relevancy, Ranking • SEO optimisation • Metadata for MashUp © AKSW (LOD2) – NRU HSE / W3C 37
- 38. RIA Novosti 3 21 4 10 2 10 11 3 9 1 12 16 3 1 14 11 2 1 12 2 17 1 5 © AKSW (LOD2) – NRU HSE / W3C Slide 38
- 39. BBC – Dynamic Semantic Publishing © AKSW (LOD2) – NRU HSE / W3C Slide 39
- 40. More from BBC http://www.w3.org/2001/sw/sweo/public/UseCases/BBC/ http://www.bbc.co.uk/blogs/bbcinternet/2012/07/olympic_data_se rvices_and_the.html © AKSW (LOD2) – NRU HSE / W3C Slide 40
- 41. RDF(a) vs Schema.org by Google, Yahoo, BING, Yandex http://schema.org/docs/schemas.html © AKSW (LOD2) – NRU HSE / W3C Slide 41
- 42. Google Knowledge Graph © AKSW (LOD2) – NRU HSE / W3C Slide 42
- 43. E-Commerce - GoodRelations http://purl.org/goodrelations/ http://www.ebusiness-unibw.org/tools/goodrelations- annotator/ Introduction by Dr M. Hepp from SemTech 2010 http://www.slideshare.net/mhepp/goodrelations-semtech2010-4590918 © AKSW (LOD2) – NRU HSE / W3C Slide 43
- 44. Magento Extension http://www.heppnetz.de/ontologies/goodrelations/v1.html http://www.magentocommerce.com/magento-connect/semantium/extension/2838/semantium_msemanticbasic#overview © AKSW (LOD2) – NRU HSE / W3C Slide 44
- 45. LINKED DATA AT CAR COMPANY Based on http://semantic-web-journal.net/content/linked-data- enterprise-information-integration http://semantic-web-journal.net/sites/default/files/swj300.pdf © AKSW (LOD2) – NRU HSE / W3C Slide 45
- 46. LED at abc (Proof of Concept) • The situation at abc: • 3.000 heterogeneous IT systems • Different units (car, bus, truck etc.) with very different views • No common language • Inability to identify crucial entities (parts, locations etc.) enterprise wide • There is no (can not be a) single Enterprise Information Model • A distributed, iterative, bottom-up integration approach such as Linked Data might be able to help (pay-as-you-go). Equipment & Assets Enterprise-Wide Reusable Information Finance Student Affairs © AKSW (LOD2) – NRU HSE / W3C Slide 46
- 47. Extraction from RDBMS “SPARQLMap – Mapping RDB 2 RDF“ 1.Either resulting RDF knowledge base is materialized in a triple store & 2.subsequently queried using SPARQL 3.or the materialization step is avoided by dynamically mapping an input SPAQRL query into a corresponding SQL query, which renders exactly the same results as the SPARQL query being executed against the materialized RDF dump © AKSW (LOD2) – NRU HSE / W3C Slide 47
- 48. Data.gov / data.gov.uk / W3C LGD Linked Government Data W3C eGovernment Interest Group http://www.w3.org/egov/wiki/Main_Page © AKSW (LOD2) – NRU HSE / W3C Slide 48
- 49. What is Open (Government) Data? Open Government Data is a worldwide movement to open data (& information) of the government / public administration* - that is NOT personal (individual related) – in human- and maschine readable open formats (non proprietary) for use & re use! OPEN stands for lowering the barriers to ensure as broad as possible re-use (for everybody)! There is a new paradigm in publishing Open Government Data = look, take and play! * ….. data and information produced or commissioned by government or government controlled entities © AKSW (LOD2) – NRU HSE / W3C
- 50. What is Important? For Whom? What is important when thinking about open data in use? •Interoperability to ensure broad & easy use & re-use •Human AND machine readable data and meta data •In open formats •For smooth and cost efficient data integration •To generate effects on several levels: local – regional – national – EU wide & worldwide For several target groups with several interests! •Public administration (also for internal use) •Politicians & decision makers •Citizens (Citizen Analysts) •Economy & Industry (data integration, -enrichment, APPs) •(Data) Journalists, media & publishers •Academia & Science © AKSW (LOD2) – NRU HSE / W3C
- 51. Data.gov (Open Data Sets) and Mashups Civic Commons has a great collection of good open use cases: http://civiccommons.org/ © AKSW (LOD2) – NRU HSE / W3C Slide 51
- 52. Where my money goes (Greece) http://publicspending.medialab.ntua.gr/en/#/~/total http://dl.dropbox.com/u/46182458/2012-06-19%20ps.gr%20BRU.pdf © AKSW (LOD2) – NRU HSE / W3C Slide 52
- 53. E.g. Chicago - https://data.cityofchicago.org/ © AKSW (LOD2) – NRU HSE / W3C Slide 53
- 54. 5 Star Pyramid of Open Data http://5stardata.info/ (Dr M. Hausenblas, DERI) See also:Christopher Gutteridge has a Linked Data crash course for programmers. http://openorg.ecs.soton.ac.uk/wiki/Linked_Data_Basics_for_Techies © AKSW (LOD2) – NRU HSE / W3C Slide 54
- 55. Let’s apply our knowledge HANDS-ON © AKSW (LOD2) – NRU HSE / W3C Slide 55
- 56. Example….. https://www.dropbox.com/s/uzulsw3zu9eyff2/LOD_Test.zip © AKSW (LOD2) – NRU HSE / W3C Slide 56
- 57. Wrap-Up: Benefits and Limitations SUMMARY © AKSW (LOD2) – NRU HSE / W3C Slide 57
- 58. Misconceptions about Linked Open Data • All of us have to use ONE schema • Everything needs to be switched to RDF • We all have to learn SPARQL, there are no standard (web) APIs • LOD is a pure academic approach • LOD can only be used by Semantic Web experts • We have to change our data integration & -management approaches © AKSW (LOD2) – NRU HSE / W3C Slide 58
- 59. The Power of Linked Open Data • Enables web-scale data publishing - distributed publication with web- based discovery mechanisms • Everything is a resource – follow your nose to discover more about properties, classes, or codes within a code list • Everything can be annotated - make comments about observations, data series, points on a map • Easy to extend - create new properties as required, no need to plan everything up-front • Easy to merge - slot together RDF graphs, no need to worry about name clashes • Easy use and re-use on top of common schemas AND schema mapping • Allows complex querying of several distributed data sources & systems © AKSW (LOD2) – NRU HSE / W3C Slide 59
- 60. The Benefits of Linked Open Data • Less replication (offering same datasets in different places) • Encouragement to re-use existing datasets • Clear which datasets are providing similar / same information • More innovation because datasets can be put in a new context and lead to interesting applications • Put information in context and thereby create knowledge © AKSW (LOD2) – NRU HSE / W3C Slide 60
- 61. Cost of Data Integration – 2 Approaches Can we afford to mash the data with ours? Source: Price Waterhouse Coopers – Technology Forecast, Spring 2009 © AKSW (LOD2) – NRU HSE / W3C Slide 61
- 62. End of the Day (tomorrow hackathon for Open Gov Data) Q&A © AKSW (LOD2) – NRU HSE / W3C Slide 62