SPARQLing Constraints for RDF Michael Schmidt, joint work with Prof. Georg Lausen, Michael Meier

About… Michael Schmidt : Studies of Applied Computer Science in Saarbrücken 2006: Started my PhD in Saarbrücken with Prof. Christoph Koch  Focus on XML, XQuery, Streams Since 2007: at Freiburg University with Prof. Georg Lausen  Focus on SPARQL, RDF

Table of Contents SPARQLing Constraints for RDF  Constraints for RDF Types of constraints Encoding of constraints in RDF Satisfiability  SPARQL in the context of constraints Extracting constraints with SPARQL Checking constraints with SPARQL Exploiting constraints: Semantic Query Optimization SP 2 Bench: A SPARQL Performance Benchmark

PART I SPARQLing Constraints for RDF

SPARQLing Constraints for RDF RDF Data Format Machine-readable information Established in the Semantic Web SPARQL Query Language W3C Recommendation since January Constraints Primary and Foreign Keys Cardinality Constraints, … bases on

Why Constraints? Restricting the state space of the database Maintenance of data consistency (e.g. when data is updated) Semantic Query Optimization Better understanding of the data In our scenario: Translation of Relational Schemata to RDF without loss of information

Our Contribution Extension of RDF by constraints  Key constraints, cardinality constraints, …  Seamless integration into the RDF Framework Study of the role of SPARQL in this context  Checking constraints with SPARQL  Specification of user-defined constraints  Optimization of SPARQL queries under constraints (Semantic Query Optimization)

The RDF Data Format Three Types of Elements  URIs: represent physical or logical resources  Blank nodes: resources without fixed URI  Literals: represent values RDF Triples: (subject, predicate, object)  subject U U B  predicate U  object U U B U L

Example RDF Triple SubjectPredicateObject „Joe“ name URILiteral Graph Representation: Person1name Person1 „Joe“ RDF Triple

RDF Databases RDF Databases are Collections of Triples Currently no support for specification of primary/foreign key constraints Person1 name „Joe“ knows Person2 name „Pete“ rdf:type Student ssn „1234“ „2345“ ssn Person rdfs:subClassOf rdf:type

Mapping Relational Data to RDF namefaculty JoeCS FredCS matricname 11111John 22222Ed taught_byname JoeDB FredWeb c_ids_id Fred11111 Fred22222 TeachersStudents CoursesParticipants + NOT NULL constraint

A Naive Translation Approach Students name Teachers Courses t1 t2 s1 s2 c1 c2 “Joe“ “Fred““CS“ “11111““22222“ “John“ “Ed“ “DB“ “Web“ name matric faculty taught_by Participants p1 p2 s_id c_id “Joe“ “Fred“ “22222““Fred“ “11111“ “Fred“ rdf:type

Improving the Translation Students name Teachers Courses t1 t2 s1 s2 c1 c2 Joe Fred “CS“ “John“ “Ed“ “DB“ “Web“ name matric faculty taught_by Participants p1 p2 s_id c_id rdf:type

Encoding Primary Key Constraints Encoding of constraints in the schema layer New namespace „rdfc“ RDF Bags name Teachers t1 t2 JoeFred“CS“ name faculty T_Key rdfc:Key rdf:_1 name rdfc:Key rdf:Bag

taught_by Courses c1 c2 “DB“ “Web“ name taught_by rdfc:FKey name T_Key rdfc:Key rdf:_1 name rdfc:Key rdf:Bag name Teachers t1 t2 JoeFred“CS“ faculty C_FKey rdfc:FKey rdf:Bag rdfc:ref rdf:_1

Other Types of Constraints Let C, C 1, C 2 be classes and Q i, R i properties  Primary Keys Key(C,[Q 1,…Q n ])  Foreign Keys FKey(C 1,[Q 1,…Q n ],C 2,[R 1,…R n ])  Cardinality Constraints Min(C,n,R), Max(C,n,R) for n N  Functionality/Totality Constraints Func(C,Q), Total(C,Q)  Singleton Constraints: Single(C)

RDFS Constraints Let C i denote classes, Q i denote properties  Subclass Constraint SubC(C 1,C 2 )  Subproperty Constraint SubP(Q 1,Q 2 )  Property Domain/Range PropD(Q,C), PropR(Q,C) Restrict the state space of the database No „axioms“ that are used for inferencing

Satisfiability Given an RDF vocabulary and a set of constraints. Is there a non-empty RDF graph that satisfies the constraints? in general undecidable  Primary keys + Foreign Keys  Singleton  Max-Cardinality  Subclass + Subproperty  Property Domain + Property Range always satisfiable

Satisfiability Given an RDF vocabulary and a set of constraints. Is there a non-empty RDF graph that satisfies the constraints?  Primary keys + Foreign Keys  Singleton  Max-Cardinality  Subclass + Subproperty  Property Domain + Property Range  Min-Cardinality undecidable in general undecidable

Satisfiability Given an RDF vocabulary and a set of constraints. Is there a non-empty RDF graph that satisfies the constraints?  Unary primary keys  Unary foreign keys  Min-Cardinality + Max-Cardinality  Subclass + Subproperty  Property Domain + Property Range decidable in ExpTime in general undecidable

The SPARQL Query Language SELECT ?name ?faculty WHERE { ?teacher rdf:type Teachers. ?teacher name ?name. ?teacher faculty ?faculty. } name Teachers t1 t2 JoeFred“CS“ name faculty ?name?faculty Joe“CS“ Fred“CS“ Operator AND („.“)

The SPARQL Query Language SELECT ?name ?faculty WHERE { ?teacher rdf:type Teachers. ?teacher name ?name. ?teacher faculty ?faculty. FILTER (?name=„Joe“) } name Teachers t1 t2 JoeFred“CS“ name faculty ?name?faculty Joe“CS“ Operator FILTER

The SPARQL Query Language SELECT ?name ?faculty ?title WHERE { ?teacher rdf:type Teachers. ?teacher name ?name. ?teacher faculty ?faculty. OPTIONAL { ?teacher title ?title. } title „Professor“ ?name?faculty?title Joe“CS“ Fred“CS““Professor“ name Teachers t1 t2 JoeFred“CS“ name faculty Operator OPTIONAL

Extracting Primary Key Constraints SELECT ?keyname ?class ?keyatt WHERE { ?class rdfc:Key ?keyname. ?keyname rdf:type rdfc:Key. ?keyname ?bagrel ?keyatt. FILTER (?bagrel!=rdf:type) } ?keyname?class?keyatt T_KeyTeachersname T_Key rdfc:Key rdf:_1 name rdfc:Key rdf:Bag Teachers ……

Extracting Foreign Key Constraints SELECT ?keyname ?class ?keyatt ?ref WHERE { ?class rdfc:FKey ?keyname. ?keyname rdf:type rdfc:FKey. ?keyname ?bagrel ?keyatt. ?keyname rdfc:ref ?ref. FILTER (?bagrel!=rdf:type && ?bagrel!=rdfc:ref) } ORDER BY ?keyname taught_by Courses rdfc:FKey T_Key rdfc:Key rdf:_1 name rdfc:Key rdf:Bag Teachers C_FKey rdfc:FKey rdf:Bag rdfc:ref rdf:_1 ?keyname?class?keyatt?ref C_FKeyCoursestaught_byT_Key … …

Use SPARQL „ASK“ query form (returns „yes“ exactly if query contains a result, no otherwise) Constraint checks possible for many natural constraints  Primary Keys + Foreign Keys  Cardinality Constraints  … Checking Constraints with SPARQL A SPARQL query checks a constraint C if it returns yes for each graph that violates C, no otherwise.

Checking Constraints with SPARQL Checking primary key constraints ASK { ?x rdf:type C. ?y rdf:type C. ?x p1 ?p1; [...]; pn ?pn. ?y p1 ?p1; [...]; pn ?pn. FILTER (?x!=?y) } Key(C,[p1,...,pn]) Returns „yes“ exactly if constraint is violated.

Checking Constraints with SPARQL Checking primary key constraints (example) ASK { ?x rdf:type Teachers. ?y rdf:type Teachers. ?x name ?name. ?y name ?name FILTER (?x!=?y) } Returns „no“ (i.e., constraint holds) name Teachers t1 t2 JoeFred“CS“ name faculty

Checking Constraints with SPARQL Checking foreign key constraints ASK { ?x rdf:type C; p1 ?p1; [...]; pn ?pn. OPTIONAL { ?y rdf:type D; q1 ?p1; [...]; qn ?pn. } FILTER (!bound(?y)) } FKey(C,[p1,...,pn],D,[q1,... qn]) Returns „yes“ exactly if constraint is violated.

Semantic Query Optimization Idea: use constraint knowledge to find a more efficient query execution plan Has been studied in the context of relational and datalog databases… … and might now be applicable in the context of RDF and SPARQL

Semantic Query Optimization SELECT ?teachername ?coursename ?studentname WHERE { ?course rdf:type Courses; taught_by ?teachername; name ?coursename. ?participant rdf:type Participants; c_id ?teachername; s_id ?studentmatric. ?teacher rdf:type Teachers; name ?teachername. OPTIONAL { ?student rdf:type Students; matric ?studentmatric; name ?studentname. }

Students name Teachers Courses t1 t2 s1 s2 c1 c2 Joe Fred “CS“ “John“ “Ed“ “DB“ “Web“ name matric faculty taught_by Participants p1 p2 s_id c_id A Solution Candidate Subgraph

Semantic Query Optimization SELECT ?teachername ?coursename ?studentname WHERE { ?course rdf:type Courses; taught_by ?teachername; name ?coursename. ?participant rdf:type Participants; c_id ?teachername; s_id ?studentmatric. ?teacher rdf:type Teachers; name ?teachername. OPTIONAL { ?student rdf:type Students; matric ?studentmatric; name ?studentname. } Key(Students,[matric]) FKey(Participants, [s_id], Student, [matric]) Total(Students,[name])

Semantic Query Optimization SELECT ?teachername ?coursename ?studentname WHERE { ?course rdf:type Courses; taught_by ?teachername; name ?coursename. ?participant rdf:type Participants; c_id ?teachername; s_id ?studentmatric. ?teacher rdf:type Teachers; name ?teachername. ?student rdf:type Students; matric ?studentmatric; name ?studentname. } Key(Teacher, [name]) FKey(Courses, taught_by, Teacher, [name])

Semantic Query Optimization SELECT ?teachername ?coursename ?studentname WHERE { ?course rdf:type Courses; taught_by ?teachername; name ?coursename. ?participant rdf:type Participants; c_id ?teachername; s_id ?studentmatric. ?student rdf:type Students; matric ?studentmatric; name ?studentname. } Other optimizations possible:  Rewriting of filter expressions  Elimination from redundant rdf:type specifications  …

Future Work Study of other types of constraints and the interaction between constraints Development of a schematic approach to Semantic Query Optimization  Mapping to SQL/Datalog?  SPARQL-specific semantic optimizations? Efficient constraint checking algorithms

PART II SP 2 B – A SPARQL Performance Benchmark

PART II: SP 2 Bench Up-to-date no benchmark for SPARQL has been proposed  LUBM: focus on OWL and reasoning  Loose collection of benchmark queries for LUBM SP 2 B fills this gap  Settled in the DBLP scenario  Data generator for creating large arbitrarily large datasets + 16 benchmark queries Currently submitted for publication, will be made available online soon

The SP 2 Bench Data Generator Creates bibliography documents similar to DBLP Mirrors vital key characteristics found in original DBLP data  Structure of entities (Articles, Journals, Books, …)  Relations between authors  Quantity of entities (development over time)  Citation system Combines the benefits of both a real-world scenario and the possibility to generate arbitrarily large documents.

The DBLP RDF Schema sc

The SP 2 Bench Queries Operate on top of the characteristics that are mirrored by the data generator Designed to test…  … typical SPARQL operators and combinations  … SPARQL solution modifiers  … existing (but also obvious future) optimizations  … RDF data access patterns  … the impact of indices on data  … and many other characteristics such as result size, different graph patterns, etc.

Benchmark Queries SELECT ?yr WHERE { ?proc rdf:type bench:Journal. ?proc dc:title "Journal 1 (1940)"^^xsd:string. ?proc dcterms:issued ?yr. } Simple Constant result size (exactly 1 result) Might be answered very fast with index Q1

Benchmark Queries SELECT DISTINCT ?person ?name Q5 WHERE { ?article rdf:type bench:Article. ?article dc:creator ?person. ?inproc rdf:type bench:Inproceedings. ?inproc dc:creator ?person2. ?person foaf:name ?name. ?person2 foaf:name ?name2. FILTER(?name=?name2). } Q5a SELECT DISTINCT ?person ?name WHERE { ?article rdf:type bench:Article. ?article dc:creator ?person. ?inproc rdf:type bench:Inproceedings. ?inproc dc:creator ?person. ?person foaf:name ?name. } Q5b Equivalent in our scenario Tests implicit vs. explicit joins We found that Q5a is much more challenging for current engines

Benchmark Queries SELECT DISTINCT ?title Q7 WHERE { ?class rdfs:subClassOf foaf:Document. ?doc rdf:type ?class. ?doc dc:title ?title. ?bag2 ?member2 ?doc. ?doc2 dcterms:references ?bag2. OPTIONAL { ?class3 rdfs:subClassOf foaf:Document. ?doc3 rdf:type ?class3. ?doc3 dcterms:references ?bag3. ?bag3 ?member3 ?doc. OPTIONAL { ?class4 rdfs:subClassOf foaf:Document. ?doc4 rdf:type ?class4. ?doc4 dcterms:references ?bag4. ?bag4 ?member4 ?doc3. } FILTER (!bound(?doc4)). } FILTER (!bound(?doc3)). } Q7 Double Closed- World-Negation Returns all publications that are cited at least once, but only cited by cited publications

Benchmark Results We tested several SPARQL engines  ARQ  Sesame  Virtuoso  … Results demonstrate that …  … there are differences between engines  … there is still room for improvement in current implementation  … there is poor support for several SPARQL specifics

Thank you for your attention! C. Bizer.D2R MAP-A Database to RDF Mapping Language. In WWW (Posters), C.Bizer, R.Cyganiak, J. Garbers, and O. Maresch. D2RQ: Treading Non-RDF Relational Databases as Virtual RDF Graphs. User Manual and Language Specification. J. J. King. QUIST: A System for Semantic Query Optimization in Relational Databases. Distributed systems, Vol. II, pages , G. Lausen. Relational Databases in RDF. In Joint ODBIS & SWDB Workshop on Semantic Web, Ontologies, Databases, To appear. B. Motik, I. Horrocks, and U. Sattler. Bridging the Gap Between OWL and Relational Databases, In WWW, pages , J. Pérez, M. Arenas, and C. Gutierrez. Semantics and Complexity of SPARQL. In CoRR Technical Report cs.DB/ , Recourse Description Framework (RDF): Concepts and Abstract Syntax. W3C Recommendation, February 10, RDF Vocabulary Description Language 1.0: RDF Schema. W3C Recommendation, Febuary 10, RDF Semantics. W3C Recommendation, February 10, S.T. Shenoy and Z.M. Ozsoyoglu. A System for Semantic Query Optimization. In SIGMOD, pages , SPAQL Query Language for RDF. W3C Proposed Recommendation, November 12, G.E. Weddell. A Theory of Functional Dependencies for Object-Oriented Data Models. In DOOD, pages , 1989.

PART III Additional Resources

The SPARQL Query Language Operator UNION SELECT ?name ?faculty WHERE { { ?teacher rdf:type Teachers. ?teacher name ?name. ?teacher faculty ?faculty. FILTER (?name=„Joe“). } UNION { ?teacher rdf:type Teachers. ?teacher name ?name. ?teacher faculty ?faculty. FILTER (?name=„Fred“). } ?name?faculty Joe“CS“ Fred“CS“ name Teachers t1 t2 JoeFred“CS“ name faculty