1. Linked Data at present Using Linked Data Ferdowsi University of Mashhad Web Technology Lab. (WTLab), www.wtlab.um.ac.ir Linked Data Group (LDG) Mahboubeh Dadkhah May 11, 2011

2. You may know the Linked Data

3. History Linked Data Design Issues by TimBL July 2006 Linked Open Data Project WWW2007 First LOD Cloud May 2007 1 st Linked Data on the Web Workshop WWW2008 1 st Triplification Challenge 2008 How to Publish Linked Data Tutorial ISWC2008 BBC publishes Linked Data 2008 2 nd Linked Data on the Web Workshop WWW2009 NY Times announcement SemTech2009 - ISWC09 1 st Linked Data-a-thon ISWC2009 1 st How to Consume Linked Data Tutorial ISWC2009 Data.gov.uk publishes Linked Data 2010 2 st How to Consume Linked Data Tutorial WWW2010 1 st International Workshop on Consuming Linked Data COLD2010 …

4. May 2007

6. Cloud statistics

7. Now that the Linked Data is here What to do next? Let’s Make Use of It

8. Linked Data Before using we should be sure that we understand the meaning. What was the problem: Searching and Finding Search for Football Players who went to the University of Texas at Austin, played for the Dallas Cowboys as Cornerback

10. Current Web = internet + links + docs Why cant we find it?

11. So, what to do? Make it easy for computers/software to find THINGS Publish Thing As data In a standardized way: RDF RDF data is serialized in different ways: – RDF/XML, RDFa, N3, Turtle, JSON

12. http://…/i sbn978 Programming the Semantic Web 978-0-596-15381-6 Toby Segaran http://…/p ublisher1 O’Reilly title name author publisher isbn http://…/i sbn978 sameAs http://…/ review1 Awesome Book http://…/ reviewer Juan Sequeda http://juanse queda.com/id hasReview hasReviewer description name sameAs livesIn Juan Sequeda name http://dbpedia.org/Austin

13. 2009’s Top 10 Linked Data Research Issues Data Linking and Fusion – linking algorithms and heuristics, identity resolution – Web data integration and data fusion – evaluating quality and trustworthiness of Linked Data Linked Data Application Architectures – crawling, caching and querying Linked Data on the Web; optimizations, performance – Linked Data browsers, search engines – applications that exploit distributed Web datasets Data Publishing – tools for publishing large data sources as Linked Data on the Web (e.g. relational databases, XML repositories) – embedding data into classic Web documents (e.g. GRDDL, RDFa, Microformats) – licensing and provenance tracking issues in Linked Data publishing – business models for Linked Data publishing and consumption

14. 2010’s Top 10 Linked Data Research Issues Linked Data Application Architectures – crawling, caching and querying Linked Data – dataset dynamics and synchronization – Linked Data mining Data Linking and Data Fusion – linking algorithms and heuristics, identity resolution – Web data integration and data fusion – link maintanance – performance of linking infrastructures/algorithms on Web data Quality, Trust and Provenance in Linked Data – tracking provenance and usage of Linked Data – evaluating quality and trustworthiness of Linked Data – profiling of Linked Data sources User Interfaces for the Web of Data – approaches to visualizing and interacting with distributed Web data – Linked Data browsers and search engines Data Publishing – tools for publishing large data sources as Linked Data on the Web (e.g. relational databases, XML repositories) – embedding data into classic Web documents (e.g. RDFa, Microformats) – describing data on the Web (e.g. voiD, semantic site maps) – licensing issues in Linked Data publishing

15. 2011’s Top 10 Linked Data Research Issues Foundations of Linked Data – Web architecture and dataspace theory – dataset dynamics and synchronisation – analyzing and profiling the Web of Data Data Linking and Fusion – entity consolidation and linking algorithms – Web-based data integration and data fusion – performance and scalability of integration architectures Write-enabling the Web of Data – access authentication mechanisms for Linked Datasets (WebID, etc.) – authorisation mechanisms for Linked Datasets (WebACL, etc.) – enabling write-access to legacy data sources (Google APIs, Flickr API, etc.) Data Publishing – publishing legacy data sources as Linked Data on the Web – cost-benefits of the 5 star LOD plan Data Usage – tracking provenance of Linked Data – evaluating quality and trustworthiness of Linked Data – licensing issues in Linked Data publishing – distributed query of Linked Data – RDF-to-X, turning RDF to legacy data Interacting with the Web of Data – approaches to visualising Linked Data – interacting with distributed Web data – Linked Data browsers, indexers and search engines

16. Linked Data makes the web appear as ONE GIANT HUGE GLOBAL DATABASE!

17. Do you remember Search and Find ?

18. SPARQL Endpoints Linked Data sources usually provide a SPARQL endpoint for their dataset(s) SPARQL endpoint: SPARQL query processing service that supports the SPARQL protocol* Send your SPARQL query, receive the result * http://www.w3.org/TR/rdf-sparql-protocol/ Query Linked Data with

19. http://www.w3.org/wiki/SparqlEndpoints

20. http://labs.mondeca.com/sparqlEndpointsStatus/

21. SPARQL queries over multiple datasets How to do this? 1.Issue follow-up queries to different endpoints 2.Querying a central collection of datasets 3.Build store with copies of relevant datasets 4.Use query federation system

22. 1- Follow-up Queries Idea: issue follow-up queries over other datasets based on results from previous queries Substituting placeholders in query templates

23. String s1 = "http://cb.semsol.org/sparql"; String s2 = "http://dbpedia.org/sparql"; String qTmpl = "SELECT ?c WHERE{ rdfs:comment ?c }"; String q1 = "SELECT ?s WHERE {..."; QueryExecution e1 = QueryExecutionFactory.sparqlService(s1,q1); ResultSet results1 = e1.execSelect(); while ( results1.hasNext() ) { QuerySolution s1 = results.nextSolution(); String q2 = String.format( qTmpl, s1.getResource("s"),getURI() ); QueryExecution e2= QueryExecutionFactory.sparqlService(s2,q2); ResultSet results2 = e2.execSelect(); while ( results2.hasNext() ) { //... } e2.close(); } e1.close(); Find a list of companies Filtered by some criteria and return DbpediaURIs from them

24. 1- Follow-up Queries Advantage – Queried data is up-to-date × Drawbacks – Requires the existence of a SPARQL endpoint for each dataset – Requires program logic – Very inefficient

25. 2- Querying a Collection of Datasets Idea: Use an existing SPARQL endpoint that provides access to a set of copies of relevant datasets Example: – SPARQL endpoint over a majority of datasets from the LOD cloud at: http://lod.openlinksw.com/sparql http://uberblic.org

26. (Linked) Data Marketplaces FactForge – Integrates some of the most central LOD datasets – General-purpose information(not specific to a domain) – 1.2billion explicit and 1 billion inferred statements – The largest upper-level knowledge base – http://www.FactForge.net http://www.FactForge.net LinkedLifeData – 25 of the most popular life-science datasets – 2.7billion explicit and 1.4 billion inferred statements – http://www.LinkedLifeData.com http://www.LinkedLifeData.com

28. 2- Querying a Collection of Datasets Advantage – No need for specific program logic × Drawbacks – Queried data might be out of date – Not all relevant datasets in the collection

29. 3- Own Store of Dataset Copies Idea: Build your own store with copies of relevant datasets and query it Possible stores: – Jena TDB http://jena.hpl.hp.com/wiki/TDBhttp://jena.hpl.hp.com/wiki/TDB – Sesame http://www.openrdf.org/http://www.openrdf.org/ – OpenLink Virtuoso http://virtuoso.openlinksw.com/http://virtuoso.openlinksw.com/ – 4store http://4store.org/http://4store.org/ – AllegroGraphhttp://www.franz.com/agraph/http://www.franz.com/agraph/ – etc.

30. 3- Own Store of Dataset Copies Advantages – No need for specific program logic – Can include all datasets – Independent of the existence, availability, and efficiency of SPARQL endpoints × Drawbacks – Requires effort to set up and to operate the store – Ideally, data sources provide RDF dumps; if not? – How to keep the copies in sync with the originals? – Queried data might be out of date

31. 4- Federated Query Processing Idea: Querying a mediator which distributes sub-queries to relevant sources and integrates the results

32. 4- Federated Query Processing DARQ (Distributed ARQ) – http://darq.sourceforge.net/ http://darq.sourceforge.net/ – Query engine for federated SPARQL queries – Extension of ARQ (query engine for Jena) – Last update: June 28, 2006 Semantic Web Integrator and Query Engine(SemWIQ) – http://semwiq.sourceforge.net/ http://semwiq.sourceforge.net/ – Actively maintained!

33. 4- Federated Query Processing Advantages – No need for specific program logic – Queried data is up to date × Drawbacks – Requires the existence of a SPARQL endpoint for each dataset – Requires effort to set up and configure the mediator

34. In any case You have to know the relevant data sources – When developing the app using follow-up queries – When selecting an existing SPARQL endpoint over a collection of dataset copies – When setting up your own store with a collection of dataset copies – When configuring your query federation system You restrict yourself to the selected sources Automated Link Traversal Idea: Discover further data by looking up relevant URIs in your application Can be combined with the previous approaches

35. Link Traversal Based Query Execution Applies the idea of automated link traversal to the execution of SPARQL queries Idea: – Intertwine query evaluation with traversal of RDF links – Discover data that might contribute to query results during query execution Alternately: – Evaluate parts of the query – Look up URIs in intermediate solutions

36. Link Traversal Based Query Execution

46. Advantages – No need to know all data sources in advance – No need for specific programming logic – Queried data is up to date – Does not depend on the existence of SPARQL endpoints provided by the data sources × Drawbacks – Not as fast as a centralized collection of copies – Unsuitable for some queries – Results might be incomplete (do we care?)

47. Implementations Semantic Web Client library (SWClLib) for Java http://www4.wiwiss.fu-berlin.de/bizer/ng4j/semwebclient/ SWIC for Prolog http://moustaki.org/swic/ SQUIN http://squin.orghttp://squin.org – Provides SWClLib functionality as a Web service – Accessible like a SPARQL endpoint

48. Real World Example

49. What is a Linked Data application? Software system that makes use of data on the web from multiple datasets and that benefits from links between the datasets

50. Characteristics of Linked Data Applications Consume data that is published on the web following the Linked Data principles: an application should be able to request, retrieve and process the accessed data. Discover further information by following the links between different data sources: the fourth principle enables this. Combine the consumed linked data with data from sources (not necessarily Linked Data). Expose the combined data back to the web following the Linked Data principles. Offer value to end-users.

51. 1st Linked Data-a-thon co-located at 8th International Semantic Web Conference (ISWC 2009)8th International Semantic Web Conference (ISWC 2009) The overall goal of this event was to – Create a Linked Data application that shows innovative and new functionality – Show that a "quick and dirty" Linked Data application can be developed in 3 days Winners United States Linked Data Overlay – Use Linked Data about geographical locations and display it on Google Earth. www.diversity-search.info – Web and Image search engine augmented with Linked Data – Pictures of David Beckham playing football in the different clubs he has played for Find traditional Chinese medicine as an alternative to western drugs iGoogr: Imagine Google was using Good Relations vocabulary for e‐commerce

52. Self-Service Development of Linked Data Applications Semantic cloud computing the Information Workbench as a self-service platform for the fast development of domain-specific Linked Data solutions Designed with the goal to leverage Linked Data deployment in the enterprise implements concepts and features for data integration, interactive visualization, exploration and analytics, as well as the collaborative acquisition and authoring of Linked Data Data sources can be dynamically integrated at the click of a button the user interface can be flexibly customized based on a large, extensible collection of widgets supporting data visualization, exploration, and collaboration

53. Self-Service Development of Linked Data Applications Platform for Linked Data Application Development – Base functionality to build applications without any programming – SDK for easy extensions – Available in Open Source at http://iwb.fluidops.com/ Covering the entire lifecycle of interacting with Linked Data – Discovery of data sources – Integration of data sources – Visualization – Search and Exploration – Collaborative generation of data Targeted at – Linked Open Data, Linked Government Data – Linked Enterprise Data – Combinations thereof

55. Still remember Search and Find ?

56. challenges discovering relevant data sources discovering useful vocabularies The query Data Quality Finding more/useful Links

58. Challenges (COLD 2010) Web scale data management (indexing, crawling, etc.) Query processing over multiple linked datasets Search in the Web of Data Auto-discovery – of URIs, – of additional data that is not from the authoritative source of a URI, – of relevant linked datasets in general Caching and replication Dataset dynamics – processing change notifications, – keeping consistency, – temporal tracking of linked datasets Reasoning on Linked Data from multiple sources Knowledge discovery deriving insights from the Web of Data Information quality of Linked Data – information quality assessment, – trustworthiness, – provenance User interface research for the interaction with the Web of Data – user interaction and usability, – visualizing Linked Data, – natural language interfaces

59. Any Opinion! Any Question! Thank You