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Programming the Semantic Web
The document discusses programming techniques for the semantic web including LITEQ, a language for integrating RDF types and queries into programming languages. LITEQ allows programmers to navigate schemas, define types aligned with programming languages, and retrieve typed instances. The document also presents SchemEX, an index for efficiently searching RDF data sources in the linked open data cloud based on their schemas.
Programming the Semantic Web
- 1. Institute for WebScience & Technologies – WeST Programming the Semantic Web Steffen Staab, Thomas Gottron, Stefan Schegelmann & Team
- 2. Steffen Staab 2Programmingthe Semantic Web Linked Open Data – Vision of a Web of Data „Classic― Web Linked documents Web of Data Linked data entities
- 3. Steffen Staab 3Programmingthe Semantic Web „Classic― Web Linked Open Data – Vision of a Web of Data Web of Data ID ID
- 4. Steffen Staab 4Programmingthe Semantic Web LOD – Base technologies IDs: Dereferencable HTTP URIs Data Format: RDF No schema often / rich schema sometimes Links to other data sources foaf:Document „Extracting schema ...― fb:Computer_Scientist dc:creator http://dblp.l3s.de/.../NesterovAM98 http://dblp.l3s.de/.../Serge_Abiteboul rdf:type „Serge Abiteboul― dc:title rdf:type foaf:name http://www.bibsonomy.org/.../Serge+Abiteboul rdfs:seeAlso 1 Statement = 1 Tripel Subject Predicate Object rdf:type = http://www.w3.org/1999/02/22-rdf-syntax-ns#type foaf:Document = http://xmlns.com/foaf/0.1/Document swrc:InProceedingsrdf:type
- 5. Steffen Staab 5Programmingthe Semantic Web LOD Cloud … the Web of Linked Data consisting of more than 30 Billion RDF triples from hundreds of data sources … Gerhard Weikum SIGMOD Blog, 6.3.2013 http://wp.sigmod.org/ Where’s the Data in the Big Data Wave?
- 6. Steffen Staab 6Programmingthe Semantic Web Some „Bubbles― of the LOD Cloud
- 7. Steffen Staab 7Programmingthe Semantic Web Agenda SchemEX Where do I find relevant data? Efficient construction of a schema-level index Application LODatio: Search the LOD cloud Active user support LiteQ – Language integrated types, extensions and queries for RDF graphs Exploring Programming, Typing
- 8. Steffen Staab 8Programmingthe Semantic Web Motivation Design Time Navigation Run Time Access
- 9. Steffen Staab 9Programmingthe Semantic Web Example RDF Graph
- 10. Steffen Staab 10Programmingthe Semantic Web Programmers Tasks 1. Explore and understand the schema of the data source • Find a type that represents dogs 2. Align schema types with programming language type system • From dog RDF data type to dog data type in the host programming language 3. Query for instances and instantiate program data types • Get all dogs that have an owner
- 11. Steffen Staab 11Programmingthe Semantic Web Programmers Tasks vs Our Solution: LITEQ 1. Explore and understand the schema of the data source • Find a type that represents dogs 2. Align schema types with programming language type system • From dog RDF data type to dog data type in the host programming language 3. Query for instances and instantiate program data types • Get all dogs that have an owner 1. Using NPQL (NodePathQueryLanguage) for exploration and definition 2. Type mapping rules for primitive data types 3. Intensional vs Extensional Intensional node path evaluation provides program data types Extensional node path evaluation provides instance data representations
- 12. Steffen Staab 12Programmingthe Semantic Web Navigating to ex:Dog: • Start with rdf:Resource as universal supertype • Use the subtype navigation operator „>― rdf:Resourcerdf:Resource >rdf:Resource > ex:Creaturerdf:Resource > ex:Creature >rdf:Resource > ex:Creature > ex:Dog Use NPQL schema query language for navigation
- 13. Steffen Staab 13Programmingthe Semantic Web Retrieving the ex:dog data type • Start with the node path from previous example • Use the intension method to get data type description Using NPQL to retrieve type descriptions ... > ex:Creature > ex:Dog... > ex:Creature > ex:Dog -> Intension type exDog = member this.exhasOwner :exPerson = member this.exhasName :String = member this.exhasAge :String = . . member this.exTaxNo :Integer =
- 14. Steffen Staab 14Programmingthe Semantic Web Using NPQL to retrieve sets of typed objects Retrieving objects for all ex:dog entities • Start with the node path from previous example • Use the extension method to get the set of typed objects ... > ex:Creature > ex:Dog... > ex:Creature > ex:Dog -> Extension Provides you with the set of objects containing typed objects for all instances of ex:Dog {exHasso}
- 15. Steffen Staab 15Programmingthe Semantic Web Retrieve all dogs with owners • Use the known path to navigate to the dog type • Use the property selection operator ―<-― to restrict the dog data type • Restrict dog data type to dogs with ex:hasOwner property • Use the extension method to retrieve all dog instances with an owner ex:Hasso object Using NPQL to define Instances ... > ex:Dog... > ex:Dog <-... > ex:Dog <- ex:hasOwner... > ex:Dog <- ex:hasOwner -> Extension
- 16. Steffen Staab 16Programmingthe Semantic Web Using LITEQ in Visual Studio • Line 5: define a datacontext object • Line 6: Use the datacontext object to define pet data type •Navigate to pet •Choose ex:hasOwner property
- 17. Steffen Staab 17Programmingthe Semantic Web Using LITEQ to define types type dog =rdfResource > exCreature > exDog → Intension Intensional semantics: type exDog= inherit exCreature hasOwner : exPerson Using LITEQ to define types • The intensional semantic of LITEQ node paths supports data type definition in the host programming language
- 18. Steffen Staab 18Programmingthe Semantic Web Using LITEQ to retrieve objects let dogs =rdfResource > exCreature > exDog → Extension Extensional semantics: {ex:Hasso,…} • The extensional semantic of LITEQ node paths supports query and retrieval of sets of typed objects
- 19. Steffen Staab 19Programmingthe Semantic Web Using LITEQ to define type conditions let payTax(dogWithOwner : exDog←hasOwner) = … Type conditions for function (method) arguments • LITEQ data types in the host programming language can be used to define type condidtions, e.g. in method heads • LITEQ data types are generated in a pre-compile step, they behave like manually implemented types • compile-time and run-time type-checking is supported
- 20. Steffen Staab 20Programmingthe Semantic Web Using Type Condidtions let dogs = rdfResource > exCreature > exDog → Extension let payTax(dogWithOwner : exDog←hasOwner) = … for dog in dogs do match dog with | :? exDog ← hasOwner as dogWithOwner -> payTax dog | _ -> () Scenario: • Get all dogs • Iterate over the set of dogs • Call paytax method for all dogs with owners
- 21. Steffen Staab 21Programmingthe Semantic Web Agenda SchemEX Where do I find relevant data? Efficient construction of a schema-level index Application LODatio: Search the LOD cloud Active user support LiteQ – Language integrated types, extensions and queries for RDF graphs Exploring Programming, Typing
- 22. Steffen Staab 22Programmingthe Semantic Web Searching the LOD cloud??? ? foaf:Document fb:Computer_Scientist dc:creator x swrc:InProceedings SELECT ?x WHERE { ?x rdf:type foaf:Document . ?x rdf:type swrc:InProceedings . ?x dc:creator ?y . ?y rdf:type fb:Computer_Scientist }
- 23. Steffen Staab 23Programmingthe Semantic Web Searching the LOD cloud??? SELECT ?x WHERE { ?x rdf:type foaf:Document . ?x rdf:type swrc:InProceedings . ?x dc:creator ?y . ?y rdf:type fb:Computer_Scientist } Index • ACM • DBLP
- 24. Steffen Staab 24Programmingthe Semantic Web Schema-level index Schema information on LOD Explicit Assigning class types Implicit Modelling attributes Class Entity rdf:type Entity Property Entity 2
- 25. Steffen Staab 25Programmingthe Semantic Web DS1 Schema-level index E1 P1 E2 XYZP2 C1 C2 C3 P1 P2 C1 C2 C3 DS1
- 26. Steffen Staab 26Programmingthe Semantic Web Typecluster Entities with the same Set of types C1 C2 DS1 DS2 DSm Cn... ... TCj
- 27. Steffen Staab 27Programmingthe Semantic Web Typecluster: Example foaf:Document swrc:InProceedings DBLP ACM tc2309
- 28. Steffen Staab 28Programmingthe Semantic Web Bi-Simulation Entities are equivalent, if they refer with the same attributes to equivalent entities Restriction: 1-Bi-Simulation P1 P2 DS1 DS2 DSm Pn... ... BSi
- 29. Steffen Staab 29Programmingthe Semantic Web Bi-Simulation: Example dc:creator BBC DBLP bs2608
- 30. Steffen Staab 30Programmingthe Semantic Web SchemEX: Combination TC and Bi-Simulation Partition of TC based on 1-Bi-Simulation with restrictions on the destination TC C1 C2 Cn... DS1 DS2 DSm ... C45 C2 Cn‗... P1 Pn... EQC EQC DS TCj TCk EQCj BSi SchemaPayload P2
- 31. Steffen Staab 31Programmingthe Semantic Web SchemEX: Example DBLP ... tc2309 tc2101 eqc707 bs2608 foaf:Document swrc:InProceedings fb:Computer_Scientist dc:creator SELECT ?x WHERE { ?x rdf:type foaf:Document . ?x rdf:type swrc:InProceedi ?x dc:creator ?y . ?y rdf:type fb:Computer_Sci }
- 32. Steffen Staab 32Programmingthe Semantic Web SchemEX: Computation Precise computation: Brute-Force C1 C2 Cn... DS1 DS2 DSm ... C12 C2 Cn‗... P1 Pn... EQC EQC DS TCj TCk EQCj BSi SchemaPayload P2
- 33. Steffen Staab 33Programmingthe Semantic Web Stream-based Computation of SchemEX LOD Crawler: Stream of n-Quads (triple + data source) … Q16, Q15, Q14, Q13, Q12, Q11, Q10, Q9, Q8, Q7, Q6, Q5, Q4, Q3, Q2, Q1 FiFo 4 3 2 1 1 6 2 3 4 5 C3 C2 C2 C1
- 34. Steffen Staab 34Programmingthe Semantic Web Quality of Approximated Index Stream-based computation vs. brute force Data set of 11 Mio. tripel
- 35. Steffen Staab 35Programmingthe Semantic Web SchemEX @ BTC 2011 SchemEX Allows complex queries (Star, Chain) Scalable computation High quality Index over BTC 2011 data 2.17 billion tripel Index: 55 million tripel Commodity hardware VM: 1 Core, 4 GB RAM Throughput: 39.500 tripel / second Computation of full index: 15h
- 36. Steffen Staab 36Programmingthe Semantic Web Agenda SchemEX Where do I find relevant data? Efficient construction of a schema-level index Application LODatio: Search the LOD cloud Active user support LiteQ – Language integrated types, extensions and queries for RDF graphs Exploring Programming, Typing
- 37. Steffen Staab 37Programmingthe Semantic Web SPARQL queries on LOD ??? SELECT ?x WHERE { ?x rdf:type foaf:Document . ?x rdf:type swrc:InProceedings . ?x dc:creator ?y . ?y rdf:type fb:Computer_Scientist } Index • ACM • DBLP 0 hits 1.000 hits Help!
- 38. Steffen Staab 38Programmingthe Semantic Web Inspiration from web search engines ... Result set size Result Snippets Ranked Retrieval Reference to data source
- 39. Steffen Staab 39Programmingthe Semantic Web Inspiration from web search engines ... Related Queries
- 40. Steffen Staab 40Programmingthe Semantic Web Did you mean? Result set size Result Snippets Related Queries Ranked Retrieval Reference to data source
- 41. Steffen Staab 41Programmingthe Semantic Web LODatio: Extending the Payload C1 C2 Cn... DS-URI1 C12 C2 Cn‗... P1 Pn... EQC EQC TCj TCk EQCj BSi SchemaPayload P2 DS1 EX1-1 EX1-2 EX1-3 200 ABC DEF GHI DS-URI2 DS2 EX2-1 150 XYZ
- 42. Steffen Staab 42Programmingthe Semantic Web C3 P1 Realizing „Related Queries― C1 C2 TC1 EQC2 DS3 300 SELECT ?x WHERE { ?x rdf:type C1 . ?x rdf:type C2 } C1 C2 EQC3 DS4 150 P1 EQC1 DS1 200 DS2 150 C3 BS1 TC2 SELECT ?x WHERE { ?x rdf:type C1 . ?x rdf:type C2 . ?x P1 ?y } SELECT ?x WHERE { ?x rdf:type C1 . ?x rdf:type C2 . ?x P1 ?y . ?y rdf:type C3 . }
- 43. Steffen Staab 43Programmingthe Semantic Web Conclusions Programming the Semantic Web requires new concepts Linked Open Data High volume, Varied data, Varying schemata Schema-level indices Efficient approximative computation High accuracy Applications Search Analysis ... (many more)
- 44. Institute for WebScience & Technologies – WeST Thank you!
- 45. Steffen Staab 45Programmingthe Semantic Web References 1. M. Konrath, T. Gottron, and A. Scherp, ―Schemex – web-scale indexed schema extraction of linked open data,‖ in Semantic Web Challenge, Submission to the Billion Triple Track, 2011. 2. M. Konrath, T. Gottron, S. Staab, and A. Scherp, ―Schemex—efficient construction of a data catalogue by stream-based indexing of linked data,‖ Journal of Web Semantics, 2012. 3. T. Gottron, M. Knauf, S. Scheglmann, and A. Scherp, ―Explicit and implicit schema information on the linked open data cloud: Joined forces or antagonists?,‖ Tech. Rep. 06/2012, Institut WeST, Universität Koblenz-Landau, 2012. 4. T. Gottron and R. Pickhardt, ―A detailed analysis of the quality of stream-based schema construction on linked open data,‖ in CSWS‘12: Proceedings of the Chinese Semantic Web Symposium, 2012. 5. T. Gottron, A. Scherp, B. Krayer, and A. Peters, ―Get the google feeling: Supporting users in finding relevant sources of linked open data at web-scale,‖ in Semantic Web Challenge, Submission to the Billion Triple Track, 2012. 6. T. Gottron, A. Scherp, B. Krayer, and A. Peters, ―LODatio: Using a Schema-Based Index to Support Users in Finding Relevant Sources of Linked Data,‖ in K-CAP‘13: Proceedings of the Conference on Knowledge Capture, 2013. 7. T. Gottron, M. Knauf, S. Scheglmann, and A. Scherp, ―A Systematic Investigation of Explicit and Implicit Schema Information on the Linked Open Data Cloud,‖ in ESWC‘13: Proceedings of the 10th Extended Semantic Web Conference, 2013. 8. J. Schaible, T. Gottron, S. Scheglmann, and A. Scherp, ―LOVER: Support for Modeling Data Using Linked Open Vocabularies,‖ in LWDM‘13: 3rd International Workshop on Linked Web Data Management, 2013.