Semantic Technologies and Triplestores for Business Intelligence

Semantic Technologies & Triplestores
for BI
1st European Business Intelligence Summer School
eBISS 2011

Marin Dimitrov (Ontotext)

Jul 2011

eBISS 2011

Semantic Technologies & Triplestores for BI (eBISS 2011) Jul 2011 #2

Contents

• Introduction to Semantic Technologies
• Semantic Databases – advantages, features and
benchmarks
• Semantic Technologies and Triplestores for BI


INTRODUCTION TO SEMANTIC
TECHNOLOGIES


The need for a smarter 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, 2001)


The need for a smarter Web (2)

• “PricewaterhouseCoopers believes a Web of data
will develop that fully augments the document Web
of today. You’ll be able to find and take pieces of
data sets from different places, aggregate them
without warehousing, and analyze them in a more
straightforward, powerful way than you can now.”
(PWC, May 2009)


The Semantic Web vision (W3C)

• Extend principles of the Web from documents to
data
• Data should be accessed using the general Web
architecture (e.g., URI-s, protocols, …)
• Data should be related to one another just as
documents are already
• Creation of a common framework that allows:
– Data to be shared and reused across applications
– Data to be processed automatically
– New relationships between pieces of data to be inferred


The Semantic Web stack

(c) Benjamin Nowack


The Semantic Web timeline
RDF RDF 2
DAML+OIL OWL OWL 2
SPARQL SPARQL 1.1
RIF
RDFa
SAWSDL
LOD
SKOS
HCLS
RDB2RDF
GLD

PIL

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011


Ontologies as data models on the Semantic Web

• An ontology is a formal specification that provides
sharable and reusable knowledge representation
– Examples – taxonomies, thesauri, topic maps, formal
ontologies
• An ontology specification includes
– Description of the concepts in some domain and their
properties
– Description of the possible relationships between concepts
and the constraints on how the relationships can be used
– Sometimes, the individuals (members of concepts)


Resource Description Framework (RDF)

• A simple data model for
– Formally describing the semantics of information
– representing meta-data (data about data)
• A set of representation syntaxes
– RDF/XML (standard), N-Triples, N3
• Building blocks
– Resources (with unique identifiers)
– Literals
– Named relations between pairs of resources (or a resource
and a literal)


RDF (2)

• Everything is a triple
– Subject (resource), Predicate (relation), Object (resource
or literal)
• The RDF graph is a collection of triples
predicate
subject object

École Centrale locatedIn
Paris
Paris

hasPopulation
Paris 2193031


RDF graph example (3)
hasName “École Centrale Paris”

dbpedia:École_centrale_Paris

locatedIn establishedIn
1829

dbpedia:Paris

hasPopulation
hasName

“Paris” 2193031

Subject Predicate Object

http://dbpedia.org/resource/Paris hasName “Paris”
http://dbpedia.org/resource/Paris hasPopulation 2193031
http://dbpedia.org/resource/École_centrale_Paris locatedIn http://dbpedia.org/resource/Paris
http://dbpedia.org/resource/École_centrale_Paris hasName “École Centrale Paris”
http://dbpedia.org/resource/École_centrale_Paris establishedIn 1829


RDF advantages

• Global identifiers of all resources (URIs)
– Reduces ambiguity
– Makes incremental data integration easier
• Graph data model
– Suitable for sparse, unstructured and semi-structured data
• Inference of implicit facts
• Schema agility
– Lowers the cost of schema evolution


RDF Schema (RDFS)

• RDFS provides means for:
– Defining Classes and Properties
– Defining hierarchies (of classes and properties)
– Domain/range of a property
• Entailment rules (axioms)
– Infer new triples from existing ones


RDFS entailment rules


RDF entailment rules (2)

• Class/Property hierarchies
– R5, R7, R9, R11
:human rdfs:subClassOf :mammal . :John a :man .
:man rdfs:subClassOf :human .  :John a :human .
 :man rdfs:subClassOf :mammal .  :John a :mammal .
:hasSpouse rdfs:subPropertyOf :hasRelative .
:John :hasSpouse :Merry .
 :John :hasRelative :Merry .

• Inferring types (domain/range restrictions)
– R2, R3
:hasSpouse rdfs:domain :human ;
rdfs:range :human .
:Adam :hasSpouse :Eve .
:Adam a :human .
:Eve a :human .


Web Ontology Language (OWL)

• More expressive than RDFS
– Identity equivalence/difference
• sameAs, differentFrom, equivalentClass/Property

• Complex class expressions
– Class intersection, union, complement, disjointness
• More expressive property definitions
– Object/Datatype properties
– Cardinality restrictions
– Transitive, functional, symmetric, inverse properties


OWL (2)
db1:Paris :hasPopulation 2913031.
• Identity equivalence db1:Paris = db2:Paris .
 db2:Paris :hasPopulation 2193031 .

:locatedIn a owl:TransitiveProperty .
:ECP :locatedIn :Paris .
• Transitive properties :Paris :locatedIn :France .
 :ECP :locatedIn :France .

:hasSpouse a owl:SymmetricProperty .
:John :hasSpouse :Merry .
• Symmetric properties  :Merry :hasSpouse :John .

:hasParent owl:inverseOf :hasChild .
• Inverse properties :John :hasChild :Jane .
 :Jane :hasParent :John .

:hasSpouse a owl:FunctionalPropety .
:Merry :hasSpouse :John .
• Functional properties :Merry :hasSpouse :JohnSmith .
 :JohnSmith = :John .


OWL sublanguages

• OWL Lite
– low expressivity / low formal complexity
– Logical decidability & completeness
– All RDFS features
– sameAs/differentFrom, equivalent class/property
– Inverse / symmetric / transitive / functional properties
– cardinality restriction (only 0 or 1)
– class intersection


OWL sublanguages (2)

• OWL DL
– high expressivity / efficient DL reasoning
– Logical decidability & completeness
– All OWL Lite features
– Class disjointness
– Complex class expressions
– Class union & complement
• OWL Full
– max expressivity / no efficient reasoning
– No guarantees for completeness & decidability


OWL 2 profiles

• Goals
– sublanguages that trade expressiveness for efficiency of
reasoning
– Cover specific important application areas
– Easier to understand by non-experts
• OWL 2 EL
– Best for large ontologies / small instance data (TBox
reasoning)
– Computationally optimal
• PTime reasoning complexity


OWL 2 profiles (2)

• OWL 2 QL
– Quite limited expressive power, but very efficient for
query answering with large instance data
– Can exploit query rewriting techniques
• Data storage & query evaluation can be delegated to a RDBMS

• OWL 2 RL
– Balance between scalable reasoning and expressive power
– Suitable for rule-based reasoning


OWL 2 profiles (3)

(c) Axel Polleres


SPARQL Protocol and RDF Query Language (SPARQL)

• SQL-like query language for RDF data
• Simple protocol for querying remote databases over
HTTP
• Query types
– select – query data by complex graph patterns
– ask – whether a query returns results (result is true/false)
– describe – returns all triples about a particular resource
– construct – create new triples based on query results


Graph patterns

• Whitespace separated list of Subject, Predicate,
Object
– ?x dbp-ont:city dbpedia:Paris
– dbpedia:École_centrale_Paris db-ont:city ?y
• Group Graph Pattern
– A group of 1+ graph patterns
– FILTERs can constrain the whole group
{
?uni a dbpedia:University ;
dbp-ont:city dbpedia:Paris ;
dbp-ont:numberOfStudents ?students .
FILTER (?students > 5000)
} Semantic Technologies & Triplestores for BI (eBISS 2011) Jul 2011 #26

Graph Patterns (2)

• Optional Graph Pattern
– Optional parts of a pattern
– pattern OPTIONAL {pattern}

SELECT ?uni ?students
WHERE {
?uni a dbpedia:University ;
dbp-ont:city dbpedia:Paris .
OPTIONAL {
?uni dbp-ont:numberOfStudents ?students
}
}


Graph Patterns (3)

• Alternative Graph Pattern
– Combine results of several alternative patterns
– {pattern} UNION {pattern}
SELECT ?uni
WHERE {
?uni a dbpedia:University .
{
{ ?uni dbp-ont:city dbpedia:Paris }
UNION
{ ?uni dbp-ont:city dbpedia:Lyon }
}
}


Anatomy of a SPARQL query

• List of namespace prefixes
– PREFIX xyz: <URI>
• Query result clause (variables)
– ?x, $y
• Datasets
• Graph patterns + filters
– Simple / group / alternative / optional
• Modifiers
– ORDER BY, DISTINCT, OFFSET/LIMIT


Linked Data

• “To make the Semantic Web a reality, it is necessary to have a
large volume of data available on the Web in a standard,
reachable and manageable format. In addition the
relationships among data also need to be made available. This
collection of interrelated data on the Web can also be referred
to as Linked Data. Linked Data lies at the heart of the
Semantic Web: large scale integration of, and reasoning on,
data on the Web.” (W3C)
• Linked Data is a set of principles that allows publishing,
querying and consumption of RDF data, distributed across
different servers
• similar to the way HTML is currently published & consumed


Linked Data design principles

1. Unambiguous identifiers for objects (resources)
– Use URIs as names for things
2. Use the structure of the web
– Use HTTP URIs so that people can look up the names
3. Make is easy to discover information about an
object (resource)
– When someone lookups a URI, provide useful
information
4. Link the object (resource) to related objects
– Include links to other URIs


Linked Data evolution – Oct 2007

(c) R. Cyganiak & A. Jentzsch


Linked Data evolution – Sep 2008



Linked Data evolution – Jul 2010



Linked Data evolution – Sep 2010

Introduction to Semantic Technologies, Ontologies and the Semantic Web Aug 2010 #35

SEMANTIC DATABASES
(TRIPLESTORES)


Triplestores

• RDF databases
– Store data according to the RDF data model
– Provide inference of implicit triples (either at data loading
time, or at query time)
– SPARQL as a query language
• Many similarities to traditional DBMS approaches
– … and many differences too


Triplestores vs. traditional DBMS

Triplestore OLTP OLAP NoSQL Graph

Update performance +/- + +/- ++ +
Complex Queries + + ++ - +
inference + - +/- - -
Sparse data + - +/- + +
Semi-structured / unstructured data + - - +/- +
Dynamic schema + - - +/- +/-


Triplestore advantages

• Global identifiers of resources (entities)
– Lowers the cost of data integration
• Inference of implicit facts
• Graph data model
– Suitable for sparse, semi-structured and unstructured data
• Agile schema
– New relations between entities may be easily added
• Exploratory queries against unknown schema
– Query and data vocabulary may differ
• Compliance to standards (RDF, SPARQL)

Design & Implementation

• Storage engine
– Native
– on top of an RDBMS
– on top of a NoSQL engine
• Triple density
– The in-memory “footprint” per triple may differ x10
between different triplestores
– Impact on TCO
• Compression
– Improves I/O on multi-core systems


Design & Implementation (2)

• Reasoning strategy
– Forward-chaining – at data loading time, start from the
explicit facts and apply the inference rules until the
complete closure is inferred
– Backward-chaining – at runtime, start with a query and
decompose recursively into smaller requests that can be
matched to explicit facts
– Hybrid strategy – partial materialization at data loading
time + partial query decomposition at runtime


Pros and cons of forward-chaining based
materialization

• Relatively slow addition of new facts
– inferred closure is extended after each transaction
• Deletion of facts is slow
– facts that are no longer true are removed from the
inferred closure
• The maintenance of the inferred closure requires
more resources
• Querying and retrieval are fast
– no reasoning is required at query time
– RDBMS-like query evaluation & optimisation techniques
are applicable


• Invalidation strategy
– Truth maintenance is not trivial
– huge overhead for keeping meta-data about inference
dependencies
– Trivial approach: just re-compute the complete inferred
closure after a deletion
– Advanced approach: detect which parts of the inferred
closure are affected and need to be invalidated as well



• owl:sameAs optimization
– It is a transitive, reflexive and symmetric relationship
– owl:sameAs induced inference can “inflate” the number of
statements and deteriorate inference/query performance
– Specific optimizations allow that a compact representation
of equivalent resources is used
– Query results can be expanded through backward-chaining
at query time


Popular triplestores

• 4store
– http://4store.org
– Open source, distributed cluster (up to 32 nodes), data
fully partitioned, no inference (external reasoner,
backward chaining)
• AllegroGraph
– http://www.franz.com
– ACID transactions, RDF and limited OWL reasoning, full-
text indexing, compression, replication cluster, backward
chaining


Popular triplestores (2)

• Bigdata
– http://www.systap.com
– Open source, data partitioning, hybrid materialization, RDF
and limited OWL reasoning
• Dydra
– http://dydra.com
– SaaS, SPARQL endpoint + REST API, no reasoning
• Jena TDB
– http://www.openjena.org/TDB
– Open source, RDF and limited OWL reasoning



• Oracle
– RDF and limited OWL reasoning, data partitioning &
compression (RAC), owl:sameAs optimization, security &
versioning; geo-spatial extensions
• OWLIM
– http://www.ontotext.com
– Forward-chaining, RDF / OWL 2 RL / OWL 2 QL and limited
OWL Lite / OWL DL reasoning; replication cluster;
owl:sameAs optimization; full-text indexing; geo-spatial
extensions; scalable RDF Rank



• Sesame
– http://www.openrdf.org
– Open source; plugable storage & inference layer
• StarDog
– http://stardog.com
– backward-chaining; OWL DL and all OWL 2 profiles; full-
text indexing; compression
• Virtuoso
– http://virtuoso.openlinksw.com
– Universal server (RDF, XML, RDBMS); backward chaining;
geo-spatial extensions; full-text indexing; compression

Benchmarking

• Tasks
– Data loading
– Query evaluation
– Data modification
• Performance factors
– Forward-chaining vs. backward-chaining
– Data model complexity
– Query complexity
– Result set size
– Number of concurrent clients


Popular benchmarks for triplestores

• LUBM
– Storing & query performance benchmark
– 14 predefined queries + data generator tool
• BSBM
– SPARQL benchmark
– 3 use cases (12/17/8 distinct queries)
• SP2Bench
– SPARQL benchmark
– 12 queries for most common SPARQL constructs & access
patterns


SEMANTIC TECHNOLOGIES &
TRIPLESTORES FOR BI


Data integration & querying (HCLS)

distributed querying at present

distributed querying with RDF and SPARQL

(c) HCLS @ W3C


Data integration & querying (HCLS)

(c) HCLS @ W3C


Data integration cost (PwC)

(c) PriceWaterhouseCooper


Semantic Technologies & Triplestores for BI

• Speed-up data integration
– RDF based ETL is more agile
• Lower the cost of data integration
– Initial cost of using ontologies is higher
– But the cost of ad-hoc ETL will be higher in the long term
• Align & integrate legacy data silos
– Querying & consuming data from disparate sources is
easier with SPARQL & RDF


Semantic Technologies & Triplestores for BI (2)

• Infer implicit & hidden knowledge
– Custom, user-defined rules as well
• Efficiently manage unstructured & semi-structured
data together
– graph data model
• Improve the quality of query results
– Inference of implicit facts
– SPARQL query vocabulary may differ from data vocabulary
– Exploratory queries


Q&A

Questions?
@ontotext


Semantic Technologies and Triplestores for Business Intelligence

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