The Web of Data: The W3C Semantic Web Initiative


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From the Feb 19 2014 NISO Virtual Conference: The Semantic Web Coming of Age: Technologies and Implementations
The Web of Data - 
Ralph Swick, Domain Lead of the Information and Knowledge Domain at W3C

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The Web of Data: The W3C Semantic Web Initiative

  1. 1. The Web of Data NISO Virtual Conference 19 February 2014 Ralph Swick, W3C
  2. 2. Agenda • Data is changing our lives • W3C’s traditional focus • Expanding scope of W3C’s data activities
  3. 3. Web has transformed our relation to computers and to data • A computer in every pocket • Apps leveraging context – geolocation and other sensors – social context (“I’m at the conference, too!”) • Change in the use of search – people search for answers, not sites – answers from aggregated data (Siri, Google Now, Wolfram Alpha)
  4. 4. Apps are using data from many sources • • • • Social networking Mobile devices Sensors Open data
  5. 5. Imagine… • A “Web” where – documents are available for download on the Internet – but there would be no hyperlinks among them
  6. 6. Data on the Web is not enough… • We need a proper infrastructure for a real Web of Data where: – data are available on the Web • accessible via standard Web technologies – data are interlinked over the Web – data can be integrated over the Web • This is Linked Data
  7. 7. Agenda • Data is changing our lives • W3C’s traditional focus • Expanding scope of W3C’s data activities
  8. 8. Semantic Web Core • • • • • • • • • • RDF RDF Schema RDB2RDF SPARQL SKOS OWL RIF LDP POWDER GRDDL data model vocabulary design relational DB export query vocabulary description ontological inference rules interchange read-write Web of Data description resources app-specific XML
  9. 9. Need for RDF schemas • First step towards the “extra knowledge”: – define the terms we can use – what restrictions apply – what extra relationships are there? • “RDF Vocabulary Description Language” – the term “Schema” is retained for historical reasons…
  10. 10. Vocabularies • There is a need for “languages” to define such vocabularies – to define those vocabularies – to assign clear “semantics” on how new relationships can be deduced
  11. 11. SKOS • SKOS provides a simple bridge between the “print world” and the (Semantic) Web • Thesauri, glossaries, etc., from the library community can be made available • SKOS can also be used to organize, e.g., tags, annotate other vocabularies, …
  12. 12. Semantic Web/Linked Data Today • Standards are mature – some level of maintenance work is always needed • Server-side applications dominate • Commercial applications exist, e.g.: – direct integration/usage of linked data on the Web – consumption of other formats converted internally to a common format (RDF)
  13. 13. Challenge: leverage data in interoperable apps • Public, private, behind enterprise firewalls • From informal to highly curated • From machine readable to human readable – HTML tables, twitter feeds, local vocabularies, spreadsheets, … • Expressed in diverse data models – tree, graph, table, … • Serialized in many ways – XML, CSV, RDF, PDF, JSON, HTML Tables,…
  14. 14. The Linking Open Data Project
  15. 15. Linked Data Principles Is your data 5 Star? Available on the Web in some format (i.e., use URI to access the data) Available as machine-readable structured data (e.g., excel instead of an image scan) As before, but using a non-proprietary format (e.g., CSV instead of excel) All the above, plus use open standards (RDF & Co.) to identify things, so that people could point at your stuff All the above, plus link your data to other people’s data to provide context
  16. 16. A Three Star Example
  17. 17. The importance of Linked Data • Provide a core set of data that applications can build on – stable references for “things”, • e.g., – many many relationships that applications may reuse – a “nucleus” for a larger, semantically enabled Web!
  18. 18. Linked Data Platform (LDP) • Define an HTTP/RESTful based infrastructure to publish, read, write, or modify linked data – typical usage: data intensive application in a browser, application integration using shared data… • The infrastructure should be easy to implement and install – provides an “entry point” for Linked Data applications! • The work is nearing completion
  19. 19. RDF with HTML: RDFa • By adding some “meta” information, the same source can be reused – typical example: your personal information, like address, should be readable for humans and processable by machines • Some solutions have emerged: – add extra statements in microdata or RDFa that can be converted to RDF • microdata can be used for a (useful) subset of RDF • RDFa is, essentially, a complete serialization of RDF
  20. 20. • is a cooperation of search engines (Bing, Google, Yahoo!, and Yandex) • It is a large vocabulary that they all understand • The terms are extracted from HTML5+microdata or HTML5+RDFa – the various partners use it for different purposes – it can be used by anyone outside of the search world!
  21. 21. Some things to remember when you publish data • Publish your data first, do user interfaces later! – the “raw data” can become useful on its own right and others may use it – you can add your added value later by providing nice user access • If possible, publish your data in RDF but if you cannot, others may help you in conversions – trust the community… • Add links to other data. “Just” publishing isn’t enough…
  22. 22. Some things to remember when you publish data (2) • Think about persistence and versioning – others may depend on the data you publish… • Be thoughtful about the URIs you choose • Try to avoid reinventing the wheel when choosing vocabularies
  23. 23. Some things to remember when you publish data (3) • Document your data, i.e., provide metadata – there are vocabularies to do this • • • • Data Catalog Vocabulary (DCAT) Vocabulary of Interlinked Datasets (VoID) DCTERMS vocabularies for licensing (Open Data Commons, government licenses) – this area is still very much in development…
  24. 24. Agenda • Data is changing our lives • W3C’s work on data integration • Expanding scope of W3C’s data activities
  25. 25. New work underway • CSV on the Web • Data on the Web Best Practices • Vocabulary management
  26. 26. What we are hearing • CSV is everywhere – can be huge data sets, not easily readable in a spreadsheet or Google refine – meaning of data not in machine-readable form – data is not necessarily used for web-scale integration but rather immediate usage • Metadata is essential • Conversion is an issue • European Commission Study on business models for Linked Open Government Data (BM4LOGD)
  27. 27. Linked Data Benefits (BM4LOD) • Flexible data integration – Streamlined internal processes – Where working relationships already exist, much easier to share – Linking reference collections; discovery of new relationships • Increase in data quality – More use of data internally brings errors to light – Use of open standards increases quality of system • New services • Cost reduction – Increased efficiency – Increase in data usage due to LOD enrichment
  28. 28. CSV on the Web • How W3C can help – metadata vocabulary to describe CSV data (structure, reference to access rights, annotations, etc.) – metadata discovery (e.g., part of an HTTP header, special rows and columns, packaging formats…) – mapping content to RDF, JSON, XML
  29. 29. Best practices • Document best practices for the data publishers – URI design, management of persistence, versioning – business models – use of core metadata vocabularies (provenance, access control, ownership) • Specific vocabularies – quality, application descriptions, …
  30. 30. Vocabulary management: challenge • Interoperable vocabularies are key for (meta)data • At the moment, it is a fairly chaotic world… – many, possibly overlapping vocabularies – difficult to locate the one that is needed – vocabularies may not be properly managed, maintained, versioned, provided persistence…
  31. 31. Vocabulary management: how W3C can help • Provide a space where – communities can develop vocabularies (through, e.g., CGs, possibly WGs) – host vocabularies at W3C if requested – annotate vocabularies with a proper set of metadata terms – establish a vocabulary directory • The exact structure is still being discussed
  32. 32. Summary • Data-driven smart apps are one of the major growth engines for the worldwide software market. • We need to meet developers where they are. • 5 Star Benefits of LOD – – – – – Greater efficiency, better provision of the task Greater flexibility leads to lower costs for future projects New services, new connections, new discoveries Improved navigation within and between datasets Others can build apps based on your data
  33. 33. Available specifications: Primers, Guides` • Primers: – RDF Primer – OWL Guide – SKOS Primer – GRDDL Primer – RDFa Primer • The W3C Semantic Web Activity Wiki has links to all the specifications
  34. 34. These slides are in the Web at /0219-NISO-RRS with thanks to Ivan Herman, W3C and Phil Archer, W3C