Linked data for Enterprise Data Integration
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The document discusses the evolution of the web from static pages to linked data and its implications for enterprise information integration. It outlines principles for linked data and enterprise data, emphasizing the advantages of reduced integration costs and increased flexibility. Additionally, it examines the challenges and opportunities presented by linked data in managing diverse and complex enterprise knowledge bases.
![Creating Knowledge
out of Interlinked Data
In an uncontrolled
environment as the Data
Web, there will be a
proliferation of equivalent
or similar entity identifiers
Manual Link discovery:
• Sindice integration into UIs
• Semantic Pingback
Semi-automatic:
• SILK
• LIMES
Automatic/ Supervised:
• Raven [1]
Linking Entities on the Data Web
[1] Ngonga, Lehmann, Auer, Höffner: RAVEN -- Active Learning of Link Specifications, OM@ISWC, 2011.](https://image.slidesharecdn.com/linkeddataforenterprisedataintegration-131206083117-phpapp01/85/Linked-data-for-Enterprise-Data-Integration-24-320.jpg)
Linked data for Enterprise Data Integration
- 1. Linked Data for Enterprise Information Integration Sören Auer
- 2. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS The Web evolves into a Web of Data 2 Linked Open Data Facebook Open Graph
- 3. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS The Evolution of the Web 3 Web 1.0 - Hypertext Static Web pages Hyperlinks Link directories Web 2.0 – Social Apps Social Web Crowd-sourcing Mashups Web 3.0 – Linked Data REST APIs, RDF, JSON-LD Vocabularies Rich-snippets, Semantic Search 1990 2000 2010 Intranet 1.0 - Hypertext Static Intranet pages Keyword search Hyperlinks Intranet 2.0 – Social Enterprise Apps Salesforce Crowd-sourcing Mashups Intranet 3.0 – Enterprise Data Intranet URI Scheme Enterprise taxonomies / knowledge bases RDB2RDF Mapping 1995 2005 2015 & Enterprise Intranets
- 4. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Linked Data Principles 1. Use URIs to identify the “things” in your data 2. Use http:// URIs so people (and machines) can look them up on the web 3. When a URI is looked up, return a description of the thing (in RDF format) 4. Include links to related things http://www.w3.org/DesignIssues/LinkedData.html 4
- 5. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Linked Enterprise Data Principles 1. Evolve existing existing taxonomies into enterprise knowledge bases/hubs 2. Establish a enterprise wide URI scheme 3. Equip existing information systems in your intranet with Linked Data interfaces 4. Establish links between related information 5
- 6. © Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS Linked Enterprise Data Advantages • Light-weight linked data integration complements more complex SOA architectures • Unified data (access) model simplifies data integration • Increase standardization while preserving diversity • Facilitate information flows along supply and value creation chains Dramatically reduce data integration costs, increase enterprise flexibility 6
- 7. Creating Knowledge out of Interlinked Data Inter-linking/ Fusing Classifi-cation/ Enrichment Quality Analysis Evolution / Repair Search/ Browsing/ Exploration Extraction Storage/ Querying Manual revision/ authoring Linked Data Lifecycle
- 8. Creating Knowledge out of Interlinked Data Extraction Inter- linking Enrichm ent Quality Analysis Evolution Repair Explora- tion Extrac- tion Store Query Author ing
- 9. Creating Knowledge out of Interlinked Data From unstructured sources • NLP, text mining, annotation From semi-structured sources • DBpedia, LinkedGeoData, DataCube From structured sources • RDB2RDF Extraction
- 10. Creating Knowledge out of Interlinked Data Many different approaches: D2R, Virtuoso RDF Views, Triplify, No agreement on a formal semantics of RDF2RDF mapping • LOD readiness, SPARQL-SQL translation W3C RDB2RDF WG Extraction Relational Data Tool Triplify Sparqlify D2RQ Virtuoso RDF Views Technology Scripting languages (PHP) Java Java Whole middleware solution SPARQL endpoint - X X X Mapping language SQL SPARQL CONSTRUCT Views + SQL RDF based RDF based Mapping generation Manual Semi- automatic Semi- automatic Manual Scalability Medium- high (but no SPARQL) Very high Medium High Malhotra, Auer, Erling, Hausenblas: W3C RDB2RDF Incubator Group Report. W3C RDB2RDF Incubator Group, 2009.
- 11. Creating Knowledge out of Interlinked Data • Rationale: Exploit existing formalisms (SQL, SPARQL Construct) as much as possible • flexible & versatile mapping language • translating one SPARQL query into exactly one efficiently executable SQL query • Solid theoretical formalization based on SPARQL-relational algebra transformations • Extremely scalable through elaborated view candidate selection mechanism • Used to publish 20B triples for LinkedGeoData Sparqlify Stadler, Unbehauen, Auer, Lehmann: Sparqlify – Very Large Scale Linked Data Publication from Relational Databases. Submitted to VLDB-Journal. SPARQL Construct SQL View Bridge
- 12. Creating Knowledge out of Interlinked Data Storage and Querying Inter- linking Enrichm ent Quality Analysis Evolution Repair Explora- tion Extrac- tion Store Query Author ing
- 14. Creating Knowledge out of Interlinked Data 1. Semantic (Text) Wikis • Authoring of semantically annotated texts 2. Semantic Data Wikis • Direct authoring of structured information (i.e. RDF, RDF-Schema, OWL) Two Kinds of Semantic Wikis
- 15. Creating Knowledge out of Interlinked Data The situation at Daimler (€97.76 billion revenue, 250.000 employees): • 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). Can Linked Data help to solve the EII problem in a fortune-500 company?
- 16. Creating Knowledge out of Interlinked Data 16 Search before
- 17. Creating Knowledge out of Interlinked Data
- 18. Creating Knowledge out of Interlinked Data OntoWiki with loaded car model data
- 19. Creating Knowledge out of Interlinked Data Management of Enterprise Taxonomies with OntoWiki Based on the W3C SKOS standard Corporate Language Management at Daimler: 500k concepts in 20 languages
- 20. Creating Knowledge out of Interlinked Data Search after Showing recommondations from the knowledge base integrating car model data and enterprise taxonomy
- 21. Creating Knowledge out of Interlinked Data You can search for „Kombi“ (station wagon) and find T- Models (Daimler term for station waggon)
- 22. FromIntranettoEnterpriseDataWebaroundaknowledgehub Auer, Frischmuth, Klímek, Unbehauen, Holzweißig, Marquardt: Linked Data in Enterprise Information Integration Submitted to Semantic Web Journal 2012.
- 23. Creating Knowledge out of Interlinked Data © CC-BY-NC-ND by ~Dezz~ (residae on flickr) Linking Inter- linking Enrichm ent Quality Analysis Evolution Repair Explora- tion Extrac- tion Store Query Author ing
- 24. Creating Knowledge out of Interlinked Data In an uncontrolled environment as the Data Web, there will be a proliferation of equivalent or similar entity identifiers Manual Link discovery: • Sindice integration into UIs • Semantic Pingback Semi-automatic: • SILK • LIMES Automatic/ Supervised: • Raven [1] Linking Entities on the Data Web [1] Ngonga, Lehmann, Auer, Höffner: RAVEN -- Active Learning of Link Specifications, OM@ISWC, 2011.
- 25. Creating Knowledge out of Interlinked Data Enrichment Inter- linking Enrichm ent Quality Analysis Evolution Repair Explora- tion Extrac- tion Store Query Author ing
- 26. Creating Knowledge out of Interlinked Data Linked Data is mainly instance data!!! ORE (Ontology Repair and Enrichment) tool allows to improve an OWL ontology by fixing inconsistencies & making suggestions for adding further axioms. • Ontology Debugging: OWL reasoning to detect inconsistencies and satisfiable classes + detect the most likely sources for the problems. user can create a repair plan, while maintaining full control. • Ontology Enrichment: uses the DL-Learner framework to suggest definitions & super classes for existing classes in the KB. works if instance data is available for harmonising schema and data. http://aksw.org/Projects/ORE Enrichment & Repair Lehmann, Auer, Tramp: Class Expression Learning for Ontology Engineering. Journal of Web Semantics (JWS), 2011.
- 27. Creating Knowledge out of Interlinked Data Analysis Quality Inter- linking Enrichm ent Quality Analysis Evolution Repair Explora- tion Extrac- tion Store Query Author ing CC BY SA Wikipedia
- 28. Creating Knowledge out of Interlinked Data Quality on the Data Web is varying a lot • Hand crafted or expensively curated knowledge base (e.g. DBLP, UMLS) vs. extracted from text or Web 2.0 sources (DBpedia) Research Challenge • Establish measures for assessing the authority, provenance, reliability of Data Web resources Opportunity for EII: Employ crowd-sourced knowledge from the Data Web in the Enterprise Linked Data Quality Analysis FP7-IP DIACHRON Managing the Evolution and Preservation of the Data Web Started April 2013
- 29. Creating Knowledge out of Interlinked Data Evolution © CC-BY-SA by alasis on flickr) Inter- linking Enrichm ent Quality Analysis Evolution Repair Explora- tion Extrac- tion Store Query Author ing
- 30. Creating Knowledge out of Interlinked Data Exploration Inter- linking Enrichm ent Quality Analysis Evolution Repair Explora- tion Extrac- tion Store Query Author ing
- 31. Creating Knowledge out of Interlinked Data An ecosystem of LOD visualizations LODExploration Widgets Spatial faceted- browsing Faceted- browsing Statistical visualization Entity-/faceted- Based browsing Domain specific visualizations … … LODDatasetsChoreography layer • Dataset analysis (size, vocabularies, property histograms etc.) • Selection of suitable visualization widgets Brunetti, Auer, García: The Linked Data Visualization Model. To appear in IJSWIS, 2012.
- 32. Creating Knowledge out of Interlinked Data LOD Life-(Washing-)cycle supported by Debian based LOD2 Stack http://stack.lod2.eu
- 33. Creating Knowledge out of Interlinked Data Linked Enterprise Intra Data Webs fill the gap between Intra-/Extranets and EIS/ERP Unstructured Information Management Structured Information Management Support the long tail of enterprise information domains • Human-resources • Requirements engineering • Supply-chains
- 34. Creating Knowledge out of Interlinked Data • Linked Data is a promising technology for closing the gap between SOA and unstructured information management • wealth of knowledge available as LOD can be leveraged as background knowledge for Enterprise applications • The application of Linked Data in the enterprise is still largely unexplored (opportunity) • Linked Data will make Enterprise Information Integration more flexible, iterative, cost effective Take home messages Auer, Frischmuth, Klímek, Tramp, Unbehauen, Holzweißig, Marquardt: Linked Data in Enterprise Information Integration Submitted to Semantic Web Journal.
- 35. Creating Knowledge out of Interlinked Data Thanks for your attention! Sören Auer http://www.informatik.uni-leipzig.de/~auer | http://aksw.org | http://lod2.org auer@cs.uni-bonn.de