Knowledge Representation and Reasoning layer of Apache Stanbol

1. Knowledge
Representation
Semantic CMS Community
and Reasoning with
Apache Stanbol
Project Review
Andrea Nuzzolese
Meeting
Luxemburg, 14-03-2013 andrea.nuzzolese@istc.cnr.it
STLab, ISTC-CNR
Co-funded by the
Italy
European Union
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2. 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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4. 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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5. 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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7. 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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8. Scopes and sessions in th
Ontology Network Manager
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9. Ontology Network Manager
REST services
› /ontonet/ontology/{scopeName}
› - {scopeName} list (GET), delete (DELETE) all registered
and/or active ontology scopes
› + {scopeName} get or activate, delete or deactivate, create
(PUT) and update (POST) the ontology of the scope
identified by {scopeName}
› ontonet/session/{id}
› -{id} get, delete all registered ontology sessions
› + {id} get, delete, create (PUT) and update (POST) the
ontology session identified by {id}
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10. 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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11. 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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12. Stanbol Rules: some usage scenario
› Integritycheck 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
by DL reasoners
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13. 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
› Adapterscan be easily extended by implementing the
provided interface
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14. 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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15. 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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16. 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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17. 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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18. Rules REST services
› /rule
› get, create (POST), and delete rules into the rule store
› /recipe
› get,create (PUT), add rules into (POST), and delete a
recipe
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19. 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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20. 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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21. 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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22. Reasoners REST services
› Services for classification, consistency checking and
enrichment
› /reasoners/rdfs: based on RDFS
› /reasoners/owlmini: by default based on Jena OWLMini
reasoner.
› /reasoners/owl: by default based on Jena OWL reasoner.
› Refactoring services
› /refactor/apply
› Managing reasoning jobs
› /jobs/{jid}
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23. About adoption
› Netlab
› Adoptionof the Ontology Manager and Rules for storing
ontologies and enabling reasoning
› InSideOut10
› WordLiftplug-in for WordPress based on Rules for
enabling schema.org compliant content
› Acuity
Unlimited
› KR&R enables reasoning services to assist Fedora
Commons repository managers acquire and manage
semantic metadata about their contents
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24. DEMO
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25. Thank you
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