SPARQL: Un Lenguaje de Consulta para la Web

Transcription

1 SPARQL: Un Lenguaje de Consulta para la Web Semántica Marcelo Arenas Pontificia Universidad Católica de Chile y Centro de Investigación de la Web M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 1 / 54

2 Outline RDF model Querying RDF data Conjunctive queries Entailment of RDF graphs Graphs with RDFS vocabulary Inference rules Querying RDFS data: Closure, Core. Querying RDF Data in practice: SPARQL Formal semantics for SPARQL Complexity of the SPARQL evaluation problem A procedural semantics: Well designed patterns M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 2 / 54

3 Semantic Web The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Specific Goals: [Tim Berners-Lee et al ] Build a description language with standard semantics. Make semantics machine-processable and understandable. Incorporate logical infrastructure to reason about resources. W3C Proposal: Resource Description Framework (RDF). M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 3 / 54

4 RDF in a nutshell RDF is the W3C proposal framework for representing information in the Web. Abstract syntax based on directed labeled graph. Schema definition language (RDFS): Define new vocabulary (typing, inheritance of classes and properties). Extensible URI-based vocabulary. Support use of XML schema datatypes. Formal semantics. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 4 / 54

5 RDF formal model U Subject Predicate Object U = set of Uris B = set of Blank nodes U B U B L L = set of Literals M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 5 / 54

6 RDF formal model U Subject Predicate Object U = set of Uris B = set of Blank nodes U B U B L L = set of Literals (s,p,o) (U B) U (U B L) is called an RDF triple M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 5 / 54

7 RDF formal model U Subject Predicate Object U = set of Uris B = set of Blank nodes U B U B L L = set of Literals (s,p,o) (U B) U (U B L) is called an RDF triple A set of RDF triples is called an RDF graph M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 5 / 54

8 RDFS: An example person rdf:dom works in rdf:range company rdf:sc sportman rdf:sp rdf:sc rdf:sc soccer player rdf:dom plays in rdf:range soccer team rdf:type rdf:type Ronaldinho plays in Barcelona lives in Spain M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 6 / 54

9 RDFS: An example person rdf:dom works in rdf:range company rdf:sc sportman rdf:sp rdf:sc rdf:sc soccer player rdf:dom plays in rdf:range soccer team rdf:type rdf:type Ronaldinho plays in Barcelona lives in Spain M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 6 / 54

10 RDF model Some difficulties: Existential variables as datavalues Built-in vocabulary with fixed semantics (RDFS) Graph model where nodes may also be edge labels RDF data processing can take advantage of database techniques: Query processing Storing Indexing M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 7 / 54

11 RDF model Some difficulties: Existential variables as datavalues Built-in vocabulary with fixed semantics (RDFS) Graph model where nodes may also be edge labels RDF data processing can take advantage of database techniques: Query processing Storing Indexing M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 7 / 54

12 Querying RDF data Conjunctive query: Q(X) = Y t 1 t 2 t k Some examples: M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 8 / 54

13 Querying RDF data Conjunctive query: Q(X) = Y t 1 t 2 t k Some examples: (Ronaldinho, plays in, Barcelona) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 8 / 54

14 Querying RDF data Conjunctive query: Q(X) = Y t 1 t 2 t k Some examples: (Ronaldinho, plays in, Barcelona) (Ronaldinho, plays in, X) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 8 / 54

15 Querying RDF data Conjunctive query: Q(X) = Y t 1 t 2 t k Some examples: Y (Ronaldinho, plays in, Barcelona) (Ronaldinho, plays in, X) (X, plays in, Y ) (X, lives in,spain) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 8 / 54

16 Semantics of conjunctive queries Given an RDF graph G, a conjunctive query Q(X) and a tuple ā of values in U B L: Notation: G = Q(ā) Is ā an answer to Q(X) in G? Notice that Q(X) and ā may include blank nodes. Blank nodes play a similar role as existential variables. (Ronaldinho, plays in, B) and X (Ronaldinho, plays in, X) are equivalent. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 9 / 54

17 Conjunctive queries and entailment of RDF graphs Q(ā) can be transformed into an RDF graph G. Notion to define: G = G Entailment of RDF graphs: M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 10 / 54

18 Conjunctive queries and entailment of RDF graphs Q(ā) can be transformed into an RDF graph G. Notion to define: G = G Entailment of RDF graphs: Can be defined in terms of classical notions such model, interpretation, etc M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 10 / 54

19 Conjunctive queries and entailment of RDF graphs Q(ā) can be transformed into an RDF graph G. Notion to define: G = G Entailment of RDF graphs: Can be defined in terms of classical notions such model, interpretation, etc As for the case of first order logic M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 10 / 54

20 Conjunctive queries and entailment of RDF graphs Q(ā) can be transformed into an RDF graph G. Notion to define: G = G Entailment of RDF graphs: Can be defined in terms of classical notions such model, interpretation, etc As for the case of first order logic Has a graph characterization via homomorphisms. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 10 / 54

21 Homomorphism A function h : U B L U B L is a homomorphism h from G 1 to G 2 if: h(c) = c for every c U L; for every (a,b,c) G 1, (h(a),h(b),h(c)) G 2 Notation: G 1 G 2 Example: h = {B b} a p B a p p b b M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 11 / 54

22 Entailment Theorem (CM77) G 1 = G 2 if and only if there is a homomorphism G 2 G 1. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 12 / 54

23 Entailment Theorem (CM77) G 1 = G 2 if and only if there is a homomorphism G 2 G 1. a a p B p = p b b M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 12 / 54

24 Entailment Theorem (CM77) G 1 = G 2 if and only if there is a homomorphism G 2 G 1. a a p B p = p b b p M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 12 / 54

25 Entailment Theorem (CM77) G 1 = G 2 if and only if there is a homomorphism G 2 G 1. a a p B p = p b b p Complexity Entailment for RDF is NP-complete M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 12 / 54

26 Graphs with RDFS vocabulary Previous characterization of entailment is not enough to deal with RDFS vocabulary: M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 13 / 54

27 Graphs with RDFS vocabulary Previous characterization of entailment is not enough to deal with RDFS vocabulary: (Ronaldinho, rdf : type, person) person rdf:dom works in rdf:range company rdf:sc sportman rdf:sp rdf:sc rdf:sc soccer player rdf:dom plays in rdf:range soccer team rdf:type rdf:type Ronaldinho plays in Barcelona lives in Spain M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 13 / 54

28 Graphs with RDFS vocabulary Built-in predicates have pre-defined semantics: M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 14 / 54

29 Graphs with RDFS vocabulary Built-in predicates have pre-defined semantics: rdf:sc: transitive M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 14 / 54

30 Graphs with RDFS vocabulary Built-in predicates have pre-defined semantics: rdf:sc: transitive rdf:sp: transitive M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 14 / 54

31 Graphs with RDFS vocabulary Built-in predicates have pre-defined semantics: rdf:sc: transitive rdf:sp: transitive More complicated interactions: (p,rdf:dom, c) (a, p, b) (a, rdf:type, c) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 14 / 54

32 Graphs with RDFS vocabulary Built-in predicates have pre-defined semantics: rdf:sc: transitive rdf:sp: transitive More complicated interactions: (p,rdf:dom, c) (a, p, b) (a, rdf:type, c) RDFS-entailment can be characterized by a set of rules An Existential rule Subproperty rules Subclass rules Typing rules Implicit typing M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 14 / 54

33 Graphs with RDFS vocabulary: Inference rules Inference system in [MPG07] has 14 rules: Existential rule : Subproperty rules : Subclass rules : Typing rules : Implicit typing : M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 15 / 54

34 Graphs with RDFS vocabulary: Inference rules Inference system in [MPG07] has 14 rules: Existential rule : G 1 G 2 if G 2 G 1 Subproperty rules : Subclass rules : Typing rules : Implicit typing : M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 15 / 54

35 Graphs with RDFS vocabulary: Inference rules Inference system in [MPG07] has 14 rules: Existential rule : Subproperty rules : G 1 G 2 if G 2 G 1 (p,rdf:sp, q) (a, p, b) (a, q, b) Subclass rules : Typing rules : Implicit typing : M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 15 / 54

36 Graphs with RDFS vocabulary: Inference rules Inference system in [MPG07] has 14 rules: Existential rule : Subproperty rules : Subclass rules : G 1 G 2 if G 2 G 1 (p,rdf:sp, q) (a, p, b) (a, q, b) (a, rdf:sc, b) (b, rdf:sc, c) (a, rdf:sc, c) Typing rules : Implicit typing : M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 15 / 54

37 Graphs with RDFS vocabulary: Inference rules Inference system in [MPG07] has 14 rules: Existential rule : Subproperty rules : Subclass rules : Typing rules : G 1 G 2 if G 2 G 1 (p,rdf:sp, q) (a, p, b) (a, q, b) (a, rdf:sc, b) (b, rdf:sc, c) (a, rdf:sc, c) (p,rdf:dom, c) (a, p, b) (a, rdf:type, c) Implicit typing : M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 15 / 54

38 Graphs with RDFS vocabulary: Inference rules Inference system in [MPG07] has 14 rules: Existential rule : Subproperty rules : Subclass rules : Typing rules : Implicit typing : G 1 G 2 if G 2 G 1 (p,rdf:sp, q) (a, p, b) (a, q, b) (a, rdf:sc, b) (b, rdf:sc, c) (a, rdf:sc, c) (p,rdf:dom, c) (a, p, b) (a, rdf:type, c) (q, rdf:dom, a) (p, rdf:sp, q) (b, p, c) (b, rdf:type, a) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 15 / 54

39 Graphs with RDFS vocabulary: Inference rules Inference system in [MPG07] has 14 rules: Existential rule : Subproperty rules : (p,rdf:sp, q) (a, p, b) (a, q, b) Subclass rules : Typing rules : Implicit typing : (p,rdf:dom, c) (a, p, b) (a, rdf:type, c) (q, rdf:dom, a) (p, rdf:sp, q) (b, p, c) (b, rdf:type, a) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 15 / 54

40 Graphs with RDFS vocabulary: Inference rules Inference system in [MPG07] has 14 rules: Existential rule : Subproperty rules : (p,rdf:sp, q) (a, p, b) (a, q, b) Subclass rules : Typing rules : Implicit typing : (p,rdf:dom, c) (a, p, b) (a, rdf:type, c) (B, rdf:dom, a) (p, rdf:sp, B) (b, p, c) (b, rdf:type, a) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 15 / 54

41 RDFS Entailment Theorem (H03,GHM04,MPG07) G 1 = G 2 iff there is a proof of G 2 from G 1 using the system of 14 inference rules. Complexity RDFS-entailment is NP-complete. Proof idea Membership in NP: If G 1 = G 2, then there exists a polynomial-size proof of this fact. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 16 / 54

42 Querying RDFS data System of inference rules can be used as a mechanism for evaluating queries. It is difficult to implement. Is there any practical mechanism for evaluating queries? M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 17 / 54

43 Querying RDFS data System of inference rules can be used as a mechanism for evaluating queries. It is difficult to implement. Is there any practical mechanism for evaluating queries? Making explicit the implicit information. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 17 / 54

44 Closure of an RDF Graph Notation: ground(g) : Graph obtained by replacing every blank B in G by a constant c B. ground 1 (G) : Graph obtained by replacing every constant c B in G by B. Closure of an RDF graph G (denoted by closure(g)): M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 18 / 54

45 Closure of an RDF Graph Notation: ground(g) : Graph obtained by replacing every blank B in G by a constant c B. ground 1 (G) : Graph obtained by replacing every constant c B in G by B. Closure of an RDF graph G (denoted by closure(g)): G {t (U B) U (U B L) there exists a ground tuple t such that ground(g) = t and t = ground 1 (t )} M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 18 / 54

46 Closure of an RDF Graph: Example c c rdf : sc rdf : sc b b rdf : sc rdf : sc rdf : sc a a M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 19 / 54

47 Closure of an RDF Graph: Example c c rdf : sc rdf : sc b b rdf : sc rdf : sc rdf : sc a a M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 19 / 54

48 Querying RDFS data: Using the closure of a graph Proposition (H03,GHM04,MPG07) G 1 = G 2 iff G 2 closure(g 1 ) Complexity The closure of G can be computed in time O( G 4 log G ). M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 20 / 54

49 Querying RDFS data: Using the closure of a graph Proposition (H03,GHM04,MPG07) G 1 = G 2 iff G 2 closure(g 1 ) Complexity The closure of G can be computed in time O( G 4 log G ). Can the closure be used in practice? M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 20 / 54

50 Querying RDFS data: Using the closure of a graph Proposition (H03,GHM04,MPG07) G 1 = G 2 iff G 2 closure(g 1 ) Complexity The closure of G can be computed in time O( G 4 log G ). Can the closure be used in practice? Can we use an alternative materialization? M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 20 / 54

51 Querying RDFS data: Using the closure of a graph Proposition (H03,GHM04,MPG07) G 1 = G 2 iff G 2 closure(g 1 ) Complexity The closure of G can be computed in time O( G 4 log G ). Can the closure be used in practice? Can we use an alternative materialization? Can we materialize a small part of the closure? M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 20 / 54

52 Core of an RDF Graph An RDF Graph G is a core if there is no homomorphism from G to a proper subgraph of it. Theorem (HN92,FKP03,GHM04) Each RDF graph G has a unique core (denoted by core(g)). Deciding if G is a core is conp-complete. Deciding if G = core(g ) is DP-complete. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 21 / 54

53 Core and RDFS For RDF graphs with RDFS vocabulary, the core of G may contain redundant information: d d rdf : sc rdf : sc rdf : sc c c rdf : sc rdf : sc B rdf : sc b b rdf : sc rdf : sc rdf : sc a a M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 22 / 54

56 A normal form for RDF graphs To reduce the size of the materialization, we can combine both core and closure. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 23 / 54

57 A normal form for RDF graphs To reduce the size of the materialization, we can combine both core and closure. nf(g) = core(closure(g)) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 23 / 54

58 A normal form for RDF graphs To reduce the size of the materialization, we can combine both core and closure. nf(g) = core(closure(g)) Theorem (GHM04) G 1 is equivalent to G 2 iff nf(g 1 ) = nf(g 2 ). G 1 = G 2 iff G 2 nf(g 1 ) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 23 / 54

59 A normal form for RDF graphs To reduce the size of the materialization, we can combine both core and closure. nf(g) = core(closure(g)) Theorem (GHM04) G 1 is equivalent to G 2 iff nf(g 1 ) = nf(g 2 ). G 1 = G 2 iff G 2 nf(g 1 ) Complexity The problem of deciding if G 1 = nf(g 2 ) is DP-complete. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 23 / 54

60 Querying RDF Data in practice SPARQL is the W3C candidate recommendation query language for RDF. SPARQL is a graph-matching query language. A SPARQL query consists of three parts: Pattern matching: optional, union, nesting, filtering. Solution modifiers: projection, distinct, order, limit, offset. Output part: construction of new triples,.... M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 24 / 54

61 A simple RDF query language SELECT?Name? WHERE {?X :name?name?x : ? } M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 25 / 54

65 A simple RDF query language SELECT?Name? WHERE {?X :name?name?x : ? } In general, in a query we have: H Head: processing of some variables. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 25 / 54

66 A simple RDF query language SELECT?Name? WHERE {?X :name?name?x : ? } In general, in a query we have: H P Head: processing of some variables. Body: pattern matching expression. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 25 / 54

67 A simple RDF query language SELECT?Name? WHERE {?X :name?name?x : ? } In general, in a query we have: H P Head: processing of some variables. Body: pattern matching expression. We focus on P. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 25 / 54

68 But things can become more complex... Interesting features of pattern matching on graphs Grouping Optional parts Nesting Union of patterns Filtering... { P1 P2 } M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 26 / 54

69 But things can become more complex... Interesting features of pattern matching on graphs Grouping Optional parts Nesting Union of patterns Filtering... { { P1 P2 } } { P3 P4 } M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 26 / 54

70 But things can become more complex... Interesting features of pattern matching on graphs Grouping Optional parts Nesting Union of patterns Filtering... { { P1 P2 OPTIONAL { P5 } } } { P3 P4 OPTIONAL { P7 } } M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 26 / 54

71 But things can become more complex... Interesting features of pattern matching on graphs Grouping Optional parts Nesting Union of patterns Filtering... { { P1 P2 OPTIONAL { P5 } } } { P3 P4 OPTIONAL { P7 OPTIONAL { P8 } } } M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 26 / 54

72 But things can become more complex... Interesting features of pattern matching on graphs Grouping Optional parts Nesting Union of patterns Filtering... { { P1 P2 OPTIONAL { P5 } } { P3 P4 OPTIONAL { P7 OPTIONAL { P8 } } } } UNION { P9 } M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 26 / 54

73 But things can become more complex... Interesting features of pattern matching on graphs Grouping Optional parts Nesting Union of patterns Filtering... { { P1 P2 OPTIONAL { P5 } } { P3 P4 OPTIONAL { P7 OPTIONAL { P8 } } } } UNION { P9 FILTER ( R ) } M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 26 / 54

74 But things can become more complex... Interesting features of pattern matching on graphs Grouping Optional parts Nesting Union of patterns Filtering... { { P1 P2 OPTIONAL { P5 } } { P3 P4 OPTIONAL { P7 OPTIONAL { P8 } } } } UNION { P9 FILTER ( R ) } M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 26 / 54

75 A formal semantics for SPARQL is needed. A formal approach would be beneficial for: Clarifying corner cases Helping in the implementation process Providing sound foundations M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 27 / 54

76 A formal semantics for SPARQL is needed. A formal approach would be beneficial for: Clarifying corner cases Helping in the implementation process Providing sound foundations Our primary interest M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 27 / 54

77 A formal semantics for SPARQL is needed. A formal approach would be beneficial for: Clarifying corner cases Helping in the implementation process Providing sound foundations Our primary interest In our work: A formal compositional semantics (for simple RDF) Complexity bounds Optimization procedures M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 27 / 54

78 A standard algebraic syntax Triple patterns: just triples + variables, without blanks?x :name "john" (?X, name, john) Graph patterns: full parenthesized algebra { P1 P2 } (P 1 AND P 2 ) { P1 OPTIONAL { P2 }} (P 1 OPT P 2 ) { P1 } UNION { P2 } (P 1 UNION P 2 ) { P1 FILTER ( R ) } (P 1 FILTER R ) original SPARQL syntax algebraic syntax M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 28 / 54

79 A standard algebraic syntax Explicit precedence/association Example { t1 t2 OPTIONAL { t3 } OPTIONAL { t4 } t5 } ((((t 1 AND t 2 ) OPT t 3 ) OPT t 4 ) AND t 5 ) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 29 / 54

80 Mappings: building block for the semantics Definition A mapping is a partial function from variables to RDF terms. The evaluation of a pattern results in a set of mappings. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 30 / 54

81 Mappings: building block for the semantics Definition A mapping is a partial function from variables to RDF terms. The evaluation of a pattern results in a set of mappings. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 30 / 54

82 The semantics of triple patterns Given an RDF graph and a triple pattern t Definition The evaluation of t is the set of mappings that M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 31 / 54

83 The semantics of triple patterns Given an RDF graph and a triple pattern t Definition The evaluation of t is the set of mappings that make t to match the graph M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 31 / 54

84 The semantics of triple patterns Given an RDF graph and a triple pattern t Definition The evaluation of t is the set of mappings that make t to match the graph have as domain the variables in t. M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 31 / 54

85 The semantics of triple patterns Given an RDF graph and a triple pattern t Definition The evaluation of t is the set of mappings that make t to match the graph have as domain the variables in t. Example graph triple evaluation (R 1, name, john)?x?y (R 1, , J@ed.ex) (?X, name,?y ) µ 1 : R 1 john (R 2, name, paul) µ 2 : R 2 paul M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 31 / 54

88 Compatible mappings Definition Two mappings are compatible if they agree in their shared variables. Example?X?Y?Z?V µ 1 : R 1 john µ 2 : R 1 J@edu.ex µ 3 : P@edu.ex R 2 M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 32 / 54

89 Compatible mappings Definition Two mappings are compatible if they agree in their shared variables. Example?X?Y?Z?V µ 1 : R 1 john µ 2 : R 1 J@edu.ex µ 3 : P@edu.ex R 2 M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 32 / 54

90 Compatible mappings Definition Two mappings are compatible if they agree in their shared variables. Example?X?Y?Z?V µ 1 : R 1 john µ 2 : R 1 J@edu.ex µ 3 : P@edu.ex R 2 µ 1 µ 2 : R 1 john J@edu.ex M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 32 / 54

91 Compatible mappings Definition Two mappings are compatible if they agree in their shared variables. Example?X?Y?Z?V µ 1 : R 1 john µ 2 : R 1 J@edu.ex µ 3 : P@edu.ex R 2 µ 1 µ 2 : R 1 john J@edu.ex M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 32 / 54

92 Compatible mappings Definition Two mappings are compatible if they agree in their shared variables. Example?X?Y?Z?V µ 1 : R 1 john µ 2 : R 1 J@edu.ex µ 3 : P@edu.ex R 2 µ 1 µ 2 : R 1 john J@edu.ex µ 1 µ 3 : R 1 john P@edu.ex R 2 M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 32 / 54

93 Compatible mappings Definition Two mappings are compatible if they agree in their shared variables. Example?X?Y?Z?V µ 1 : R 1 john µ 2 : R 1 J@edu.ex µ 3 : P@edu.ex R 2 µ 1 µ 2 : R 1 john J@edu.ex µ 1 µ 3 : R 1 john P@edu.ex R 2 µ 2 and µ 3 are not compatible M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 32 / 54

94 Sets of mappings and operations Let M 1 and M 2 be sets of mappings: Definition M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 33 / 54

95 Sets of mappings and operations Let M 1 and M 2 be sets of mappings: Definition Join: M 1 M 2 extending mappings in M 1 with compatible mappings in M 2 M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 33 / 54

96 Sets of mappings and operations Let M 1 and M 2 be sets of mappings: Definition Join: M 1 M 2 extending mappings in M 1 with compatible mappings in M 2 Difference: M 1 M 2 mappings in M 1 that cannot be extended with mappings in M 2 M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 33 / 54

97 Sets of mappings and operations Let M 1 and M 2 be sets of mappings: Definition Join: M 1 M 2 extending mappings in M 1 with compatible mappings in M 2 Difference: M 1 M 2 mappings in M 1 that cannot be extended with mappings in M 2 Union: M 1 M 2 mappings in M 1 plus mappings in M 2 (set theoretical union) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 33 / 54

98 Sets of mappings and operations Let M 1 and M 2 be sets of mappings: Definition Join: M 1 M 2 extending mappings in M 1 with compatible mappings in M 2 Difference: M 1 M 2 mappings in M 1 that cannot be extended with mappings in M 2 Union: M 1 M 2 mappings in M 1 plus mappings in M 2 (set theoretical union) Definition Left Outer Join: M 1 M 2 = (M 1 M 2 ) (M 1 M 2 ) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 33 / 54

99 Semantics of SPARQL operators Let M 1 and M 2 be the result of evaluating P 1 and P 2. Definition The evaluation of: (P 1 AND P 2 ) (P 1 UNION P 2 ) (P 1 OPT P 2 ) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 34 / 54

100 Semantics of SPARQL operators Let M 1 and M 2 be the result of evaluating P 1 and P 2. Definition The evaluation of: (P 1 AND P 2 ) M 1 M 2 (P 1 UNION P 2 ) (P 1 OPT P 2 ) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 34 / 54

101 Semantics of SPARQL operators Let M 1 and M 2 be the result of evaluating P 1 and P 2. Definition The evaluation of: (P 1 AND P 2 ) M 1 M 2 (P 1 UNION P 2 ) M 1 M 2 (P 1 OPT P 2 ) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 34 / 54

102 Semantics of SPARQL operators Let M 1 and M 2 be the result of evaluating P 1 and P 2. Definition The evaluation of: (P 1 AND P 2 ) M 1 M 2 (P 1 UNION P 2 ) M 1 M 2 (P 1 OPT P 2 ) M 1 M 2 M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 34 / 54

103 Simple example Example (R 1, name, john) (R 1, , (R 2, name, paul) ((?X, name,?y ) OPT (?X, ,?e)) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 35 / 54

104 Simple example Example (R 1, name, john) (R 1, , (R 2, name, paul) ((?X, name,?y ) OPT (?X, ,?e)) M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 35 / 54

105 Simple example Example (R 1, name, john) (R 1, , (R 2, name, paul) ((?X, name,?y ) OPT (?X, ,?e))?x?y R 1 john paul R 2 M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 35 / 54

106 Simple example Example (R 1, name, john) (R 1, , (R 2, name, paul) ((?X, name,?y ) OPT (?X, ,?e))?x?y R 1 john paul R 2 M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 35 / 54

107 Simple example Example (R 1, name, john) (R 1, , (R 2, name, paul) ((?X, name,?y ) OPT (?X, ,?e))?x?y R 1 john paul R 2?X?E R 1 J@ed.ex M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 35 / 54

108 Simple example Example (R 1, name, john) (R 1, , (R 2, name, paul) ((?X, name,?y ) OPT (?X, ,?e))?x?y R 1 john paul R 2?X?E R 1 J@ed.ex M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 35 / 54

109 Simple example Example (R 1, name, john) (R 1, , (R 2, name, paul) ((?X, name,?y ) OPT (?X, ,?e))?x?y R 1 john paul R 2?X?Y?E R 1 john J@ed.ex R 2 paul?x?e R 1 J@ed.ex M. Arenas SPARQL: Un Lenguaje de Consulta para la Web Semántica 35 / 54