Presentation is loading. Please wait.

Presentation is loading. Please wait. Co-funded by the European Union Semantic CMS Community Project Review Meeting Luxemburg, 14-03-2013 Knowledge Representation and Reasoning.

Similar presentations

Presentation on theme: " Co-funded by the European Union Semantic CMS Community Project Review Meeting Luxemburg, 14-03-2013 Knowledge Representation and Reasoning."— Presentation transcript:

1 Co-funded by the European Union Semantic CMS Community Project Review Meeting Luxemburg, Knowledge Representation and Reasoning with Apache Stanbol Andrea Nuzzolese STLab, ISTC-CNR Italy

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 Copyright IKS Consortium 2

3 3 Copyright IKS Consortium

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. 4 Copyright IKS Consortium

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 5 Copyright IKS Consortium

6 6 Copyright IKS Consortium

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 7 Copyright IKS Consortium

8 Scopes and sessions in th Ontology Network Manager 8 Copyright IKS Consortium

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} 9 Copyright IKS Consortium

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 10 Copyright IKS Consortium

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 11 Copyright IKS Consortium

12 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 by DL reasoners 12 Copyright IKS Consortium

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 Adapters can be easily extended by implementing the provided interface 13 Copyright IKS Consortium

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 14 Copyright IKS Consortium

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) 15 Copyright IKS Consortium

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) 16 Copyright IKS Consortium

17 A rule example prefix myont =. uncleRule[ is(myont:Human, ?x). has(myont:hasParent, ?x, ?z). has(myont:hasSibling, ?z, ?y) -> has(myont:hasUncle, ?x, ?y) ] 17 Copyright IKS Consortium

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 18 Copyright IKS Consortium

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 19 Copyright IKS Consortium

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 20 Copyright IKS Consortium

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 21 Copyright IKS Consortium

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} 22 Copyright IKS Consortium

23 About adoption Netlab Adoption of the Ontology Manager and Rules for storing ontologies and enabling reasoning InSideOut10 WordLift plug-in for WordPress based on Rules for enabling compliant content Acuity Unlimited KR&R enables reasoning services to assist Fedora Commons repository managers acquire and manage semantic metadata about their contents 23 Copyright IKS Consortium

24 DEMO 24 Copyright IKS Consortium

25 Thank you 25 Copyright IKS Consortium

Download ppt " Co-funded by the European Union Semantic CMS Community Project Review Meeting Luxemburg, 14-03-2013 Knowledge Representation and Reasoning."

Similar presentations

Ads by Google