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Next-Generation User-Centered Information Management

Next-Generation User-Centered Information Management. Ontology-based Information Representation. Information. Ontology. Representation.

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Next-Generation User-Centered Information Management

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  1. Next-Generation User-Centered Information Management Ontology-based Information Representation Information Ontology Representation Software Engineering betrieblicher Informationssysteme (sebis)Ernst Denert-StiftungslehrstuhlLehrstuhl für Informatik 19 Institut für InformatikTU München wwwmatthes.in.tum.de 030502-Wi-sebis-Master

  2. Ontology-based Information Representation • Outline • Motivation • Semantic Models for Information Representation • Taxonomy • Thesaurus • Topic Map • Ontology • The Semantic Web • URI, XML, RDF, RDFS, OWL • Jena • Ontology-Based Information Visualization withCluster Maps • Conclusion 030502-Wi-sebis-Master

  3. Motivation (1) • Information Representation • Data: information resources described by concepts • Semantic Structure: select, filter, classify, merge... based on terms • Representation: organized information resources • Search for information • Visualize search results • Navigate through search results what how   ... Data Semantic Structure Representation 030502-Wi-sebis-Master

  4. Motivation (2) • Metadata • Information about information resources  Object-based information representation • Example: Dublin Core • Best-known vocabulary for metadata, a set of 13 properties describing information resource • Document managemen properties: title, creator, publisher, date, language • Semantic properties: subject • Metadata about a document in a simple textfield without restrictions?  Context-based information representation • Grouping information resources by subjects they are about  Semantic models for information representation 030502-Wi-sebis-Master

  5. Ontology-based Information Representation • Outline • Motivation • Semantic Models for Information Representation • Taxonomy • Thesaurus • Topic Map • Ontology • The Semantic Web • URI, XML, NS, XMLS • RDF, RDFS, OWL • Jena • Ontology-Based InformationVisualization with Cluster Maps • Conclusion 030502-Wi-sebis-Master

  6. Taxonomy (1) • Taxonomy • Biologically motivated: classification of organisms (Carl von Linné) • Classification that arranges terms into a hierarchy • Based on inheritance (is-a relationship) • [ABiilsma] 030502-Wi-sebis-Master

  7. Taxonomy (2) • Taxonomy of Visual Elements • [JHugo] 030502-Wi-sebis-Master

  8. Taxonomy (3) • Person Taxonomy • Child • Adult Person Child Adult Boy Girl Man Woman Baby Student Baby Student Student Pensioneer Student Pensioneer Employee Employee Toddler School-Boy Toddler School-Girl 030502-Wi-sebis-Master

  9. Taxonomy (4) • Properties of Taxonomies • Hierarchy based on inheritance (is-a relationship) • A mammal is an animal. • Grouping of related terms • No explicite definition about how terms relate • Synonyms • Terms with some degree of similarity • Redundancy when a subclass belongs to more than one superclasses • Baby, Toddler and Student appear more than once in the Person taxonomy. 030502-Wi-sebis-Master

  10. Thesaurus (1) • Thesaurus • Motivated by linguistics • Classification of terms based on inheritance, similarity and synonymity • ISO standard: ISO2788 for monolingual and ISO5964 multilingual thesauri • [Creighton] 030502-Wi-sebis-Master

  11. Thesaurus (2) • Example of Thesaurus for „Person“ • Toddler  Baby • Student  School-Girl • Student  School-Boy Child Boy Girl Similarity Synonym Student Baby Student Baby Toddler School-Boy Toddler School-Girl 030502-Wi-sebis-Master

  12. Thesaurus (3) • Properties of Thesauri • Hierarchy based on inheritance (is-a relationship): same as taxonomy • Much reacher vocabulary for describing relationships • Related term: term with similar meaning • USE: with synonyms, preferred term; UF: inverse • Property: scope note • annotation, string attached to the term explaining its meaning • Homonyms (same word, different meaning) not possible to distinguish • Still redundancy when a sublcass belongs to more than one superclasses • Baby, Toddler and Student appear more than once in the taxonomy. 030502-Wi-sebis-Master

  13. Topic Map (1) • Topic Map • Motivated by mathematical models of how long-term memory works • Classification of terms represented by topics based on • Inheritance • Similarity, synonyms • User-defined relationships • XML Topic Maps • Standard XML format for TM  Open Vocabulary • www.TopicMaps.org • [TM2] 030502-Wi-sebis-Master

  14. Topic Map (2) • Information resource optionally identified by URI • Hierarchy of concept represented by a topic described by • Name with the properties • Scope – a set of topics representing a context • Type – a set of topics, a kind of an association between topics • Occurances (properties) connect a topic to an information resource; optionally scope and type • Association (Relationship); optionally scope and type • [TM3] 030502-Wi-sebis-Master

  15. Topic Map (3) isSiblingOf • Example of Topic Map for „Person“ • Toddler  Baby • Student  School-girl • Student  School-boy • Name • Age Person isChildOf hasChild Child Adult hasParent Boy Name Age Girl Similarity Synonym Student Baby Student Baby Toddler School-Boy Toddler School-Girl 030502-Wi-sebis-Master

  16. Topic Map (4) • Properties of Topic Maps • Flexible network of concepts strucutured by open vocabulary  More powerful (precise) searches  Flexible navigation • Composition, association (user-defined relationship types) possible • Able to distinguish between homonyms due to concept‘s type • Name and Age on the same conceptual level as Boy and Girl • Disambiguity of homonyms • Paris (France), Paris (Greek Mythology) • Still redundancy when a sublcass belongs to more than one superclasses • Model in its infancy 030502-Wi-sebis-Master

  17. Ontology (1) • Ontology • Originally motivated by philosophy: „the science of being“ (Aristotle) • Definition: „a formal explicit specification of a shared conceptualization“ (Gruber) • Vocabulary + Structure = Taxonomy • Taxonomy + Relationships, Constraints, Rules = Ontology • „Model for describing the world that consists of • a set of types, • properties, and • a set of relationship types“ (Garshol) • Classification of terms for objects and individuals • Open set of terms • Open language for describing relationships 030502-Wi-sebis-Master

  18. Ontology (2) • Ontology for „Person“ hasChild hasParent Person isSiblingOf isChildOf Adult Child Boy Girl Baby School-Boy School-Girl Name Age Student Rules Toddler A isChildOf B isChildOf C A isGrandChildOf C A isChildOf B B isParentOf A John Big 6 months ... ... A isChildOf B A hasParent B 030502-Wi-sebis-Master

  19. Ontology (3) Properties of Ontologies • Clearly defined relationships (inverse, transitive, symmetrical... ) • Constraints, rules • Open vocabulary  Machine-readability  Rule-based (logical) inferencing  Descriptive power  Precise searching, visualization, navigation • Managed redundancy • Easily extensible • Not only meta-model but also instances • Common standard between several parties • Binding data from heterogeneous sources 030502-Wi-sebis-Master

  20. Ontology-based Information Representation • Outline • Motivation • Semantic Models for Information Representation • Taxonomy • Thesaurus • Topic Map • Ontology • The Semantic Web • URI, XML, RDF, RDFS, OWL • Jena • Ontology-Based Information Visualization with Cluster Maps • Conclusion 030502-Wi-sebis-Master

  21. The Semantic Web (1) • Motivation • Extend existing markup with semantic markup • Define a standard web ontology language • Common syntax in order to share semantics • Provide tools and services to help users to • Design and maintain high quality ontologies • Store instances of ontology classes • Query ontology classes and instances • Integrate and align multiple ontologies 030502-Wi-sebis-Master

  22. The Semantic Web (2) • The Semantic Web • A product of W3C (World Wide Web Consortium) headed by Berners-Lee • Goal: lead W3 to its full potential • Develop common protocols • Control evolution of W3 • Maintain interoperability of W3 Semantics and Reasoning Relational Data Data Exchange 030502-Wi-sebis-Master

  23. XML (1) • XML and XML Schema • eXtensible Markup Language • Open vocabulary  extensibility • Strict syntax  well-formedness • Separation of content  different rendering of tree-like documents • XML Schema • Validity • NameSpace • URI that vocabulary is associated with, need not contain a document • Uniform Resource Identifier  the set of all addresses that refer to resources • Resource: any object that can be pointed by a URI • URL: subtype of URI  Unambiguous interpretation of identifiers 030502-Wi-sebis-Master

  24. RDF (1) • RDF • Resource Description Framework: • Standardization of description of resources • Extensible and flexible hierarchy based on XML • Open vocabulary: classes with properties and relationships • Namespaces: range and domain of properties, need be an existing document • Directed Graph built using statements • Statement specifies properties and values of web resources: John (Object) name (Property) „John Big“ (Value) John (Object) age (Property) „6 months“ (Value) John (Object) isChildOf (Property) Jane (Object) John (Object) isChildOf (Property) Tom (Object) 030502-Wi-sebis-Master

  25. RDF (2) • RDF Document: one description per resource with a list of properties • Description element • may be anonymous (no attributes) • possible attribute for class (object) definion • rdf:about to describe a resource (via URI) or • rdf:ID to define a resource (via a fragment identifier without #) • Fundamental Concepts • Object: resource defined by URI • Property: resource • Value: resource or literal Only fact-stating, basic data model for object, property, value • RDF schema vocabulary (RDF Schema Building Blocks) 030502-Wi-sebis-Master

  26. RDF (3) http://www.family.org/isChildOf http://www.person.bgr/jane http://www.person.bgr/john http://www.family.org/isChildOf http://www.person.bgr/name http://www.person.bgr/tom http://www.person.bgr/age http://purl.org/cd/elements/1.1/creator „6 months“ „John Big“ mailto:tom.big@big.bgr 030502-Wi-sebis-Master

  27. RDF (4) <Description about=„http://www.big.bgr/john“> <person:name resource=„John Big“/> <person:age resource =„6 months“/> < family:isChildOf resource =„http://www.person.org/jane“/> < family:isChildOf resource =„http://www.person.org/tom“/> </Description> <Description about=„http://www.big.bgr“ dc:creator=„tom.big@big.bgr“> </Description> 030502-Wi-sebis-Master

  28. RDFS (1) • Valid RDF • Provides information about interpretation of RDF statements • Class definition • Subclass definition using rdfs:subClassOf • Subproperty definition using rdfs:Property • Domain and Range restrictions • Example for Music use <Music rdf:resource=http://www.music.bgr/> 030502-Wi-sebis-Master

  29. RDFS (2) • <!DOCTYPE rdf:RDF [ <!ENTITY rdf 'http://www.w3.org/1999/02/22-rdf-syntax-ns#'> • <!ENTITY rdfs 'http://www.w3.org/2000/01/rdf-schema#'> ]> • <rdf:RDF xmlns:rdf="&rdf;" xmlns:rdfs="&rdfs;"> • <rdf:Description rdf:ID="Music"> • <rdf:type rdf:resource="&rdfs;Class"/> </rdf:Description> • <rdf:Description rdf:ID="Symphony"> • <rdf:type rdf:resource="&rdfs;Class"/> • <rdfs:subClassOf rdf:resource="#Music"/> </rdf:Description> • <rdf:Description rdf:ID="Concerto"> • <rdf:type rdf:resource="&rdfs;Class"/> • <rdfs:subClassOf rdf:resource="#Music"/> </rdf:Description> • </rdf:RDF> 030502-Wi-sebis-Master

  30. RDFS (3) • RDFS Weakness to describe resources in sufficient detail • No localized range and domain constraints: the range of hasChild is • person when applied to person • animal when applied to animal • No cardinality constraints: • Person has exactly two parents • No existence constraints: • all instances of person have a mother that is also a person • No transitive, inverse, symmetrical properties: • isChildOf is a transitive property • isChildOf is the inverse of isParentOf • isSiblingOf is symmetrical 030502-Wi-sebis-Master

  31. OWL (1) OWL • Web Ontology Language • General Public Licence • Based on RDF Open vocabulary • Logical combinations of classes (union, interesection, complement) • Extented properties: transitive, symmetrical, inverse • Web Ontology Language Requirements • Easy to understand and use • Formally specified, of adequate expressive power • Providing an automated reasoning support 030502-Wi-sebis-Master

  32. OWL (2) • OWL Types • OWL Full • Greatest expressive power • OWL DL • Extention of DL subset of RDF  Well-defined semantics  User-friendly syntax • OWL Lite • Simple syntax, tractable inference • [OWL] 030502-Wi-sebis-Master

  33. OWL (3) • Example of Ontology for two books about African Lion 030502-Wi-sebis-Master

  34. OWL (4) • Example of Ontology for „Man“ • <owl:Class rdf:ID="Man"> • <rdfs:subClassOf rdf:resource="#Person"/> • <rdfs:subClassOf rdf:resource="#Adult"/> • <owl:disjointWith rdf:resource="#Woman"/> • </owl:Class> • Example of Ontology for Property „isChildOf“ <owl:ObjectProperty rdf:ID=„isChildOf"> <owl:inverseOf rdf:resource="#isParentOf"/> </owl:ObjectProperty> 030502-Wi-sebis-Master

  35. OWL (4) • Extention towards including instances • Use of OWL and Ontologies • Data integration  Ontology mapping • Minimization of intellectual effort involved in developing an ontology by re-use • Composition of ontologies and adoption • Data interchange  Jena • Data querying  RDQL • Data visualization  Cluster Maps 030502-Wi-sebis-Master

  36. Ontology-based Information Representation • Outline • Motivation • Semantic Models for Information Representation • Taxonomy • Thesaurus • Topic Map • Ontology • The Semantic Web • URI, XML, RDF, RDFS, OWL • Jena • Ontology-Based Information Visualization with Cluster Maps • Conclusion 030502-Wi-sebis-Master

  37. Jena (1) • Jena Semantic Web Toolkit (Open Source, HP) • Java framework for writing web application in Java • OWL Lite based on RDF 030502-Wi-sebis-Master

  38. Jena (2) • Jena Architecture • Model Factory creates an empty ontology model that can be added resources, properties, statements Model model = ModelFactory.createDefaultModel(); ModelFactory createDefaultModel:Model 030502-Wi-sebis-Master

  39. Jena (4) Model Model createResource(String) : Resource createProperty(String):Property createStatement(Resource, Property, Object): Statements listStatements(Object, Object, Object) listObjectsOfProperty(Property) • Creation of resources, properties and rules Resource john = model.createResource(familyURI+“john“); Resource jane = model.createResource(familyURI+“jane“); Property childOf = model.createProperty(relationshipURI); Statement statement = model.createStatement(john, childOf, jane); • Querying of a model model.listObjectsOfProperty(childOf); model.listStatements(john,childOf, null); 030502-Wi-sebis-Master

  40. Jena (5) • Addition of properies to subjects john.addProperty(childOf,jane); • Querying of properties john.listProperties(siblingOf); Resource Resource addProperty(Property,Object) listProperties(Property) 030502-Wi-sebis-Master

  41. Jena (6) RDF Data Query Language (RDQL) • Keywords: select, where, using SELECT ?x WHERE (?x, http://www.family.org/child#, „John Big“) ================== http://www.big.bgr/john ================== SELECT ?resource FROM http://www.big.bgr WHERE (?resource info:age ?age) AND ?age >= 2 USING info FOR http://www.big.bgr/peopleInfo# =================== http://www.big.bgr/jane http://www.big.bgr/tom 030502-Wi-sebis-Master

  42. Ontology-based Information Representation • Outline • Motivation • Semantic Models for Information Representation • Taxonomy • Thesaurus • Topic Map • Ontology • The Semantic Web • URI, XML, RDF, RDFS, OWL • Jena • Ontology-Based Information Visualization with Cluster Maps • Conclusion 030502-Wi-sebis-Master

  43. Cluster Maps (1) • Clustering based on similarity • Tasks: • Data Analysis: different ontologies, same dataset • Data comparison: same ontology, multiple data sets • Query relaxation: find result set to queries for which no exact matches exist • Data Analysis: Search on jobs offered by economics sector • Visible size • Differentiation 030502-Wi-sebis-Master

  44. Cluster Maps (2) • Data Analysis: Search on jobs offered by economic sector • Various overlaps 030502-Wi-sebis-Master

  45. Cluster Maps (3) • Data Analysis: Search on jobs offered by region • Visible size • Geographical closeness is preserved 030502-Wi-sebis-Master

  46. Cluster Maps (4) • Data Comparison: services offered by two banks • Same ontology, different data sets 030502-Wi-sebis-Master

  47. Cluster Maps (5) • Query relaxation: query about a holiday in France • colour intensity for the cases • no exact matches • matches based on query relaxation 030502-Wi-sebis-Master

  48. Cluster Maps (6) • Clustering based on similarityforSearch, Navigation, Vizualization • Advantages • Visible and configurable size of the result set • Similarity between the instances of the result set • Intuitive search and navigation process 030502-Wi-sebis-Master

  49. User-centered Information Management! Conclusion Context-dependent Information • Use of Ontologies • [Ont15] Information Visulaization Information Sharing Personalized Information 030502-Wi-sebis-Master

  50. Share your opinion ... • Can we expect maturity in the field of ontology engineering in 5, 10, 15 years from now? • Is there a way to make information find you rather than look for it? • Is XML the best format to build on? How does it influence ontologies today? 030502-Wi-sebis-Master

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