Semantic Web
&
Semantic Web Processes
A course at Universidade da Madeira, Funchal, Portugal
June 16-18, 2005
Dr. Amit P. Sheth
Professor, Computer Sc., Univ. of Georgia
Director, LSDIS lab
CTO/Co-founder, Semagix, Inc
Special Thanks: Cartic Ramakrishnan, Karthik Gomadam
Agenda
Part I
• What is Semantic Web?
• What makes the Semantic web
• Ontologies – importance of relationships and knowledge
• Representation and Languages
• Why XML is not enough
• Describe semantic web resources- RDF and RDFS
• OWL
• Query processing and storage
Part II
• Metadata, Enabling techniques and technologies
• Ontology and knowledge engineering: ontology design,
ontology population maintaining, ontology freshness
• Automated metadata extraction and annotation
• Computation and reasoning with focus on relationships
• Example commercial Semantic Web platform
1
Agenda
2
Part III
• Semantic web applications: search, integration, analysis
a. Pan-Web and consumer-centric
b. Enterprise
Part IV
• Semantic Web Services and Processes
•
•
•
•
What are Web Services ?
What are Web processes ?
Creating Web processes: Annotation, discovery,
composition, etc.
Semantic Web Service/Process tools
Part I
• What is Semantic Web?
• What makes the Semantic web
• Ontologies – importance of relationships and knowledge
• Types and examples of ontologies
• Metadata and Semantic Annotation -- metadata
classifications
• Representation and Languages
• Why XML is not enough
• RDF - Describe semantic web resources and RDFS - RDF
as a triple, RDF as a graph (show example RDF/S)
• OWL
• RDF Query processing and storage
Three generation of Information Systems:
Where we have come from, where we are going
Semantics
Generation III
2000s
MediaAnywhere
InfoQuilt,
OBSERVER , Semantic Web technologies and platforms
Semagix Freedom
Metadata
Generation II
1990s
Generation I
1980s
(Ontology, Context, Relationships, KB)
VisualHarness
InfoHarness
(Domain model)
Metadata based integration, Mediator
Systems, Digital Libraries
AdaptX/Harness
Data (Schema, “semantic data modeling)
Mermaid
DDTS
Intervisio
Heterogeneous databases/
Federated Databases Research
Semi-Formal
Informal
Degree of Agreement
Formal
Other dimensions:
how agreements are reached,
…
Broad Scope of
Semantic (Web)
Technology
Current Semantic
Web Focus
Lots of
Useful
Semantic
Technology
(interoperability,
Integration)
Qos
Execution
Scope of Agreement
Task/
App
Domain
Industry
Gen.
Purpose,
Broad Based
Function
Common
Sense
Data/
Info.
Cf: Guarino, Gruber
What is the 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." -Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic
Web, Scientific American, May 2001
• Ontologies
• RDF/RDFS or OWL Syntax – machine
processable
• Semantic Metadata – annotation of web
resources
“An ontology is a specification of a
conceptualization” (T. Gruber)
• A conceptualization is the way we think
about a domain
• A specification provides a formal way of
writing it down
Building Ontologies from the Ground Up
When users set out to model their professional activity – Mark Mussen
Conceptualization and Ontology
Everything that can
be expressed in the
language
http://www.w3c.it/events/minerva20040706/guarino.pdf
Ontology
Constraining
Possible
Interpretations
Of what can
Be expressed
Central Role of Ontology
• Ontology represents agreement, represents
common terminology/nomenclature
• Ontology is populated with extensive domain
knowledge or known facts/assertions
• Key enabler of semantic metadata extraction from
all forms of content:
– unstructured text (and 150 file formats)
– semi-structured (HTML, XML) and
– structured data
• Ontology is in turn the center piece that enables
– resolution of semantic heterogeneity
– semantic integration
– semantically correlating/associating objects and
documents
Types of Ontologies (or things close to
ontology)
• Upper ontologies: modeling of time, space, process, etc
• Broad-based or general purpose ontology/nomenclatures: Cyc,
CIRCA ontology (Applied Semantics), SWETO, WordNet ;
• Domain-specific or Industry specific ontologies
–
–
–
–
–
–
News: politics, sports, business, entertainment
Financial Market
Terrorism
Pharma
GlycO, ProPreO
(GO (a nomenclature), UMLS inspired ontology, …), MGED
–
–
–
–
Anti-money laundering
Equity Research
Repertoire Management
Financial irregularity
• Application Specific and Task specific ontologies
Fundamentally different approaches in developing ontologies
at the two end of the above spectrum
Building ontology
Three broad approaches:
• social process/manual: many years, committees
– Can be based on metadata standard
• automatic taxonomy generation (statistical clustering/NLP):
limitation/problems on quality, dependence on corpus,
naming
• Descriptional component (schema) designed by domain
experts; Description base (assertional component,
extension) using automated processes from trusted
knowledge sources
Option 2 is being investigated in several research projects;
Option 3 is currently supported by Semagix Freedom
SUMO -- http://ontology.teknowledge.com/
Part of the CYC Upper Ontology
http://www.cyc.com/cyc/technology/whatiscyc_dir/whatdoescycknow
SWETO (Semantic Web Testbed
Ontology) Current Status
• Developed using Semagix technology for free noncommercial usage by the SW community; some initial
users
• V1.4 population includes over 800,000 entities and over
1,500,000 explicit relationships among them
• Continue to populate the ontology with diverse sources
thereby extending it in multiple domains, new smaller and
larger release due soon; RDF and OWL versions
• Significant information for provenance/trust support [UMBC
partnership]
• 97% of disambiguation performed automatically, 2%
manually; not quite a high-quality as an evaluation testset
(e.g., low connectivity)
• Working on test harness, quality measures, and
benchmarks
Expressiveness Range: Knowledge
Representation and Ontologies
TAMBIS
KEGG
Thesauri
“narrower
term”
relation
Catalog/ID DB Schema
UMLS
Wordnet
Terms/
glossary
RDFS
OO
Formal
instance
SWETO
Disjointness,
Inverse,
part of…
Frames
(properties)
RDF
Informal
is-a
GO
Simple
Taxonomies
Formal
is-a
BioPAX
DAML
CYC
OWL
IEEE SUO
Value
Restriction
General
Logical
constraints
GlycO
Pharma
Expressive
Ontologies
Ontology Dimensions After McGuinness and Finin
EcoCyc
Gene Ontology (GO)
• Comprises three independent “ontologies”
– molecular function of gene products
– cellular component of gene products
– biological process representing the gene product’s
higher order role.
• Uses these terms as attributes of gene products in the
collaborating databases (gene product associations)
• Allows queries across databases using GO terms, providing
linkage of biological information across species
http://www.geneontology.org/
GO = Three Ontologies
• Molecular Function
– elemental activity or task
– example: DNA binding
• Cellular Component
– location or complex
– example: cell nucleus
• Biological Process
– goal or objective within cell
– example: secretion
http://www.geneontology.org/
GlycO
 GlycO: a domain Ontology embodying knowledge of the
structure and metabolisms of glycans
 Contains 770 classes – describe structural features of
glycans
 URL: http://lsdis.cs.uga.edu/projects/glycomics/glyco is
a focused ontology for the description of glycomics
• models the biosynthesis, metabolism, and
biological relevance of complex glycans
• models complex carbohydrates as sets of simpler
structures that are connected with rich
relationships
GlycO statistics: Ontology schema can be
large and complex
• 770 classes
• 142 slots
• Instances Extracted with Semagix Freedom:
–
–
–
–
–
–
–
69,516 genes (From PharmGKB and KEGG)
92,800 proteins (from SwissProt)
18,343 publications (from CarbBank and MedLine)
12,308 chemical compounds (from KEGG)
3,193 enzymes (from KEGG)
5,872 chemical reactions (from KEGG)
2210 N-glycans (from KEGG)
GlycO taxonomy
The first levels of
the GlycO
taxonomy
Most relationships
and attributes in
GlycO
GlycO exploits the
expressiveness of OWL-DL.
Cardinality constraints, value
constraints, Existential and
Universal restrictions on
Range and Domain of
properties allow the
classification of unknown
entities as well as the
deduction of implicit
relationships.
Query and visualization
A biosynthetic pathway
N-glycan_beta_GlcNAc_9
GNT-I
attaches GlcNAc at position 2
N-acetyl-glucosaminyl_transferase_V
N-glycan_alpha_man_4
GNT-V
attaches
GlcNAc at position 6
UDP-N-acetyl-D-glucosamine + alpha-D-Mannosyl-1,3-(R1)-beta-D-mannosyl-R2
<=>
UDP + N-Acetyl-$beta-D-glucosaminyl-1,2-alpha-D-mannosyl-1,3-(R1)-beta-D-mannosyl-$R2
UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021
The impact of GlycO
• GlycO models classes of glycans with
unprecedented accuracy
• Implicit knowledge about glycans can be
deductively derived
• Experimental results can be validated
according to the model
N-Glycosylation Process (NGP)
Cell CultureBy N-glycosylation Process,
extract
we mean the identification and
Glycoprotein Fraction
quantification of
proteolysis
glycopeptides
Glycopeptides Fraction
1
n
Separation technique I
Glycopeptides Fraction
n
PNGase
Peptide Fraction
Separation technique II
n*m
Peptide Fraction
Mass spectrometry
ms data
ms/ms data
Data reduction
ms peaklist
ms/ms peaklist
binning
Glycopeptide identification
and quantification
N-dimensional array
Signal integration
Data reduction
Peptide identification
Peptide list
Data correlation
ProPreO - Experimental Proteomics Process Ontology
 ProPreO models the phases of proteomics experiment using five
fundamental concepts:
 Data: (Example: a peaklist file from ms/ms raw data)
 Data_processing_applications: (Example: MASCOT* search
engine)
 Hardware: embodies instrument types used in proteomics
(Example: ABI_Voyager_DE_Pro_MALDI_TOF)
 Parameter_list: describes the different types of parameter
lists associated with experimental phases
 Task:
(Example:
chromatography)
*http://www.matrixscience.com/
component
separation,
used
in
Semantic Annotation of Scientific Data
830.9570 194.9604 2
580.2985 0.3592
688.3214 0.2526
779.4759 38.4939
784.3607 21.7736
1543.7476 1.3822
1544.7595 2.9977
1562.8113 37.4790
1660.7776 476.5043
ms/ms peaklist data
<ms/ms_peak_list>
<parameter
instrument=micromass_QTOF_2_quadropole_time_of_flight_m
ass_spectrometer
mode = “ms/ms”/>
<parent_ion_mass>830.9570</parent_ion_mass>
<total_abundance>194.9604</total_abundance>
<z>2</z>
<mass_spec_peak m/z = 580.2985 abundance = 0.3592/>
<mass_spec_peak m/z = 688.3214 abundance = 0.2526/>
<mass_spec_peak m/z = 779.4759 abundance = 38.4939/>
<mass_spec_peak m/z = 784.3607 abundance = 21.7736/>
<mass_spec_peak m/z = 1543.7476 abundance = 1.3822/>
<mass_spec_peak m/z = 1544.7595 abundance = 2.9977/>
<mass_spec_peak m/z = 1562.8113 abundance = 37.4790/>
<mass_spec_peak m/z = 1660.7776 abundance = 476.5043/>
<ms/ms_peak_list>
Annotated ms/ms peaklist data
Semantic annotation of Scientific Data
<ms/ms_peak_list>
<parameter
instrument=“micromass_QTOF_2_quadropole_time_of_flight_mass_s
pectrometer”
mode = “ms/ms”/>
<parent_ion_mass>830.9570</parent_ion_mass>
<total_abundance>194.9604</total_abundance>
<z>2</z>
<mass_spec_peak m/z = 580.2985 abundance = 0.3592/>
<mass_spec_peak m/z = 688.3214 abundance = 0.2526/>
<mass_spec_peak m/z = 779.4759 abundance = 38.4939/>
<mass_spec_peak m/z = 784.3607 abundance = 21.7736/>
<mass_spec_peak m/z = 1543.7476 abundance = 1.3822/>
<mass_spec_peak m/z = 1544.7595 abundance = 2.9977/>
<mass_spec_peak m/z = 1562.8113 abundance = 37.4790/>
<mass_spec_peak m/z = 1660.7776 abundance = 476.5043/>
<ms/ms_peak_list>
Annotated ms/ms peaklist data
Syntax for Onologies and Metadata
•
•
•
•
Why not use XML?
Why use OWL?
Or for that matter why RDF?
So many questions …
From XML to OWL
•
XML
•
XML Schema
•
RDF
•
RDF Schema
•
OWL
NO SEMANTICS
– surface syntax for structured documents
– imposes no semantic constraints on the meaning of these documents.
– is a datamodel for objects ("resources") and relations between them,
– provides a simple semantics for this datamodel
Relationships as
– these datamodels can be represented in an XML syntax. first class objects–
key to Semantics
– is a vocabulary for describing properties and classes of RDF resources
– with a semantics for generalization-hierarchies of such properties and classes.
– adds more vocabulary for describing properties and classes:
•
•
•
•
relations between classes (e.g. disjointness),
cardinality (e.g. "exactly one"),
SEMANTICS
equality, richer typing of properties,
characteristics of properties (e.g. symmetry), and enumerated classes.
http://en.wikipedia.org/wiki/Semantic_web#Components_of_the_Semantic_Web
Expressive Power
– is a language for restricting the structure of XML documents.
From an alphabet to a Language
• XML
– “XML is only the first step to ensuring that computers can
communicate freely. XML is an alphabet for computers and as
everyone traveling in Europe knows, knowing the alphabet
doesn’t mean you can speak Italian of French.” – Business
Week, March 18th 2002
– Example cited by Nicola Guarino in
http://www.w3c.it/events/minerva20040706/guarino.pdf
• RDF/RDFS and OWL would therefore be akin to the
language computers use to communicate
• And ontologies represented in these languages would be
akin to the exact interpretations of the concepts being
communicated
Syntax for Onologies and Metadata
• RDF
– A simple W3C standard used to describe Web
resources
– Relationships in RDF (Properties), are binary
relationships between two resources or a
resource and a literal
– Resources take on the roles of Subject and
Object respectively.
– The Subject, Predicate and Object compose an
RDF statement
http://www.w3.org/RDF/
What is RDF?
• Resource Description Framework
• Proposed as the base semantic web
language
• Data model for describing properties of
resources
• Statements about properties and values of
web resources
• Machine-understandable metadata
RDF Elements
• Resource:
– Something that can be described/referenced
– Identified by a URI
• Property:
– Relationship from a resource to a value:
• Another resource
• An atomic value/literal
• Statement:
– resource -> property -> value
RDF Statement
RDF Model
• Formal Data Model
– Directed labeled graph
• Nodes: resources or literals
• Edges: properties (relationships/attributes)
• Labels: URIs of nodes and edges
– Collection of triples
• subject (resource)
• predicate (property)
• object (resource or literal)
• W3C recommendation
Graph Model
Triple Model
Subject
Predicate
Object
Shaguille O’Neal plays_for
Miami Heat
Kobe Bryant
plays_for
LA Lakers
LA Lakers
competes_with
Miami Heat
Phil Jackson
coaches
LA Lakers
RDF Syntax
•
•
•
•
•
Formal syntax
Encoded in XML
Unambiguous property names and values
RDF adds rules for interpretation
W3C recommendation
Example
<sample:Athlete rdf:about="&sample;Kobe_Bryant">
<rdfs:label xml:lang="en">Kobe Bryant</rdfs:label>
<sample:plays_for rdf:resource="&sample;LA_Lakers"/>
</sample:Athlete>
<sample:Athlete rdf:about="&sample;Shaquille_ONeal">
<rdfs:label xml:lang="en">Shaquille O'Neal</rdfs:label>
<sample:plays_for rdf:resource="&sample;Miami_Heat"/>
</sample:Athlete>
<sample:Team rdf:about="&sample;LA_Lakers"
<rdfs:label xml:lang="en">LA Lakers</rdfs:label>
</sample:Team>
<sample:Team rdf:about="&sample;Miami Heat"
<rdfs:label xml:lang="en">Miami Heat</rdfs:label>
<sample:competes_with rdf:resource="&sample;LA_Lakers"/>
</sample:Team>
<sample:Coach rdf:about="&sample;sample1_Instance_8"
<rdfs:label xml:lang="en">sample1_Instance_8</rdfs:label>
<sample:coaches rdf:resource="&sample;LA_Lakers"/>
</sample:Coach>
What is RDFS?
• RDF Vocabulary Description Language
• (RDF Schema)
• Extension of RDF: same data model
– graph or triples
• A hierarchy of classes
• A hierarchy of properties relating classes
• W3C recommendation
RDF
Schema
RDF
Instances
Passenger
Ticket
subClassOf(isA)
number
for
String
Flight
String
FFlyer
Customer
fflierno
typeOf(instance)
String
purchased
String
subPropertyOf
Bank
Account
Payment
ffid
FFNo
float
String
CCard
&r4
Cash
Client
ffid “XYZ123”
&r11
“M’mmed”
&r1
purchased
&r2
paidby
&r3
“Atta”
&r5
“Marwan”
&r7
“Al-Shehhi”
lname
purchased
&r8
for
paidby
&r6
&r9
holder
RDFS Core Classes
• rdfs:Class
– Class of resources that are RDF classes
– Instance of rdfs:Class
• rdfs:Resource
– All things being described
– The class type of everything in RDF(S)
– Instance of rdfs:Class
• rdf:Property
– Class of RDF properties
– Instance of rdfs:Class
http://www.w3.org/TR/rdf-schema/
RDFS Core Properties
• rdfs:type
– A resource is an instance of a class
– Instance of rdf:Property
• rdfs:subClassOf
– All instances of a class are also instances of another
class
– Instance of rdf:Property
• rdfs:subPropertyOf
– All resources related by one property are also related by
another property
– Instance of rdf:Property
RDF Core Properties
• rdfs:range
– All values of a property are instances of one or more
class
• The value MUST be an instance of all range classes
– Instance of rdf:Property
• rdfs:domain
– All resources with the given property are instances of
one or more class
• The resource MUST be an instance of all domain classes
– Instance of rdf:Property
OWL, W3C definition
• “language for defining structured, Webbased ontology
which enables richer integration
and interoperability of data
across
application boundaries”
http://www.w3.org/2004/OWL/
OWL Use Cases
•
•
•
•
•
•
Web portals
Multimedia Collections
Corporate web site management
Design documentation
Agents and services
Ubiquitous computing
OWL Design Goals
•
•
•
•
•
•
•
•
Shared ontologies
Ontology evolution
Ontology interoperability
Inconsistency detection
Expressivity vs. scalability
Ease of use
Compatibility with other standards
Internationalization
What’s in OWL, but not in RDF
• Ability to be distributed across many
systems
– By means of owl:imports (similar to ‘include’ in
C/C++)
• Scalable to Web needs (?)
• Compatible with Web standards for:
– accessibility, and
– Internationalization
• Open and extensible
OWL open and extensible
• RDF Schema (meta-modeling facilities, i.e.
classes of classes)
• OWL Full
• OWL DL (Description Logics)
• OWL Lite
– targeting tool builders
owl:Class
• Sub class of Class in RDF
• Better to forget about classes of classes
• Top-most class: owl:Thing
OWL Properties
Object
Properties
Data type
Properties
Ana  owns  Cuba
Ana  age  25
Is range a
literal / typed value ?
then ERROR
• XML Schema data
types supported
– DB people happy
Transitivity of properties
X  p1  Y
Y  p1  Z
implies X  p1  Z
• Transitivity existed already in RDF
– “subClassOf”, and “subPropertyOf”
• Example: located_in
Atlanta
located_in Georgia
located_in U.S.A.
located_in
Symmetric properties
X  p1  Y
implies X  p1 Y
Spain
Portugal
has_border_with
has_border_with
Portugal
Spain
Functional Properties
X  p1  Y
X  p1  Z
imply Z is the same as Y
(they describe the same)
• example,
Portugal
Portugal
p1 = has_name
has_capital
&r1
has_capital
&r2
Result: &r1 and &r2
represent the
same entity
Inverse Functional Properties
Y  p1  A
Z  p1  A
imply Z is the same as Y
(they describe the same)
• example,
&r1:Tim Finin
&r2:Timothy Finin
p1 = has_email
has_email
has_email
[email protected]
Result: &r1 and &r2
represent the
same entity
OWL Cardinality
• min Cardinality
• max Cardinality
• “Cardinality”
– When
min = max
• has Value
– belongs to the class if it has the value
OWL Tools
• Pellet
(umd.edu)
– DLbased reasoner implemented in Java
• Euler
– an inference engine supporting logic based
proofs. Finds out whether a given set of facts
support a given conclusion
• FaCT
(Ian Horrocks)
– DL classifier that can also be used for modal
logic satisfiability testing
RDF Storages
•
•
•
•
•
•
•
•
•
•
Jena
Sesame
Redland
Triple
3store
RDFSuite
RDFStore
Kowari
Yars
Brahms
developed at LSDIS
• Variety of available
storages
• Different APIs and
languages
• Support from RDF to
OWL-full
– even reasoning
• Storage and query
approach: graph Vs.
triple-centric
http://www.w3.org/2001/sw/Europe/reports/rdf_scalable_storage_report/
Jena
•
•
•
•
•
Implemented in Java by HP Laboratories
Support for RDF, RDFS and OWL
Reasoning / inference engine
Support for reified statements
In-memory and persistent storage
(Oracle, MySQL, PostgreSQL)
• Query language: RDQL, SPARQL
• Read/write RDF in RDF/XML, N3 and N-Triples
format
• Triple-centric organization and API
Jena – graph abstraction
• Graph interface is
separated from
(persistent) triple
storage layer
• Special support for
different types of
graphs - optimized for
performance
• Support operations
like add, delete, find.
“Efficient RDF Storage and Retrieval in Jena2”
Kevin Wilkinson, Craig Sayers, Harumi Kuno, Dave Reynolds
Jena – query processing
• Converting multiple patterns in query into one query
to DB
• Use DB query optimizer instead of executing multiple
queries from Jena level
• Cluster properties that are likely to be accessed
together - optimize for common patterns
• Associate a table with pattern (best) or span pattern
between tables (requires join operation)
• Query may span between different graphs, but it can
be optimized only if they are in the same database
Redland, Rasqual, Raptor
• Storage for RDF triples - do not implement any
language by itself
• This is the main module to include in RDF
manipulation system
• Implemented in pure C for portability
• Rich API enables to build modules on top of it
• Rasqual - RDF query module
– RDQL
– SPARQL
• Raptor - a fast RDF parser
Redland
• API available in different languages
– C, C#, Java, Perl, Python, PHP, Ruby, Tcl
• API for manipulating
– triples, URI/literals, graphs
• Portable - can built in most OSes
• Scalable to handle millions of triples
– while using of persistent storage
– but indexing is very space-consuming
• Support for context and hierarchy of models
Redland - model
• Abstraction of model
to support different
storages
• In-memory and
persistent models
– BerkeleyDB, 3store,
MySQL
• Rich, triple-centric API
„The Design and Implementation of the Redland RDF Application Framework” - David Beckett
Sesame
• Implemented in Java
• Database independent
– idea of SAIL (Storage Abstraction Interface
Layer)
• Scalable architecture
• Implementation of remote models
– can query different models over network
• Graph-centric approach
• Language: RQL
Sesame - architecture
• RAL - Repository Abstraction Layer
– makes Sesame storage independent
– API supportes RDF Schema semantics
(e.g. subsumption reasoning)
– can be stacked one on another
– interface oriented for persistance storage
(DBMS, Object-Relational DB)
– data returned as streams
– can even use net-based RDF services (!)
• Due to poor performance, implemented
cache as one of RALs
– cache mainly for RDFS, as it needs code
support in reasoning (subClassOf, ...)
“Sesame: An Architecture for Storing and Querying
RDF Data and Schema Information” - Jeen Broekstra, Arjohn
Kampman, Frank van Harmelen
Sesame – query module
• Query module
– query plan and optimizer similar to already
known DB solutions
– query is translated to a set of simple RAL calls
– each leaf of the query plan can ‘evaluate itself’
and pull data from RAL
– data are returned as streams
– lack of optimization on storage level
Brahms
•
•
•
•
Implemented in C++ (bindings for Java also available)
Graph-centric approach
Designed to support large in-memory RDF graphs
Optimized for speed and memory usage
– other storages do not offer optimized in-memory
implementation for large graphs
– only main memory offers fastest access - usage of
persistent storage decreases performance
• In-memory storage with fast precomputed graph
snapshot loading
– minimize cold-start time
Brahms
• Framework for fast discovery of long association
paths in large RDF bases
– memory and CPU intense algorithms
• Rich API, but no query language supported
– higher level query languages do not support variable
length association path queries
– association path discovery algorithms operate on lowlevel graph API
• Overperformed Jena, Sesame and Redland during
tests for association discovery
– also was able to work efficiently on much larger inmemory graphs than other storages did not handle
“BRAHMS: A WorkBench RDF Store And High Performance Memory System for Semantic
Association Discovery” (Technical report) - Maciej Janik, Krys Kochut
Why RDF languages?
• Find resources based on predicates,
values, labels or associations
• SQL is not good for querying RDF data
– different models: relational and graph
• XML query languages cannot deal with
graph data
• Syntactic approach is not enough
• Required semantic querying
• Inferencing is desirable
Available query languages
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RQL
RDQL
SeRQL
Triple
SPARQL – (latest)
SquishQL
Versa
N3
RxPath
RDFQL
• Majority of languages
have roots in SQL
• No single standard as
SQL
• Some languages are
tightly coupled with
specific storages
RQL
• Based on OQL
• Utilizes functional approach with support for
generalized path expressions
– both nodes and edges can become variables
• Not completely compatible with RDF specification
– has some additional restrictions
• Return bindings to variables (no closure)
• Implemented in RDFSuite and partially in Sesame
select Res from {Res} ns:label {x} where x=“foo” using
namespace ns=…
“RQL: A Declarative Query Language for RDF” - Greg Karvounarakis, Sofia Alexaki, Vassilis
Christophides, Dimitris Plexousakis, Michel Scholl
RDQL
• SQL-like syntax
– easy to adopt for DB users
• Can specify patterns of triples to select
• Schema is not interpreted
• Not closed under queries
– output as bindings to selected variables
• Implemented in Jena
select ?p, ?q where (?p <rdfs:label> “foo”) (?p <rdf:type> ?q)
“RDQL - A Query Language for RDF” (W3C Member Submission) - Andy Seaborne (HP
Labs Bristol)
SeRQL
• Sesame RDF Query Language
• Based on RQL and RDQL
• Support for generalized path expressions
and optional matching
• Query filters
– select-from-where – return variable bindings
and is not closed
– construct-from-where – return matching
subgraph that can be queried (closure)
“SeRQL: Sesame RDF query language” - Jeen Broekstra
Triple
• Derived from F-logic
– should be easy to adopt for logic programmers
• Triples are logic expressions
– S[PO]
• Queries and triples have the same logic
representation
• Reasoning is a part of language
• Does not fulfill closure property
• Implemented in Triple system
FORALL X <- ( X[rdfs:label -> “foo”] )@default:ln.
“TRIPLE - A Query, Inference, and Transformation Language for the Semantic Web” Michael Sintek, Stefan Decker
SPARQL
• W3C effort to standarize query language
– best experience and requirements from different
languages (like RQL, RDQL)
• Based on matching graph patterns
– triples, paths, subgraphs
– optional blocks and matching
– matching alternatives (union) and disjunction
• Many additional operators
– grouping, sorting, limit results
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name ?mbox
WHERE { ?x foaf:name ?name . OPTIONAL { ?x foaf:mbox ?mbox } }
“SPARQL Query Language for RDF” (W3C Working Draft) - Eric Prud'hommeaux ,
Andy Seaborne
Sample path query
“A Comparison of RDF Query Languages” - Peter Haase, Jeen Broekstra, Andreas
Eberhart, Raphael Volz
Expressive power of RDF languages
“Ontology Storage and Querying” (Technical Rreport No 308) - Aimilia Magkanaraki et al.
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