Semantic Web SPARQL. Gerd Gröner, Matthias Thimm. July 16,

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1 Semantic Web SPARQL Gerd Gröner, Matthias Thimm Institute for Web Science and Technologies (WeST) University of Koblenz-Landau July 16, 2013 Gerd Gröner, Matthias Thimm Semantic Web 1 / 47

2 Agenda for this Week SPARQL (2 lectures) Ontology Engineering Pattern-based Ontology Design Semantic Search Semantic Web Applications, Closing Gerd Gröner, Matthias Thimm Semantic Web 2 / 47

3 Outline 1 SPARQL Protocol and RDF Query Language 2 SPARQL Protocol 3 Networked Graphs 4 SPARQL 1.1 Gerd Gröner, Matthias Thimm Semantic Web 3 / 47

4 Outline 1 SPARQL Protocol and RDF Query Language 2 SPARQL Protocol 3 Networked Graphs 4 SPARQL 1.1 Gerd Gröner, Matthias Thimm Semantic Web 4 / 47

5 Outline 1 SPARQL Protocol and RDF Query Language 2 SPARQL Protocol 3 Networked Graphs 4 SPARQL 1.1 Gerd Gröner, Matthias Thimm Semantic Web 5 / 47

6 SPARQL Protocol The SPARQL protocol specifies how queries and query results are exchanged / sent between providing and requesting service. Queries and results are sent over the network. Protocol is specified by two parts 1. SPARQL interface (abstract interface, independent of concrete implementation) 2. bindings of this interface for the query (request) HTTP binding SOAP binding for the result XML result binding WSDL 2.0 (Web service description langauge) to describe the SPARQL protocol Gerd Gröner, Matthias Thimm Semantic Web 6 / 47

7 SparqlQuery Interface SparqlQuery is the only interface of the SPARQL protocol. It contails one operation: query the query operation described by an in-out message exchange pattern this in-out message exchange pattern consists of two messages: in -message out -message Gerd Gröner, Matthias Thimm Semantic Web 7 / 47

8 Interface and Operation the query in message Content of an in message (of a SPARQL query operation) is an instance of an XML Schema complex type Gerd Gröner, Matthias Thimm Semantic Web 8 / 47

9 Interface and Operation the query out message contents of the out message of the query operation is an instance of an XML Schema complex type the out message is composed of either: a SPARQL result document (for SELECT and ASK queries), or a serialized RDF graph (e.g., in the RDF/XML syntax) (for CONSTRUCT and DESCRIBE queries) Additionally, a specification of return message in case of incorrect (malformed) queries, or refused request Gerd Gröner, Matthias Thimm Semantic Web 9 / 47

10 SPARQL Query Binding: HTTP Binding Query: PREFIX dc : <h t t p : / / p u r l. org / dc / e l e m e n t s /1.1/ > SELECT? book?who WHERE {? book dc : c r e a t o r?who } conveyed to a SPARQL query service http: // www. example. com/ sparql/, which is illustrated by the following HTTP trace: GET / s p a r q l /? q u e r y=encodedquery HTTP/ 1. 1 Host : www. example. com User agent : my s p a r q l c l i e n t / 0. 1 Gerd Gröner, Matthias Thimm Semantic Web 10 / 47

11 SPARQL Query Binding: SOAP Binding SOAP binding: <?xml v e r s i o n = 1.0 e n c o d i n g= UTF 8?> <soapenv : E n v e l o p e xmlns : soapenv= h t t p : / /www. w3. org /2003/05/ soap e n v e l o p e / xmlns : xsd= h t t p : / /www. w3. org /2001/XMLSchema xmlns : x s i = h t t p : / /www. w3. org /2001/XMLSchema i n s t a n c e > <soapenv : Body> <query r e q u e s t xmlns= h t t p : / /www. w3. org /2005/09/ s p a r q l p r o t o c o l t y p e s/# > <query>select? x? y WHERE {? x i s R e l a t e d T o? y}</query> </query r e q u e s t > </soapenv : Body> </soapenv : Envelope > Gerd Gröner, Matthias Thimm Semantic Web 11 / 47

12 Result XML Binding Remember: previously introduced example query: SELECT? book?who WHERE {? book dc : c r e a t o r?who } Result: HTTP/ OK S e r v e r : Apache / ( Unix ) PHP/ DAV/ C o n n e c t i o n : c l o s e Content Type : a p p l i c a t i o n / s p a r q l r e s u l t s+xml <?xml v e r s i o n = 1.0?> <s p a r q l xmlns= h t t p : / /www. w3. org /2005/ s p a r q l r e s u l t s # > <head> < v a r i a b l e name= book /> < v a r i a b l e name= who /> </head> < r e s u l t s d i s t i n c t = f a l s e o r d e r e d= f a l s e > <r e s u l t > h t t p : / /www. example / book5<bnode>r r2922 </bnode> </ r e s u l t >... </ s p a r q l > Gerd Gröner, Matthias Thimm Semantic Web 12 / 47

13 SPARQL Result XML Binding <?xml v e r s i o n = 1.0?> <s p a r q l xmlns= h t t p : / /www. w3. org /2005/ s p a r q l r e s u l t s # > <head> < v a r i a b l e name= name /> < v a r i a b l e name= mbox /> </head> <r e s u l t s > <r e s u l t > ... ... </ r e s u l t > <r e s u l t > ... ... </ r e s u l t >... </ r e s u l t s > </ s p a r q l > Gerd Gröner, Matthias Thimm Semantic Web 13 / 47

14 Binding Types inside Results <r e s u l t > <bnode>r2 </bnode> h t t p : / / work. example. org /bob/ < l i t e r a l xml : l a n g= en >Bob</ l i t e r a l > < l i t e r a l d a t a t y p e= h t t p : / /www. w3. org /2001/XMLSchema#i n t e g e r >30 </ l i t e r a l > m a i l t o : bob@work. example. org </ r e s u l t > Gerd Gröner, Matthias Thimm Semantic Web 14 / 47

15 Outline 1 SPARQL Protocol and RDF Query Language 2 SPARQL Protocol 3 Networked Graphs 4 SPARQL 1.1 Gerd Gröner, Matthias Thimm Semantic Web 15 / 47

16 Networked Graphs Simon Schenk and Steffen Staab Networked Graphs: A Declarative Mechanism for SPARQL Rules, SPARQL Views and RDF Data Integration on the Web, Proceedings of the 17th International World Wide Web Conference, WWW2008, Bejing, China, Gerd Gröner, Matthias Thimm Semantic Web 16 / 47

17 Networked Graphs Basic Idea: define RDF graphs extensionally by listing statements (RDF triples) or intensionally using views possible to have view within another view (recursion) Integrate with existing Semantic Web infrastructure Easy exchange of (networked) graphs Use existing data sources (RDF graphs) Gerd Gröner, Matthias Thimm Semantic Web 17 / 47

18 Background: The Vision of the Semantic Web A Web of Machine understandable data. Allowing for Reuse of data. A Distributed Infrastructure Managed by autonomous agents Fundamental technologies: RDF, SPARQL HTTP GET Gerd Gröner, Matthias Thimm Semantic Web 18 / 47

19 Background: The Vision of the Semantic Web A Web of Machine understandable data. Allowing for Reuse of data. A Distributed Infrastructure Managed by autonomous agents Fundamental technologies: RDF, SPARQL HTTP GET Gerd Gröner, Matthias Thimm Semantic Web 18 / 47

20 Motivation for Networked Graphs: Mashups Gerd Gröner, Matthias Thimm Semantic Web 19 / 47

21 Mashups and Semantic Web Mashups most popular model: Hack-and-Hope Disadvantages: Screen-Scraping (reading, extracting text from documents (Web pages)) No agreement on a data model It may be different sometimes: Google Web service Amazon Web service Semantic Web most popular model: Crawl-Integrate-and-Reason Disadvantages: Data is outdated Data is not declarative, but only has implicit semantics Scalability problems Access rights: not all is allowed to be copied / published Provenance data sources can be blurred Gerd Gröner, Matthias Thimm Semantic Web 20 / 47

22 Mashups and Semantic Web Mashups most popular model: Hack-and-Hope Disadvantages: Screen-Scraping (reading, extracting text from documents (Web pages)) No agreement on a data model It may be different sometimes: Google Web service Amazon Web service Semantic Web most popular model: Crawl-Integrate-and-Reason Disadvantages: Data is outdated Data is not declarative, but only has implicit semantics Scalability problems Access rights: not all is allowed to be copied / published Provenance data sources can be blurred Gerd Gröner, Matthias Thimm Semantic Web 20 / 47

23 Mashups and Semantic Web Mashups most popular model: Hack-and-Hope Disadvantages: Screen-Scraping (reading, extracting text from documents (Web pages)) No agreement on a data model It may be different sometimes: Google Web service Amazon Web service Semantic Web most popular model: Crawl-Integrate-and-Reason Disadvantages: Data is outdated Data is not declarative, but only has implicit semantics Scalability problems Access rights: not all is allowed to be copied / published Provenance data sources can be blurred Gerd Gröner, Matthias Thimm Semantic Web 20 / 47

24 Networked Graphs A Concrete Scenario Gerd Gröner, Matthias Thimm Semantic Web 21 / 47

25 Objectives in this Scenario A Web of Machine understandable data. Allowing for Reuse of data. A Distributed Infrastructure Managed by autonomous agents Hardly any assumptions: RDF, SPARQL HTTP GET Objectives: Distributed Views Re-use existing languages/protocols (RDF extension) Default-negation Easy to learn, easy to exchange Gerd Gröner, Matthias Thimm Semantic Web 22 / 47

26 Objectives in this Scenario A Web of Machine understandable data. Allowing for Reuse of data. A Distributed Infrastructure Managed by autonomous agents Hardly any assumptions: RDF, SPARQL HTTP GET Objectives: Distributed Views Re-use existing languages/protocols (RDF extension) Default-negation Easy to learn, easy to exchange Gerd Gröner, Matthias Thimm Semantic Web 22 / 47

27 Remember: Named Graphs Used to group a set of triples. Gerd Gröner, Matthias Thimm Semantic Web 23 / 47

28 Remember: SPARQL CONSTRUCT Queries Build an RDF graph from existing triples (here from named graphs): CONSTRUCT {?member f o a f : c u r r e n t P r o j e c t : SemWebProject.?member f o a f : phone? phone } FROM NAMED : bobfoaf FROM NAMED : chrisfoaf FROM NAMED... WHERE { GRAPH? f o a f F i l e {? f o a f F i l e f o a f : p r i m a r y T o p i c?member OPTIONAL {?member f o a f : phone? phone FILTER (REGEX(? phone, ˆ \ ) ) } } } Query: Find the persons described in the FOAF files, their phone numbers, if available. German phone numbers only. Gerd Gröner, Matthias Thimm Semantic Web 24 / 47

29 Now: Networked Graphs Basic Idea: Define RDF graphs extensionally by listing statements or intensionally using views based on SPARQL Gerd Gröner, Matthias Thimm Semantic Web 25 / 47

30 Independence Set IS (BobFOAF) = { BobFOAF M i k e s P r o j e c t, DBLP, ChrisFOAF } Semantics of the independence set: Iteratively evaluate all views until a fixpoint is reached. Possible problems: Termination of recursion Non-monotonic negation Gerd Gröner, Matthias Thimm Semantic Web 26 / 47

31 Well-founded Semantics Models of the Well Founded Semantics closed-world (Here: rules only, graphs may be open worlds.) pessimistic (negate as much as possible) Interpretation of each fact is three-valued (true, false, unknown) Usage with RDF In RDF only true statements; false, unknown treated equally. In Networked Graphs false and unknown are used for interim results. Gerd Gröner, Matthias Thimm Semantic Web 27 / 47

32 RDF Mashups with declarative, dynamic semantics, Mashups can be very powerful! re-use and combine data from several data sources on the Web Gerd Gröner, Matthias Thimm Semantic Web 28 / 47

Networked Graphs Architecture Based on Sesame 2 (http://www.openrdf.org/) Open source (http://west.

33 Networked Graphs Architecture Based on Sesame 2 ( Open source ( systems/networkedgraphs) Gerd Gröner, Matthias Thimm Semantic Web 29 / 47

34 Networked Graphs Conclusion Networked Graphs allows for easy formulation, exchange and distributed evaluation of rules and views for the Semantic Web using default negation Networked Graphs integrate well with existing mechanisms and can be evaluated efficiently. Networked Graphs are eases the re-use of existing data in various applications. Gerd Gröner, Matthias Thimm Semantic Web 30 / 47

35 Outline 1 SPARQL Protocol and RDF Query Language 2 SPARQL Protocol 3 Networked Graphs 4 SPARQL 1.1 Gerd Gröner, Matthias Thimm Semantic Web 31 / 47

36 SPARQL some extensions in SPARQL 1.1: Projected expressions Aggregationen Subqueries Negation Property path Service descriptions Update language Update protocol HTTP RDF update (RESTful) Basic federated query Gerd Gröner, Matthias Thimm Semantic Web 32 / 47

37 Projected expressions SELECT queries are no longer restricted to variables: SELECT (? p r i c e? amount AS? t o t a l P r i c e ) WHERE {... } Gerd Gröner, Matthias Thimm Semantic Web 33 / 47

38 Aggregation Like in SQL: COUNT, SUM, MIN, MAX, GROUP BY SELECT (MIN(? p r i c e ) AS? m i n P r i c e )... WHERE {... } GROUP BY? a r t i c l e Gerd Gröner, Matthias Thimm Semantic Web 34 / 47

39 Subqueries nested queries multiple queries can be cominged in one query SELECT? a r t i c l e? a u t h o r WHERE? a r t i c l e ex : a u t h o r? a u t h o r. { SELECT? a r t i c l e WHERE {...? a r t i c l e... } ORDER BY... LIMIT... } Result of a query is nested in another query. Gerd Gröner, Matthias Thimm Semantic Web 35 / 47

40 Negation The trick with OPTIONAL and BOUND is no longer needed. SELECT... WHERE {? p e r s o n a f o a f : Person. MINUS {? p e r s o n f o a f : mbox? e m a i l } } Both graph patterns are evaluated and then the difference is returned. Gerd Gröner, Matthias Thimm Semantic Web 36 / 47

41 Negation (2) Alternative construct NOT EXISTS SELECT... WHERE {? p e r s o n a f o a f : Person. { FILTER NOT EXISTS {? p e r s o n f o a f : mbox? e m a i l } } Both graph patterns are evaluated and then the difference is returned. Gerd Gröner, Matthias Thimm Semantic Web 37 / 47

42 Property path Excerpt of constructors (let p be an IRI for a predicate, e.g., foaf:knows): ^p inverse path p* multiple times (including 0) p+ multiple times at least 1 p? zero or one times p1/p2 sequence p1 p2 alternative Gerd Gröner, Matthias Thimm Semantic Web 38 / 47

43 Property path (2) regular expression SELECT... WHERE {? p e r s o n f o a f : knows+? network. } Gerd Gröner, Matthias Thimm Semantic Web 39 / 47

44 Property path (3) SELECT... WHERE {? book dc : t i t l e r d f s : l a b e l? d i s p l a y S t r i n g. } regular expression e.g., alternative Gerd Gröner, Matthias Thimm Semantic Web 40 / 47

45 Property path (3) SELECT... WHERE {? book dc : t i t l e r d f s : l a b e l? d i s p l a y S t r i n g. } regular expression e.g., alternative Gerd Gröner, Matthias Thimm Semantic Web 40 / 47

46 Property path (3) regular expression e.g., Sequence: Find the names of people 2 foaf:knows links away. SELECT... WHERE {? x f o a f : mbox <m a i l t o : a l i c e x a m p l e >.? x f o a f : knows/ f o a f : knows/ f o a f : name?name. } without property path? Gerd Gröner, Matthias Thimm Semantic Web 41 / 47

47 Property path (3) regular expression e.g., Sequence: Find the names of people 2 foaf:knows links away. SELECT... WHERE {? x f o a f : mbox <m a i l t o : a l i c e x a m p l e >.? x f o a f : knows/ f o a f : knows/ f o a f : name?name. } without property path? Gerd Gröner, Matthias Thimm Semantic Web 41 / 47

48 Property path (4) equivalent query (without property path): SELECT... WHERE {? x f o a f : mbox <m a i l t o : a l i c e x a m p l e >.? x f o a f : knows? a1.? a1 f o a f : knows? a2.? a2 f o a f : name?name. } Gerd Gröner, Matthias Thimm Semantic Web 42 / 47

49 HAVING (over GROUPed sets) HAVING operates over grouped solution sets, in the same way that FILTER operates over un-grouped sets PREFIX : <h t t p : / / data. example/> SELECT (AVG(? s i z e ) AS? a s i z e ) WHERE {? x : s i z e? s i z e } GROUP BY? x HAVING(AVG(? s i z e ) > 10) This will return average sizes, grouped by the subject, but only where the mean size is greater than 10. Gerd Gröner, Matthias Thimm Semantic Web 43 / 47

50 HAVING (over GROUPed sets) HAVING operates over grouped solution sets, in the same way that FILTER operates over un-grouped sets PREFIX : <h t t p : / / data. example/> SELECT (AVG(? s i z e ) AS? a s i z e ) WHERE {? x : s i z e? s i z e } GROUP BY? x HAVING(AVG(? s i z e ) > 10) This will return average sizes, grouped by the subject, but only where the mean size is greater than 10. Gerd Gröner, Matthias Thimm Semantic Web 43 / 47

51 Summary SPARQL Standard query language for RDF SELECT, CONSTRUCT, ASK, DESCRIBE Extensive filters, optional and alternative patterns Protocol for queries and results Based on triples model (subject-predicate-object) No logic inference in language, only in underlying knowledge base Named graphs Separate or specialized knowledge Gerd Gröner, Matthias Thimm Semantic Web 44 / 47

52 Learning goals Understanding basic and advanced queries using SPARQL. Gain knowledge about how to query reified data. Gerd Gröner, Matthias Thimm Semantic Web 45 / 47

53 SPARQL endpoints Name URL What s in there? DBPedia extensive RDF data from Wikipedia SPARQLer tttp://sparql.org/sparql.html General-purpose query endpoint for Web-accessible data DBLP Bibliographic data from computer science journals and conferences LinkedMDB Films, actors, directors, writers, producers, etc. World factbook Country statistics from the CIA World Factbook bio2rdf Bioinformatics data from around 40 public databases Gerd Gröner, Matthias Thimm Semantic Web 46 / 47

54 Exercise querying DBpedia endpoint Find 10 cities ( that are located in (property Germany ( Find 10 Populated Places (URI: with their names (rdfs:label) in which the names are described in German (i.e., language tag de ). Find 10 people ( with place of birth ( Berlin ( Gerd Gröner, Matthias Thimm Semantic Web 47 / 47

SPARQL Extensions and Outlook

SPARQL Extensions and Outlook Axel Polleres 1 1 DERI Galway, National University of Ireland, Galway axel.polleres@deri.org European Semantic Web Conference 2007 A. Polleres SPARQL Extensions and Outlook