RDF Storage and Retrieval Systems. Jan Pettersen Nytun, UiA

Transcription

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

2 P Federating Data [1] The common standards simplifies the issue of federating data that are housed in multiple RDF stores, possibly coming from different vendor sources. Jan Pettersen Nytun, UiA, page 2

3 P DATA FEDERATIN [1] 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, page 3

4 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, page 4

5 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, page 5

6 P RDF tore [1] Jan Pettersen Nytun, UiA, page 6

7 Jan Pettersen Nytun, UiA, page 7 P Possible Generic Architecture of an RDF tore [2]

8 Admin Module [2] 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, page 8

9 Query Module and Export Module [2] 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, page 9

10 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. [2] Jan Pettersen Nytun, UiA, page 10

11 [3] P Jan Pettersen Nytun, UiA, ntologies, page 11

12 P RDF TRE [1] 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 12

13 Conceptually the simplest relational implementation of a triple store [1] 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, page 13

14 [1] Jan Pettersen Nytun, UiA, page 14

15 ne table with three columns named ubject, Predicate and bject Continues 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, page 15

16 Normalized triple store a more advanced generic schema [2] Requires significantly less storage space. Jan Pettersen Nytun, UiA, page 16

17 References [1] 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 [2] RDF torage and Retrieval ystems Alice Hertel, Jeen Broekstra, and Heiner tuckenschmidt Found in: teffen taab - Rudi tuder (Eds.) Handbook on ntologies, econd Edition [3] Understanding Computers: Today and Tomorrow, 13th Edition Chapter 14: Databases and Database Management ystems. Published by onny yms Jan Pettersen Nytun, UiA, page 17