www.iks-project.eu co-funded by the european union semantic cms community project review meeting...
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www.iks-project.eu
Co-funded by the European Union
Semantic CMS Community
Project ReviewMeetingLuxemburg, 14-03-2013
Knowledge Representation and Reasoning with Apache Stanbol
Andrea Nuzzolese
STLab, ISTC-CNR
Italy
www.iks-project.eu
What does KR and Reasoning layer provide to Sanbol?
Services used to define and manipulate semantic data models in the CMS i.e., Ontology Network Manager component
Services able to retrieve additional semantic information about content i.e., Reaoners and Rules components
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Ontology Network Manager: motivations
To enable a more scalable reasoning by activating only parts of the knowledge that is really needed
by the application limiting the scope of specific reasoning tasks.
To distinguish between core and volatile knowledge core knowledge describes the semantic domain of the
CMS volatile knowledge can be any knowledge coming from
external services, or extracted from contents etc.
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Ontology Network Manager
The Ontology Network Manager provides a controlled environment for managing ontology networks
An ontology network is “a collection of ontologies related together through a variety of different relationships such as mapping, modularization, and versioning.” [NeOn D1.1.5 Haase et. al]
The ONM provides API and REST services for constructing ontology networks and maintaining connectivity at runtime
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Ontology networks in Stanbol
The ONM relies on two types of artifacts for constructing ontology networks Scope:
a shared artifacts within the CMS for collecting all the persistent knowledge.
can be seen as a "logical realm" for the ontologies that encompass a certain CMS-related set of concepts
e.g., "User", "Event", "Content”, "Community”,
Session : a shared artifact for volatile knowledge
e.g., knowledge extracted on-the-fly from content
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Scopes and sessions in th Ontology Network Manager
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Stanbol Rules
Stanbol Rules is the component that supports the construction and the management of inference rules within Stanbol
Stanbol Rules provide an additional layer and a syntax for expressing business logics by means of axioms
The management of rules is performed through HTTP REST services
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Rules and Recipes
Rules are organized into a logic container called recipe
A recipe identifies a set of rules that share the same business logic e.g., integrity check of data, Search Engine Optimizaion
Rules within a recipe are interpreted and executed as a whole
A rule can be shared by different recipes
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Stanbol Rules: some usage scenario
Integrity check from data fusion the CMS administrator can define integrity checks for data
fetched from heterogeneous and external sources in order to prevent unwanted formats or inconsistent data
Vocabulary harmonization Rules can be used for the alignment of external data
representation to internal one (managed via the Ontology Network Manager)
DL reasoning Rules can be used as axioms for inferring new knowledge
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Stanbol Rules adapters Stanbol Rules are expressed by using the Stanbol Rule
language
By need, rules are converted at runtime to the format required by a concrete rule engine
By default, a list of rule adapters is provided i.e., SWRL for DL reasoning through OWL API, Jena
Rules, Clerezza SPARQL Constructs, pure SPARQL Constructs
Adapters can be easily extended by implementing the provided interface
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The rule language
The rule syntax synoptic is
ruleName[body -> head]
The rule name uniquely identifies a rule The body and head consist of a set of conjunctive atoms
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Core rule atoms
Core atoms are Class assertion
i.e., is(classPredicate, argument) Individual assertion
i.e., has(properyPredicate, arg1, arg2) Data value assertion
i.e., values(properyPredicate, arg1, arg2)
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Additional rule atoms
Comparison e.g., same(arg1, arg2), greaterThan(arg1, arg2)
String manipulation e.g., concat(arg1, arg2), lowercase(arg)
Arithmetical atoms e.g., sum(arg1, arg2), mult(arg1, arg2)
Production atoms e.g., newIRI(arg1, arg2), newLiteral(arg1, arg2)
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A rule example
prefix myont = <http://www.foo.org/myont.owl#> .
uncleRule[
is(myont:Human, ?x) .
has(myont:hasParent, ?x, ?z) .
has(myont:hasSibling, ?z, ?y)
->
has(myont:hasUncle, ?x, ?y)
]
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Stanbol Reasoners
Common REST wrapper around available reasoners
Provides a default reasoner based on Jena
Other reasoners can be plugged through the OWLLink protocol
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Reasoning services
Currently implemented services are consistency checking classification enrichment refactoring
Inputs for reasoning are ontology networks and rules recipes
Supported different reasoners and reasoning configuration in parallel
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Dealing with big data reasoning
Reasoning with big data is performed by means of jobs through HTTP services
A job is associated to an ID
The status of a job can be queried through REST API
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DEMO
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Thank you
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