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Linked Enterprise Data

Linked Enterprise Data. Leveraging the Semantic Web stack in a corporate environment ISWC 2012 – Boston Fabrice LACROIX – lacroix@antidot.net. Antidot – who we are. French-based Software Vendor Since 1999 | Paris, Lyon, Aix-en-Provence Information access | Data management

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Linked Enterprise Data

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  1. Linked Enterprise Data Leveraging the Semantic Web stackin a corporate environment ISWC 2012 – Boston Fabrice LACROIX – lacroix@antidot.net

  2. Antidot – who we are • French-based Software Vendor • Since 1999 | Paris, Lyon, Aix-en-Provence • Information access | Data management • Mission: Provide our customers with innovative customizable solutions that help them create value with their data, and make their employees more aware and efficient.

  3. Clients Enterprises Publishing E-commerce Healthcare

  4. Unstructured documents • files, ECM, collaborative spaces • intranet, extranet, Web sites • e-mails, instant messaging

  5. Structured data • CRM, ERP, directory • knowledge bases • business applications (production, support)

  6. IS are bloated • 1 practice => 1 need => 1 application => 1 silo • Information system is driven by the process • Data are numerous, various and scattered

  7. Solutions or workarounds? BI MDM SOA Search

  8. Solutions and workarounds • Enterprise Search brings little value to users • Document oriented • Does not solve real business problems Google like Verity like

  9. What we want

  10. What we want ERP CRM Production LDAP ECM Support Files

  11. Changing the paradigm • Switching from an application view to a data centric way of thinking.

  12. Bring out the implicit • Build the Giant Enterprise Graph

  13. LED • Linked Enterprise Data application of the Semantic Web technologies and Linked Data principles to the enterprise infrastructure

  14. What works for the Web… • Federating silos on the Web http://www.w3.org/People/Ivan/CorePresentations/RDFTutorial/Slides.html#(102)

  15. …can’t always be used • in corporate IS • Legacy apps can’t be "Sparql’ed" • 80% un- or semi- structured data don’t fit in the model as such • Defining vocabularies/ontologies for silos is too complex and expensive • Don’t want RDF per se but valuable information • External data is available in XML/JSON through Web Services • Staff trained for RDB, XML, Web apps. • No Risk and stability strategy: SemWeb technology considered as new and immature

  16. The RDF/storage approach • Setting up a global RDF repository does not work either • ITs are afraid by the "RDF everywhere" activists

  17. Semantic Web technology still is the right solution in corporate environment BUT it is not an aim JUST use it as a means

  18. Just do it • Think of it as a stream paradigm • build new objects using existing data • without interfering with the existing infrastructure • with SemWeb somewhere under the hood

  19. Enterprise Graph HowTo • Construct the graph • generate triples from data • create triples from documents • Leverage the graph • enrich • infer • Browse the graph • select resources • build objects • Trash the graph

  20. How: extract & normalize • Harvest and normalize • as in an ETL • fetch, clean, transform… • normalize records (names, IDs) to prepare the linking step • For databases • db2triples : an RDB2RDF implementation by Antidot (open source, W3C validated)

  21. How: semantize • Don’t transform everything in RDF • cherry-pick a subset of interesting fields for each object and create their RDF triples counterpart • interesting == needed for linking or inferring Semantize

  22. How: semantize • Triples generation • Be smart: avoid upfront ontology design, use small vocabularies • Be pragmatic: transform XML tags and field names to predicates • Be agile: only insert what you need. And when you need more, add more. • Semantic Web fuels the modeling, linking and information building process

  23. Enterprise Graph HowTo • Construct the graph • generate triples from data • create triples from documents • Leverage the graph • enrich • infer • Browse the graph • select resources • build objects • Trash the graph

  24. How: semantize • Unstructured documents • Extract metadata and transform them as needed to RDF. • Ex: author => dc:creator • Use of text-mining to extract named entities: people, organizations, products… • generate those entities list using the data sources: directory for employees, CRM for companies and people, ERP for products • create triples like doc_URI quotes entity_URI

  25. How: semantize • Unstructured documents • Compare documents using various and dedicated algorithms • is the same • is included • is similar • is related • Generates new triples • create triples like <docA> is_sub_version_of <docB>

  26. Enterprise Graph HowTo • Construct the graph • generate triples from data • create triples from documents • Leverage the graph • enrich • infer • Browse the graph • select resources • build objects • Trash the graph

  27. How: enrich • Enrich the graph • run specific algorithms to generate more links and triples (classifiers, topic detection, …) • insert external data gathered from the LOD or other external datasets or APIs

  28. How: infer • Create new knowledge • add rules according to your needs IF a coworker is quoted in documents AND this coworker belongs to a business unit THEN the business unit is bound to the documents

  29. Enterprise Graph HowTo • Construct the graph • generate triples from data • create triples from documents • Leverage the graph • enrich • infer • Browse the graph • select resources • build objects • Trash the graph

  30. How: build • Build • select resources corresponding to objects seeds (using Sparql queries) • for each seed, follow links smartly in order to create basic objects Build

  31. How: build • Finalize • decorate the new knowledge objects with data set apart (not loaded in the triplestore) • now we have rich user-actionable objects Build Finalize

  32. Enterprise Graph HowTo • Construct the graph • generate triples from data • create triples from documents • Leverage the graph • enrich • infer • Browse the graph • select resources • build objects • Trash the graph

  33. How: expose • Make the new information available to users and to the entire IS Enrich Semantize Harvest Relational DB RDF Triplestore (Linked Data) Normalize Classify Annotate AFS search engine Indexation

  34. Conclusion • It works! • The triples we create and the inference rules we add are dictated by the goal / application • usage and value oriented • We benefit from the lazy-flexible-dynamic modeling of RDF-RDFS-OWL • we are agile • What matters is the graph. But the graph is not the triplestore • storage independent

  35. There’s an app for that • Antidot Information Factory • a software solution designed specificallyto leverage structured and unstructured data • enable large-scale processing of existing data • automate publishing of enriched or newly created information. Harvest Normalize Semantize Enrich Build Expose

  36. The Giant Enterprise Graph • Now we have a path to let SemWeb enter the enterprise

  37. Discuss Understand Learn Exchange www.antidot.net info@antidot.net Thanks for your attention QUESTIONS?

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