NextGeneration UserCentered Information Management Ontologybased Information Representation Information

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

Ontology-based Information Representation Outline o Motivation o Semantic Models for Information Representation n Taxonomy n Thesaurus n Topic Map n Ontology o The Semantic Web n URI, XML, RDFS, OWL n Jena o Ontology-Based Information Visualization with Cluster Maps o Conclusion 030502 -Wi-sebis-Master © sebis 2

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

Motivation (2) o Metadata n Information about information resources Object-based information representation n 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 n Metadata about a document in a simple textfield without restrictions? Context-based information representation n Grouping information resources by subjects they are about Semantic models for information representation 030502 -Wi-sebis-Master © sebis 4

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

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

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

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

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

Thesaurus (1) Thesaurus o Motivated by linguistics o Classification of terms based on inheritance, similarity and synonymity o ISO standard: ISO 2788 for monolingual and ISO 5964 multilingual thesauri o [Creighton] 030502 -Wi-sebis-Master © sebis 10

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

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

Topic Map (1) Topic Map o Motivated by mathematical models of how long-term memory works o Classification of terms represented by topics based on n Inheritance n Similarity, synonyms n User-defined relationships o XML Topic Maps n Standard XML format for TM Open Vocabulary n www. Topic. Maps. org o [TM 2] 030502 -Wi-sebis-Master © sebis 13

Topic Map (2) o Information resource optionally identified by URI o Hierarchy of concept represented by a topic described by n Name with the properties - Scope – a set of topics representing a context - Type – a set of topics, a kind of an association between topics n Occurances (properties) connect a topic to an information resource; optionally scope and type n Association (Relationship); optionally scope and type o [TM 3] 030502 -Wi-sebis-Master © sebis 14

Topic Map (3) is. Sibling. Of o Example of Topic Map for „Person“ Person n Toddler Baby n Student School-girl is. Child. Of n Student School-boy has. Child n Name Adult has. Parent n Age Similarity Boy Name Age Girl Synonym Baby Toddler 030502 -Wi-sebis-Master Student School-Boy Baby Toddler Student School-Girl © sebis 15

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

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

Ontology (2) o Ontology for „Person“ has. Child has. Parent Person is. Sibling. Of Adult Child Boy Baby Name Toddler Age is. Child. Of Girl School-Boy School-Girl Student Rules A is. Child. Of B is. Child. Of C A is. Grand. Child. Of C A is. Child. Of B B is. Parent. Of A John Big 030502 -Wi-sebis-Master . . . 6. . . months A is. Child. Of B A has. Parent B © sebis 18

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

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

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

The Semantic Web (2) The Semantic Web o A product of W 3 C (World Wide Web Consortium) headed by Berners-Lee o Goal: lead W 3 to its full potential n Develop common protocols n Control evolution of W 3 n Maintain interoperability of W 3 Semantics and Reasoning Relational Data Exchange 030502 -Wi-sebis-Master © sebis 22

XML (1) XML and XML Schema o e. Xtensible Markup Language n Open vocabulary extensibility n Strict syntax well-formedness n Separation of content different rendering of tree-like documents o XML Schema n Validity o Name. Space n 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 © sebis 23

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

RDF (2) o RDF Document: one description per resource with a list of properties o Description element n may be anonymous (no attributes) n 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 #) o Fundamental Concepts n Object: resource defined by URI n Property: resource n 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 © sebis 25

RDF (3) http: //www. family. org/is. Child. Of http: //www. person. bgr/jane http: //www. person. bgr/john http: //www. family. org/is. Child. Of http: //www. person. bgr/name http: //www. person. bgr/age „ 6 months“ 030502 -Wi-sebis-Master http: //www. person. bgr/tom http: //purl. org/cd/elements/1. 1/creator „John Big“ mailto: tom. big@big. bgr © sebis 26

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

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

RDFS (2) <!DOCTYPE rdf: RDF [ <!ENTITY rdf 'http: //www. w 3. org/1999/02/22 -rdf-syntax-ns#'> <!ENTITY rdfs 'http: //www. w 3. 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: sub. Class. Of rdf: resource="#Music"/> </rdf: Description> <rdf: Description rdf: ID="Concerto"> <rdf: type rdf: resource="&rdfs; Class"/> <rdfs: sub. Class. Of rdf: resource="#Music"/> </rdf: Description> </rdf: RDF> 030502 -Wi-sebis-Master © sebis 29

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

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

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

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

OWL (4) o Example of Ontology for „Man“ <owl: Class rdf: ID="Man"> <rdfs: sub. Class. Of rdf: resource="#Person"/> <rdfs: sub. Class. Of rdf: resource="#Adult"/> <owl: disjoint. With rdf: resource="#Woman"/> </owl: Class> o Example of Ontology for Property „is. Child. Of“ <owl: Object. Property rdf: ID=„is. Child. Of"> <owl: inverse. Of rdf: resource="#is. Parent. Of"/> </owl: Object. Property> 030502 -Wi-sebis-Master © sebis 34

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

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

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

Jena (2) o Jena Architecture n Model Factory creates an empty ontology model that can be added resources, properties, statements Model model = Model. Factory. create. Default. Model(); Model. Factory create. Default. Model: Model 030502 -Wi-sebis-Master © sebis 38

Jena (4) Model create. Resource(String) : Resource create. Property(String): Property create. Statement(Resource, Property, Object): Statements list. Statements(Object, Object) list. Objects. Of. Property(Property) o. Creation of resources, properties and rules Resource john = model. create. Resource(family. URI+“john“); Resource jane = model. create. Resource(family. URI+“jane“); Property child. Of = model. create. Property(relationship. URI); Statement statement = model. create. Statement(john, child. Of, jane); o. Querying of a model. list. Objects. Of. Property(child. Of); model. list. Statements(john, child. Of, null); 030502 -Wi-sebis-Master © sebis 39

Jena (5) Resource o Addition of properies to subjects john. add. Property(child. Of, jane); o Querying of properties john. list. Properties(sibling. Of); Resource add. Property(Property, Object) list. Properties(Property) 030502 -Wi-sebis-Master © sebis 40

Jena (6) RDF Data Query Language (RDQL) n 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/people. Info# ========== http: //www. big. bgr/jane http: //www. big. bgr/tom 030502 -Wi-sebis-Master © sebis 41

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

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

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

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

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

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

Cluster Maps (6) o Clustering based on similarity for Search, Navigation, Vizualization o Advantages n Visible and configurable size of the result set n Similarity between the instances of the result set n Intuitive search and navigation process 030502 -Wi-sebis-Master © sebis 48

Conclusion o Use of Ontologies Context-dependent Information User-centered Information Management! Information Visulaization Information Sharing o [Ont 15] 030502 -Wi-sebis-Master Personalized Information © sebis 49

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 © sebis 50

Refereces n [ABiilsma] Allard Biilsma. De Rode Planeet. www. drp. nl/openmind/ voorbeelden. htm n [JHugo] Jacques Hugo. Visual Literacy and Software Design. http: //www. chisa. org. za/articles/vislit 2. htm n [Creighton] Technology in the Secondary Schools. http: //spahp. creighton. edu/chapman/EDU 342/lesson 3 word/thesaurus_word. htm n [TM 1] www. media-style. com/gfx/assets/topicmap. gif n [TM 2] http: //www. ontopia. net/topicmaps/materials/tm-vs-thesauri. html n [TM 3] http: //sys-con. com/xml n [CM 1] www. touchgraph. com/news 2001. html n [CM 2] www. infovis. net/ n [OWL] http: //www. cs. vu. nl/~guus/public/2004 -webont-zeist/all. htm n [RDFS] http: //www. kanzaki. com/docs/sw/ 030502 -Wi-sebis-Master © sebis 51

Application Area Search Engine Graphical Representation of the results of a search engine Source: www. kartoo. com 030502 -Wi-sebis-Master © sebis 52

Topic Map of Documents o. Distances between topics proportional to semantics o. Colour intensity proportional to pecentage o [TM 1] 030502 -Wi-sebis-Master © sebis 53