SPARQL and SQL: technical aspects and synergy

SPARQL and SQL technical aspects and synergy
Relational model
flat model, tabular form, typing is implicit (by convention, column definition)
JOIN functions used to combine information from tables
use of foreign keys to add semantics and graph-like structures
each table has many columns = many attributes of objects
RDF model
may be viewed as flat
but because it is a graph can also be traversed hierarchically or arbitrarily
explicit typing is common (by namespace definition)
URIs instead of closed database table names
explicit relationships via predicates (triple format: subj, pred, obj)
relationships and references are the key point
SPARQL
designed to query collections of triples and to easily traverse relationships
syntax vaguely resembles SQL (SELECT, WHERE, etc)
it matches graph patterns
SPARQL and SQL
SPARQL can be translated to SQL

SPARQL “under the hood”
Works with popular web and web services protocols: HTTP and SOAP
XML results format (easy to transform, XSLT, XQuery)
A SPARQL query consists of three parts:
Pattern matching (OPTIONAL, UNION, FILTER)
Solution modifiers (PROJECTION, DISTINCT, OFFSET, LIMIT)
Output part CONSTRUCT of new triples
BGP (Basic Graph Pattern) built-in mechanism
Can work in conjunction with RDFS ’s taxonomic reasoning (respects RDFS type checking and subsumption thus providing inference)
Inference system in RDFS (and through SPARQL) supports existential, sub-property, sub-class, and typing (implicit and explicit) inferences

SPARQL applications, engines, endpoints
RDF engines (SPARQL to interrogate them)
Oracle (will support SPARQL syntax), Allegrograph, OpenLink Virtuoso, ARQ/Joseki (HP), Boca (IBM), Rasqal for Redland, SWI-Prolog, Sesame, D2R Server
Notable SPARQL users & applications (R&D and commercial)
Garlik: SPARQL to built reports on people’s online credit reports, 500-2000 SPARQL queries to build a report, 1-2 seconds processing time, 8 big knowledge bases (>2bn triples), XML format)
JSpace: an extension to mSpace, UI driven SPARQL firing, digital music domain
POPS: expertise location service for NASA, federates 4 diverse data sources, 4.5M triples, pilot study for the Office of the Chief Engineer
BIANCA: network asset management service, integrated view of applications, servers, networks and changes and their relations, deployed at NASA
HCLS (Health Care and Life Sciences) interest group at W3C, 60 organisations members, Google maps based interface for Allen Brain Atlas, 20k genes, 400k images.
Further resources and point of info:
http://esw.w3.org/topic/SparqlImplementations

SPARQL and the Semantic Web - technical
Relation of SPARQL to rules for the Semantic Web
Rules: several proposals for rule systems usable on top of RDF/RDF (SWRL, N3, TRIPLE, SWI-Prolog, WSML, etc)
Only a handful through W3C standardisation (SWRL) and already working on RIF (for interchanging rules)
SPARQL is a query language (primarily) but can be used as a rule language (translation work underway for SPARQL to LP (Datalog) )
Example the RDFS entailment rule that can be expressed with SPARQL CONSTRUCT clause
RIF charter states that it will be compatible with SPARQL

SPARQL and SQL technical aspects and synergy
SQL
Great for finding data from tabular representations, can get complex when many tables are involved in a given query
SPARQL
Good pattern matching paradigm, especially when relationships have to be used to answer a query
Also add here: work on BGP for translating SPARQL to SQL (technical paper)
Federated SPARQL endpoints and SPARQL validators, query resolvers, SPARQL APIs
Use of SPARQL CONSTRUCTS as opposed to rules and inference engines
Argument for SPARQL overarching SQL schemata: reduces computational workload with having to describe complex JOIN SQL queries; common namespaces and a match to underlying SQL schemata is the only workload
Syndication and verticals to agree on common namespaces? Abstract layer from which to access merchant specific DBs
Check sylvia’s slides from WSRI seminar – scalability of SPARQL and use cases – lack of SUM function, etc. – check also my slides
Some examples of SPARQL at work? From the recent NaTii experiment and from e-response - show them data provenance and typing examples (use of namespaces)

SPARQL and SQL: technical aspects and synergy

by Yannis Kalfoglou

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