RDF Storage and Retrieval Systems. Jan Pettersen Nytun, UiA

Transcription

1 RDF torage and Retrieval ystems Jan Pettersen Nytun, UiA 1

2 Following lides - Ref.: Chapter 4 emantic Web application architecture - emantic Web for the Working ntologist, econd Edition: Effective Modeling in RDF and WL, May 20, 2011, by Dean Allemang, James Hendler Jan Pettersen Nytun, UiA, ntologies, page 2

3 RDF TRE An RDF store typically includes a query engine (PARQL). RDF allows easy merging of data sets (in contrast to relational data stores). RDF stores comes in many flavors: Custom programmed database solutions, fully supported off-the-shelf products, Jan Pettersen Nytun, UiA, page 3

4 Conceptually the simplest relational implementation of a triple store A triple store is designed to store and retrieve triple collections of strings (i.e., ubject-predicate-bject statements). Each RDF statement is stored as a single row in a three column table. ince this fits in a relational database representation, it can be accessed using conventional relational database tools such as QL. Jan Pettersen Nytun, UiA, ntologies, page 4

5 Jan Pettersen Nytun, UiA, ntologies, page 5

6 RDF data standards and interoperability of RDF stores Relational data stores: Difficult process to transfer a whole database from one system to another. RDF tores: All have the underlying RDF data model and support RDF/XML and/or Turtle. Easy to transfer an RDF data set. Jan Pettersen Nytun, UiA, ntologies, page 6

7 The common standards also simplifies the issue of federating data that are housed in multiple RDF stores, possibly coming from different vendor sources. Jan Pettersen Nytun, UiA, ntologies, page 7

8 RDF query engines The PARQL query language includes a protocol for communicating queries and results so that a query engine can act as a web service. PARQL endpoints provide access to large amounts of structured RDF data. It is even possible to provide PARQL access to databases that are not triple stores, e.g., PARQL translated to QL. Jan Pettersen Nytun, UiA, ntologies, page 8

9 DATA FEDERATIN The RDF data model was designed from the beginning with data federation in mind. Information from any source is converted into a set of triples so that data federation of any kind spreadsheets and XML, database tables and web pages is accomplished with a single mechanism. Jan Pettersen Nytun, UiA, ntologies, page 9

10 PARQL 1.1 Federated Query Extension - ERVICE This extension allows a query author to direct a portion of a query to a particular PARQL endpoint. Results are returned to the federated query processor and are combined with results from the rest of the query. Jan Pettersen Nytun, UiA, ntologies, page 10

11 PREFIX : < PREFIX dc: < ELECT?a FRM <mybooks.rdf> {?b dc:title?title. ERVICE < {?s dc:title?title.?s dc:creator?a } } Jan Pettersen Nytun, UiA, ntologies, page 11

12 RDF tore (Merge) Jan Pettersen Nytun, UiA, ntologies, page 12

13 More Details Following lides Ref. article: RDF torage and Retrieval ystems Alice Hertel, Jeen Broekstra, and Heiner tuckenschmidt Found in: teffen taab - Rudi tuder (Eds.) Handbook on ntologies econd Edition Jan Pettersen Nytun, UiA, ntologies, page 13

14 Possible Generic Architecture of an RDF tore Jan Pettersen Nytun, UiA, page 14

15 Admin Module Gives functionality for adding and deleting data from the RDF store. Loading data from files requires parsing and validating RDF, consequently an RDF parser and an RDF validator are usually part of the admin module. Jan Pettersen Nytun, UiA, ntologies, page 15

16 Query Module and Export Module Query module handles queries to the RDF store. Implements a parser and handler for one query language. Export module allows a dump of the RDF store into files for data exchange with other systems. Jan Pettersen Nytun, UiA, ntologies, page 16

17 The modules can be accessed locally or remotely, e.g. using AP or RMI. This is why the highest layer in the middleware contains protocol handlers that can manage different access modes. Jan Pettersen Nytun, UiA, ntologies, page 17

18 toring RDF data in a relational database requires an appropriate table design. There are different approaches that can be classified in generic schemas, i.e. schemas that do not depend on the ontology, and ontology specific schemas. Jan Pettersen Nytun, UiA, ntologies, page 18

19 imples Generic chema: ne table with three columns named ubject, Predicate and bject Advantage: No restructuring is required if the ontology changes (e.g., new classes, etc., realized by a simple INERT command in the table). Disadvantage: Performing a query means searching the whole database and queries involving joins become very expensive. Jan Pettersen Nytun, UiA, ntologies, page 19

20 Normalized triple store a more advanced generic schema Requires significantly less storage space. ne may also split the Triples table into several tables based on the RDF properties, e.g., a separate table for RDF rdfs:subclassf. Jan Pettersen Nytun, UiA, ntologies, page 20

21 ntology pecific chemas ntology specific schemas are changing when the ontology changes, i.e. when classes or properties are added or removed. The basic schema consists of one table with one column for the instance ID, one for the class name and one for each property in the ontology. Thus, one row in the table corresponds to one instance. ome sort of one-table-per-property and/or onetable-per class schema. Jan Pettersen Nytun, UiA, ntologies, page 21