1 / 44

Language (Formalisms) For Ontology Building

Language (Formalisms) For Ontology Building. Neda Alipanah 22 October 2012. Content. Why Ontologies ? Machine Process able Knowledge Knowledge Exchange Big Data Relevant Technologies Layered Architecture Building Tools and Visualization Ontology Application Information Integration

miranda-roach
Télécharger la présentation

Language (Formalisms) For Ontology Building

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Language (Formalisms) For Ontology Building Neda Alipanah 22 October 2012

  2. Content • Why Ontologies? • Machine Process able Knowledge • Knowledge Exchange • Big Data • Relevant Technologies • Layered Architecture • Building Tools and Visualization • Ontology Application • Information Integration • Web Database Management • Web Services

  3. Why Ontologies? • Machine readable and understandable process of data • Consistent Knowledge Presentation for Enterprise application integration (Knowledge Exchange) • Nodes and links that essentially form a very large database with specific rules

  4. Why Ontologies? 1. Machine readable and understandable process of data John Smith is Assistant Professor of Computer Science in University of X. He is teaching several courses including Course A, B, C. Assistant Professor University X John Smith

  5. Why Ontologies? 2. Consistent Knowledge Presentation for Enterprise application integration (Knowledge Exchange) Disease Symptoms Patient Address

  6. Why Ontologies? 3. Nodes and links that essentially form a very large database with specific rules. Database capture the data and relations (Entity Relations) but not the semantic and rules Disease Symptoms Patient Address Concept 1 is reverse of Concept 2. Concept 2 is subclass of Concept 3. Concept 100 has isA relation with Concept 2000 and is reverse of Concept 500.

  7. Content • Why Ontologies? • Machine Process able Knowledge • Knowledge Exchange • Big Data • Relevant Technologies • Layered Architecture • Building Tools and Visualization • Ontology Application • Information Integration • Web Database Management • Web Services

  8. Technologies- Layered Architecture Tim Berners Lee Architecture Logic, Proof and Trust TRUST P R I V A C Y Rules/Query Other Services RDF/Ontologies XML/XML Schemas URI/UNICODE

  9. Technologies- Layered Architecture URI (Uniform Resource Identifiers): • Simple and Extensible means for Identifying a Resource • Universal Resource Identifiers in WWW • Example http://www.nih.gov/ http://www.ncbi.nlm.nih.gov/gap http://www.ucsd.edu http://www.semanticweb/JohnSmith

  10. What is XML about? • XML= eXtensibleMarkup Language by the W3C (World Wide Web Consortium) • Transport and Store Data (Structured Knowledge) • Key to XML is Document Type Definitions (DTDs) • Defines the role of each element of text in a formal model • Compound Documents(Multiple files)

  11. XML Example Year: 2002 Asset report Name: U. Of X Assets Patents Equipment Other assets Dept Funds Patent news Expenses Name: BioInformatics Contracts ID Author title Grants

  12. XML File Example <Professor credID=“9” subID = “16: CIssuer = “2”> <name> Alice Brown </name> <university> University of X <university/> <department> CS </department> <research-group> BioInformatics</research-group> </Professor> <Secretary credID=“12” subID = “4: CIssuer = “2”> <name> John James </name> <university> University of X <university/> <department> BioInformatics </department> <level> Senior </level> </Secretary>

  13. Technologies- Layered Architecture Tim Berners Lee Architecture Logic, Proof and Trust TRUST P R I V A C Y Rules/Query Other Services RDF/OWL Ontologies XML/XML Schemas URI/UNICODE

  14. RDF • RDF = Resource Description Framework • Adds semantics with the use of ontologies, XML syntax • RDF Concepts • Basic Model • Resources, Properties and Statements • Container Model • Bag, Sequence and Alternative

  15. RDF • RDF/RDFS Elements • Class (School, Department, Person) • Rdfs:SubClassOf • Properties (Works) • Rdfs:SubPropertiesOf • Domain and Range of Property • Rdfs: domain (School) • Rdfs: range (Person) Works School Person SubClass Department

  16. RDF vs. XML Views An iPhone is a Product that has a price of $200″ <product> 
<title>iPhone</title> 
<price>$200</price> 
</product> <product title=”iPhone”>
 <price>$200</price>
 </product> XML Views <owl:Classrdf:about="&OntTeaching;Product"/> <owl:NamedIndividualrdf:about="&OntTeaching;Product1"> <rdf:typerdf:resource="&OntTeaching;Product"/> <rdfs:labelrdf:datatype="&xsd;Name">iPhone</rdfs:label> <price rdf:datatype="&xsd;decimal">200</price> </owl:NamedIndividual> OntTeaching:product1 rdf:type OntTeaching:Product OntTeaching:product1 OntTeaching:title “iPhone” OntTeaching:product1 price “200″ RDF View

  17. OWL Web Ontology Language • OWL: Semantic Markup Language for Publishing/Sharing Ontologies • Enumeration on Classes <owl:Class> <owl:oneOfrdf:parseType="Collection"> <owl:Thingrdf:about="#Europe"/> <owl:Thingrdf:about="#Africa"/> <owl:Thingrdf:about="#NorthAmerica"/> <owl:Thingrdf:about="#SouthAmerica"/> <owl:Thingrdf:about="#Australia"/> <owl:Thingrdf:about="#Antarctica"/> </owl:oneOf> </owl:Class>

  18. OWL Web Ontology Language • OWL • Value Constraints • owl:allValuesFrom • owl:someValuesFrom • owl:hasValue <owl:Restriction> <owl:onPropertyrdf:resource="#hasParent" /> <owl:someValuesFromrdf:resource="#Physician" /> </owl:Restriction> • <owl:Restriction> • <owl:onPropertyrdf:resource="#hasParent" /> • <owl:hasValuerdf:resource="#Clinton" /> • </owl:Restriction>

  19. OWL Web Ontology Language • OWL: Cardinality constraints • owl:maxCardinality • owl:minCardinality • owl:cardinality <owl:Restriction> <owl:onPropertyrdf:resource="#hasParent" /> <owl:cardinalityrdf:datatype="&xsd;nonNegativeInteger">2</owl:cardinality> </owl:Restriction>

  20. OWL Web Ontology Language • OWL: Intersection, union and complement • owl:intersectionOf • owl:unionOf • Owl:complementOf <owl:Class> <owl:unionOfrdf:parseType="Collection"> <owl:Class> <owl:oneOfrdf:parseType="Collection"> <owl:Thingrdf:about="#Tosca" /> <owl:Thingrdf:about="#Salome" /> </owl:oneOf> </owl:Class> <owl:Class> <owl:oneOfrdf:parseType="Collection"> <owl:Thingrdf:about="#Turandot" /> <owl:Thingrdf:about="#Tosca" /> </owl:oneOf> </owl:Class> </owl:unionOf> </owl:Class> Not Meat <owl:Class> <owl:complementOf> <owl:Classrdf:about="#Meat"/> </owl:complementOf> </owl:Class>

  21. OWL Web Ontology Language • OWL: Equivalent Class, Disjoint Class <owl:Classrdf:about="#DaPonteOperaOfMozart"> <owl:equivalentClass> <owl:Class> <owl:intersectionOfrdf:parseType="Collection"> <owl:Restriction> <owl:onPropertyrdf:resource="#hasComposer"/> <owl:hasValuerdf:resource="#Wolfgang_Amadeus_Mozart"/> </owl:Restriction> <owl:Restriction> <owl:onPropertyrdf:resource="#hasLibrettist"/> <owl:hasValuerdf:resource="#Lorenzo_Da_Ponte"/> </owl:Restriction> </owl:intersectionOf> </owl:Class> </owl:equivalentClass> </owl:Class> <owl:Classrdf:about="#Man"> <owl:disjointWithrdf:resource="#Woman"/> </owl:Class>

  22. How to Build OWL/RDF files? • Do we need to remember all the OWL language syntax? • How to do it easy to use and remember?

  23. Content • Why Ontologies? • Machine Process able Knowledge • Knowledge Exchange • Big Data • Relevant Technologies • Layered Architecture • Building Tools and Visualization • Ontology Application • Information Integration • Web Database Management • Web Services

  24. How to Build RDF/OWL files? • Different Building and Visualization Tools • Protégé, http://protege.stanford.edu/ • Gruff, http://www.franz.com/agraph/gruff/ (Download version 3.3) • Using Programming Languages • Java and Jena API • http://jena.apache.org/ • http://jena.sourceforge.net/tutorial/RDF_API/

  25. Protégé Tool- Open Source Ontology Editor • Class Creation

  26. Protégé Tool- Open Source Ontology Editor • Property (Object/Data properties)

  27. Protégé Tool- Open Source Ontology Editor • Individual Creation

  28. Gruff Tool- A Grapher-Based Triple-Store Browser for AllegroGraph What is triple Store? Product1 200 type Product price iPhone title <owl:Classrdf:about="&OntTeaching;Product"/> <owl:NamedIndividualrdf:about="&OntTeaching;Product1"> <rdf:typerdf:resource="&OntTeaching;Product"/> <rdfs:labelrdf:datatype="&xsd;Name">iPhone</rdfs:label> <price rdf:datatype="&xsd;decimal">200</price> </owl:NamedIndividual> Subject Predicate Object OntTeaching:product1 rdf:type OntTeaching:Product OntTeaching:product1 OntTeaching:title “iPhone” OntTeaching:product1 price “200″

  29. Gruff Tool- A Grapher-Based Triple-Store Browser for AllegroGraph Create a New Triple Store Choose a Path for the Ontology Load Ontology Present the Ontology Triples Query the Triples

  30. Gruff Tool- A Grapher-Based Triple-Store Browser for AllegroGraph

  31. Gruff Tool- A Grapher-Based Triple-Store Browser for AllegroGraph

  32. Gruff Tool- A Grapher-Based Triple-Store Browser for AllegroGraph

  33. Programming with OntologiesJava + Jena API • Collection of Tools and Java Libraries • For Developing Linked-data Apps, Tools and Servers • Store Information in RDF Triples in Directed Graphs • An Ontology API for Handling OWL and RDFS Ontologies • ARule-based Inference Engine for Reasoning with RDF and OWL data sources • Efficient Storage of Triples on Disk • Aquery engine compliant with the latest SPARQL

  34. Ontology Building using Jena Ontology: iPhone.owl Product1 200 price type Product iPhone title Subject Predicate Object Triples http://www.semanticweb.org/ontologies/2012/9/OntTeaching.owl#Product1 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.semanticweb.org/ontologies/2012/9/OntTeaching.owl#Product Product1 http://www.semanticweb.org/ontologies/2012/9/OntTeaching.owl#title “iPhone” Product1 http://www.semanticweb.org/ontologies/iPhone.Owl#price “200”

  35. Ontology Building using Jena The code to create this graph, or model, is simple: // some definitions • static String productURI= "http://www.semanticweb.org/ontologies/Product"; // create an empty Model • Model model = ModelFactory.createDefaultModel(); // create the resource • Resource product= model.createResource(productURI); // add the property • product.addProperty(title, ”iPhone”); • product.addProperty(price, ”200”);

  36. Jena • How to read Ontology? // list the statements in the Model StmtIteratoriter = model.listStatements(); // print out the predicate, subject and object of each statement while (iter.hasNext()) { Statement stmt = iter.nextStatement(); // get next statement Resource subject = stmt.getSubject(); // get the subject Property predicate = stmt.getPredicate(); // get the predicate RDFNode object = stmt.getObject(); // get the object System.out.print(subject.toString()); System.out.print(" " + predicate.toString() + " "); if (object instanceof Resource) { System.out.print(object.toString()); } else { // object is a literal System.out.print(" \"" + object.toString() + "\""); } System.out.println(" ."); }

  37. SPARQL Query • Query on Triples with Exact Pattern Matching (Subject of query is Product1) SELECT ?b ?c Where { <http://www.semanticweb.org/ontologies/2012/9/OntTeaching.owl#Product1> ?b ?c } • Result

  38. Content • Why Ontologies? • Machine Process able Knowledge • Knowledge Exchange • Big Data • Relevant Technologies • Layered Architecture • Building Tools and Visualization • Ontology Application • Information Integration • Web Database Management • Web Services

  39. Ontology Applications The database of Genotypes and Phenotypes (dbGaP) is archiving the results of different Genome Wide Association Studies (GWAS). • Phenotype variables are not harmonized across studies. • Redundent phenotype identifiers for the same phenotype. • dbGaP lacks semantic relations among its variables. • Search on phenotypes is inefficient and inaccurate . • Goal is to standardize dbGaP information to allow accurate, reusable and quick retrieval of information

  40. Ontology Applications • Several Available dbGAP Studies phs000284.v1.pht001901.v1.CFS_CARe_Sample.data_dict_2011_02_07 id=”phv00122015”, Description=”Age at time of Study”, name=”age”, version=“1”, Logical Max=”65”, Logical Minimum=”18”, unit=”Years”, type=”decimal” id=”phv00122058”, Description=”Age of patient at the time of Study”, name=”age”, version=“1”, Logical Max=”90”, Logical Minimum=”20”, unit=”Years”, type=”decimal” phs000284.v1.pht001903.v1.CFS_CARe_ECG.data_dict_2011_02_07

  41. Ontology Applications • Building Information Model (Ontology) Individual id=”phv00122058” Age id=”phv00122015”

  42. Ontology Applications • Information Retrieval and Ranking Phenotypes Query={Age of Subject}

  43. Conclusion • Benefits of Structured Data (XML, OWL) • Tools to Create and Visualize Ontologies • Jena API for Building Ontologies • Sparql Queries on Ontologies • Applications uses Ontologies

  44. Contacts • NedaAlipanah • Division of Biomedical Informatics9500 Gilman Dr., Bldg 2 #0203E • Email: nalipanah@ucsd.edu

More Related