Knowledge Representation and Reasoning with Apache Stanbol

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Knowledge Representation and Reasoning layer of Apache Stanbol

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Knowledge Representation and Reasoning with Apache Stanbol

  1. 1. Knowledge RepresentationSemantic 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 www.iks-project.eu
  2. 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 www.iks-project.eu Copyright IKS Consortium 2
  3. 3. www.iks-project.eu Copyright IKS Consortium 3
  4. 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. www.iks-project.eu Copyright IKS Consortium 4
  5. 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 www.iks-project.eu Copyright IKS Consortium 5
  6. 6. www.iks-project.eu Copyright IKS Consortium 6
  7. 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 www.iks-project.eu Copyright IKS Consortium 7
  8. 8. Scopes and sessions in thOntology Network Manager www.iks-project.eu Copyright IKS Consortium 8
  9. 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} www.iks-project.eu Copyright IKS Consortium 9
  10. 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 www.iks-project.eu Copyright IKS Consortium 10
  11. 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 www.iks-project.eu Copyright IKS Consortium 11
  12. 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 www.iks-project.eu Copyright IKS Consortium 12
  13. 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 www.iks-project.eu Copyright IKS Consortium 13
  14. 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 www.iks-project.eu Copyright IKS Consortium 14
  15. 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) www.iks-project.eu Copyright IKS Consortium 15
  16. 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) www.iks-project.eu Copyright IKS Consortium 16
  17. 17. A rule exampleprefix 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)] www.iks-project.eu Copyright IKS Consortium 17
  18. 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 www.iks-project.eu Copyright IKS Consortium 18
  19. 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 www.iks-project.eu Copyright IKS Consortium 19
  20. 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 www.iks-project.eu Copyright IKS Consortium 20
  21. 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 www.iks-project.eu Copyright IKS Consortium 21
  22. 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} www.iks-project.eu Copyright IKS Consortium 22
  23. 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 www.iks-project.eu Copyright IKS Consortium 23
  24. 24. DEMOwww.iks-project.eu Copyright IKS Consortium 24
  25. 25. Thank youwww.iks-project.eu Copyright IKS Consortium 25

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