1 / 38

RAL: an R DF Al gebra

RAL: an R DF Al gebra. Flavius Frasincar Geert-Jan Houben Richard Vdovjak Peter Barna. Contents. Introduction RAL Goals RAL Data Model RAL Operators Conclusion. 1. Introduction. Metadata is machine understandable information about web resources or other things

jana-bryant
Télécharger la présentation

RAL: an R DF Al gebra

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. RAL: an RDF Algebra Flavius Frasincar Geert-Jan Houben Richard Vdovjak Peter Barna WISE 2002

  2. Contents • Introduction • RAL Goals • RAL Data Model • RAL Operators • Conclusion WISE 2002

  3. 1. Introduction • Metadata is machine understandable information about web resources or other things [Source: Tim Berners-Lee, “Metadata Architecture”] • RDF (Resource Description Framework) is the Web metadata language for the Web • RDF extends the syntactic interoperability of XML to semantic interoperability being the foundation for the Semantic Web WISE 2002

  4. Semantic Web Architecture “Layer Cake” [Source: Tim Berners-Lee Director W3C Keynote speech at XML2000 “RDF and the Semantic Web” (Washington DC, 6 Dec. 2000)] WISE 2002

  5. Hera • Hera research project: Web Information Systems (WIS) and web (hypermedia) generation in WIS • WIS use RDF to represent and query application data for: • Semantic integration of data coming from heterogeneous sources • Semantic information presentation • Semantic querying • Huge quantities of data and metadata need to be processed in real-time: optimization is crucial WISE 2002

  6. Hera Methodology/Suite WISE 2002

  7. Rembrandt painted_by http://example.com/sb.jpg RDF Representations Primitive semantics: Subject Predicate Object Three alternative notations: • Triple (http://example.com/sb.jpg, painted_by, “Rembrandt”) • RDF/XML<rdf:Description rdf:ID=http://example.com/sb.jpg> <painted_by> Rembrandt </painted_by> </rdf:Description> • Graph WISE 2002

  8. RDF Query Languages • Triple-based: • Triple [successor of SiLRI] (Horn logic) • Metalog (Datalog) • XML-based: • RDF Query • RQuery (XQuery) • Graph-based (but not graphical): • RQL (OQL) WISE 2002

  9. 2. RAL Goals • Support the formal specification of RDF query languages • Provide a reference framework to compare different RDF query languages • Consider the result construction phase • presently neglected by RDF query languages which focus only on extraction • Enable algebraic query optimization WISE 2002

  10. RAL • RAL Data Model: specify what information is accessible (for RAL operators) in an RDF graph • Nodes: Resources and Literals • Edges: Properties • RAL Operators: define operators working on collections of nodes from the RAL Data Model • Extraction Operators • Loop Operators • Construction Operators WISE 2002

  11. rdf:Property rdf:type 3. RAL Data Model • R is the set of resources R = U  B • U is the set of URI references rdf:Property U • B is the set of blank nodes • L is the set of literals U, B, L are disjoint • P is the set of properties P  R, rdf:type P R L U B P WISE 2002

  12. An RDF model M is a finite set of triples(statements) M  R  U  (R  L) • The set of properties of an RDF model M PM = {p| (s, p, o)  M  (p, rdf:type, rdf:Property)  M} • The RDF graph model is similar to a directed labeled graph (DLG) • It is not a DLG since it allows for multiple edges between two nodes • It is not a general multigraph because different edges between two nodes cannot share the same label WISE 2002

  13. The RDFgraph model corresponding to an RDF model M is defined by • GM= (N, E, lN , lE), lN: N  R L, lE: E P • using the following construction mechanism: • for each (s, p, o)  M • add nodes ns, no to N (different only if s  o) • assign lN (ns) = s, lN (no) = o • add ep to E as a directed edge between ns and no • assign lE (ep ) = p • Observations: • lN (.) is an injective partial function • lE (.) is a total function WISE 2002

  14. Basic Properties Edges Nodes • Two non-blank nodes are equal if they have the same id • Twoblank nodes are equal if they have the same properties • and the corresponding property values are equal WISE 2002

  15. RDF(S)-Closure • RDF Model Theory defines the RDF-closure and RDFS-closure of an RDF Model M by proposing a set of rules for generating new triples • Extensional data: the original model M triples • Intensional data: the new triples generated by the RDF(S)-closure • RAL operators work on extensional+intensional data • Variants of the operators can be defined to neglect the intensional data (similar to the RQL strict interpretation) WISE 2002

  16. 4. RAL Operators • All operators have the following form o[f](x1, x2, … xn: expression) wherean expression is a collection of nodes and f is a function having as input/output collection of nodes • Extraction Operators: retrieve the needed information from an RDF graph • Loop Operators: control the repetitive application of certain operators • Construction Operators: build new RDF graphs from the extracted data WISE 2002

  17. WISE 2002

  18. 4.1 Extraction Operators Projection [re_name](e: expression) computes the values of the properties with a name given by the regular expression re_name over strings on the input collection given by e Example [(P|p)aint[s]#](r4) returns the resources painted by r4 WISE 2002

  19. WISE 2002

  20. Selection [condition](e: expression) selects input collection nodes fulfilling the given condition Example [[tname] = “Chiaroscuro”](c) where c is the collection of input resourcesr1, r2, r3, and r4, returns the resources representing the painting technique with the name“Chiaroscuro” WISE 2002

  21. WISE 2002

  22. Cartesian Product • (x: expression)  (y: expression) • for each element in the Cartesian product of the input collections, a blank node that has all properties of both originating nodes is added to the result • Example • [[rdf:type] = Technique](c)[[rdf:type] = Painter](c) • returns a collection of blank nodes, each blank node having all the properties of the corresponding pair from the Cartesian product(the new nodes have both types Technique and Painter) WISE 2002

  23. WISE 2002

  24. Join (x: expression) ⋈[condition] (y: expression)  [condition](x  y) is a Cartesian product followed by a selection Example (x:[[rdf:type] = Technique](c)) ⋈[[exemplified_by](x) = [paints](y)] (y: [[rdf:type] = Painter](c)) returns a collection of blank nodes, each blank node having all the properties of the corresponding pair from the Cartesian productthat satisfies the given condition WISE 2002

  25. WISE 2002

  26. Union, Difference, Intersection (x: expression)  (y: expression) where  {, , } defined as in set theory Example [[rdf:type] = Technique](c)[[rdf:type] = Painter](c), returns the collection of resources obtained by combining the two collections (these two collections are obtained using two selections) WISE 2002

  27. WISE 2002

  28. 4.2 Loop Operators Map map[f](e: expression) applies the function f to each element of the input collection; the function results are added in the output collection Example map[[rdfs:subClassOf]](Painting, Painter) computes the parent classes using the property rdfs:subClassOf for the collection consisting of Painting and Painter WISE 2002

  29. WISE 2002

  30. WISE 2002

  31. Kleene Star [f](e: expression) repeats the function f possibly infinite times starting with the given input collection; at each iteration the results of the function are added to the next function input Example [[rdfs:subClassOf]](Painting)) computes the transitive closure of the property rdfs:subClassOf starting from Painting, i.e. Painting and all its superclasses WISE 2002

  32. WISE 2002

  33. 4.3 Construction Operators Create Node node[type, id]() adds a new node to the graph with the given type and id (id is missing for blank nodes) and returns this node; if a resource is created, an rdf:type edge is added between the resource and the node representing rdfs:Resource The Create Node operator assigns a unique (in the resulted RDF graph) internal identifier for each created node WISE 2002

  34. Caravagio rdfs:Resource rdf:type Example node[Resource]() and node[Literal,“Caravagio”]() create a Resource representing a blank node and a Literal representing the string “Caravagio” WISE 2002

  35. Create Edge edge[name, subject](object: expression) adds edges between the subject node and each of the nodes in the object collection, and returns the subject node; the label of the edges is given by name which is the id of a property resource The Create Node and Create Edge operators abort if the “well-formed RDF(S) graph” conditions (e.g. rdf:type cannot refer to a literal, literals cannot have properties etc.) are not met after construction WISE 2002

  36. Caravagio rdfs:Resource rdf:type name Example edge[name, node[Resource]()](node[Literal, ”Caravagio”]()) creates an edge labeled with name between the nodes defined in the previous example WISE 2002

  37. 5. Conclusion • The RAL algebra is developed from a DB perspective and proposes a set of operators similar to their relational algebra counterparts: • Extraction Operators: Projection, Selection, Cartesian Product, Join, Union, Difference, Intersection • Similar to the existing semi-structured query languages RAL considers powerful repetition operators: • Loop Operators: Map, Kleene Star • As opposed to present RDF query languages RAL supports result construction: • Construction Operators: Create Node, Create Edge WISE 2002

  38. Future Work • Analyze the power of expression of RAL compared to RQL, a popular RDF query language at present time (build a translation scheme from RQL to RAL) • Formally specify the semantics of other RDF query languages in terms of RAL • Compare the power of expression of different RDF query languages using RAL as reference language • Explore equivalence rules for RAL expressions to be used in query optimization • Develop an RDF query optimization algorithm on RAL WISE 2002

More Related