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Information Retrieval on the Semantic Web Using Ontology-based Visualization Larry Reeve INFO780 – XML and Databases Dr. Han - Spring 2004.

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Presentation on theme: "Information Retrieval on the Semantic Web Using Ontology-based Visualization Larry Reeve INFO780 – XML and Databases Dr. Han - Spring 2004."— Presentation transcript:

1 Information Retrieval on the Semantic Web Using Ontology-based Visualization Larry Reeve INFO780 – XML and Databases Dr. Han - Spring 2004

2 2 Overview Semantic Web and Ontologies Semantic Web and Ontologies RDF and OWL RDF and OWL Visualization Uses Visualization Uses Cluster Map Cluster Map Futures Futures

3 3 Semantic Web Machine-processable Web Machine-processable Web How to model meaning?How to model meaning? Common framework that allows data to be shared and reusedCommon framework that allows data to be shared and reused Extension of current webExtension of current web Funding Funding DARPA - $70 millionDARPA - $70 million European Union - € 55 millionEuropean Union - € 55 million

4 4 Existing Web Resources: Resources: identified by URI'sidentified by URI's untypeduntyped Links: Links: href, src,...href, src,... limited, non-descriptivelimited, non-descriptive User: User: Semantics of resource gleaned from contentSemantics of resource gleaned from content Machine: Machine: Little information available - significance of the links only evident from the context around the anchor.Little information available - significance of the links only evident from the context around the anchor. Source: W3C

5 5 Semantic Web Resources: Resources: Globally Identified by URI'sGlobally Identified by URI's or Locally scoped (Blank)or Locally scoped (Blank) ExtensibleExtensible RelationalRelational Links: Links: Identified by URI'sIdentified by URI's ExtensibleExtensible RelationalRelational User: User: Richer user experienceRicher user experience Exchange knowledge effectivelyExchange knowledge effectively Machine: Machine: More processable information is availableMore processable information is available Source: W3C

6 6 Semantic Web Architecture Source: W3C

7 7 Ontology Specification of a conceptualization (Gruber) Specification of a conceptualization (Gruber) Provide common definition of a domain Provide common definition of a domain Documents annotated with metadata to determine “meaning” Documents annotated with metadata to determine “meaning”

8 8 Ontology Play central role in Semantic Web Play central role in Semantic Web Used for: Used for: QueryingQuerying PresentationPresentation NavigationNavigation Move from keyword-based searching to logic-based searching Move from keyword-based searching to logic-based searching

9 9 Keyword Search + Taxonomy

10 10 Ontology Types Lightweight Lightweight Simple keyword hierarchiesSimple keyword hierarchies (Yahoo, Open Directory Project)(Yahoo, Open Directory Project) Well-defined Well-defined Complex concept hierarchies, properties, value restrictions, axiomatised relationshipsComplex concept hierarchies, properties, value restrictions, axiomatised relationships

11 11 Ontology Many ontologies currently defined: Many ontologies currently defined: DAML – DARPA Agent Markup Language (www.daml.org) DAML – DARPA Agent Markup Language (www.daml.org) DAML Ontology Library – 282 entriesDAML Ontology Library – 282 entries Baseball Teams Baseball Teams GPS coordinate systems GPS coordinate systems Employment hierarchy for CMU Employment hierarchy for CMU Stanford Stanford OntoLingua Server (www-ksl-svc.stanford.edu)OntoLingua Server (www-ksl-svc.stanford.edu) Protégé Ontologies Library (protege.stanford.edu)Protégé Ontologies Library (protege.stanford.edu)

12 12 W3C Standards RDF – Resource Description Framework RDF – Resource Description Framework data model for representing resources and their relations between themdata model for representing resources and their relations between them OWL – Web Ontology Language OWL – Web Ontology Language provides a vocabulary for describing properties and classes and allows for greater expressive complexity than RDF aloneprovides a vocabulary for describing properties and classes and allows for greater expressive complexity than RDF alone Both recommendations issued Feb 2004 Both recommendations issued Feb 2004

13 13 RDF Represented using XML Represented using XML An RDF statement is a triple composed of a subject, a predicate, and an object An RDF statement is a triple composed of a subject, a predicate, and an object Each RDF statement is modeled as a graph structure : Each RDF statement is modeled as a graph structure : subjects and objects are nodessubjects and objects are nodes predicate is an arcpredicate is an arc Example: Example: index.html has a creator whose value is John Smithindex.html has a creator whose value is John Smithindex.html has a creator whose value is John Smithindex.html has a creator whose value is John Smith subject(“index.html”)  predicate(“creator”)  object(“John Smith”)subject(“index.html”)  predicate(“creator”)  object(“John Smith”) Helpful in IR by providing more details to a search engine other than keywords Helpful in IR by providing more details to a search engine other than keywords

14 14 RDF Fragment xmlns="http://directory.mozilla.org/rdf"> Top Top Source: Open Directory Project (www.dmoz.org)

15 15 OWL Considered an extension of RDF Considered an extension of RDF The vocabulary provided by OWL describes items such as: The vocabulary provided by OWL describes items such as: relations between classesrelations between classes cardinalitycardinality equalityequality richer typing of propertiesricher typing of properties characteristics of propertiescharacteristics of properties enumerated classesenumerated classes Comprised of three languages: Comprised of three languages: OWL Lite for building classification hierarchies and simple constraintsOWL Lite for building classification hierarchies and simple constraints OWL Description LogicsOWL Description Logics OWL FullOWL Full

16 16 OWL OWL Lite: OWL Lite: for building classification hierarchies and simple constraints for building classification hierarchies and simple constraints OWL Description Logics (DL) OWL Description Logics (DL) provides all OWL features in addition to computational completeness (guaranteed computability of conclusions) as well as decidability (all computations will finish in finite time) provides all OWL features in addition to computational completeness (guaranteed computability of conclusions) as well as decidability (all computations will finish in finite time) OWL Full OWL Full provides all OWL features with no computational guarantees provides all OWL features with no computational guarantees

17 17 OWL Fragment xml:base="http://www.w3.org/2002/03owlt/Ontology/premises001" > Source: W3C

18 18 Ontology-based IViz Ontology Life Cycle Ontology Life Cycle DevelopmentDevelopment IsAViz, Protégé IsAViz, Protégé InstantiationInstantiation Manual, semi-automatic Manual, semi-automatic DeploymentDeployment Analyze, query, and navigate an ontology- based information space Analyze, query, and navigate an ontology- based information space

19 19 Ontology-based IViz Ontology Characteristics Ontology Characteristics Light-weightLight-weight (Taxonomies with few logical class relations) (Taxonomies with few logical class relations) Large number of instancesLarge number of instances Instance overlaps between classesInstance overlaps between classes IncompleteIncomplete

20 20 IViz in Deployment Stage Analysis Visualization Analysis Visualization Overview; pattern detectionOverview; pattern detection Requires: data set, ontology, classifierRequires: data set, ontology, classifier Query Visualization Query Visualization Use ontology in query constructionUse ontology in query construction Query Navigation Query Navigation Information spaces / result setsInformation spaces / result sets

21 21 Analysis Visualization Requires: data set, ontology, classifier Requires: data set, ontology, classifier Analysis within single domain Analysis within single domain Same document set with different ‘perspectives’Same document set with different ‘perspectives’ Comparison of different data sets Comparison of different data sets Information change over time Information change over time

22 22 Economic SectorGeographic Region Analysis within single domain

23 23 Comparison of Different Data Sets Two banking web sites analyzed using the same ontology

24 24 Monitoring Three ontology classes changing over time

25 25 Query Visualization Query Formulation; Review of Results; Query Refinement Query Formulation; Review of Results; Query Refinement

26 26 Query Navigation Visualization is not primary interface Visualization is not primary interface Serves as a global mapServes as a global map Select ontology classes Select ontology classes Documents displayed in text list Documents displayed in text list

27 27 Existing IViz Techniques Hyperbolic Tree Hyperbolic Tree ‘The Brain” ‘The Brain” Self-Organizing Maps (SOMs) Self-Organizing Maps (SOMs)

28 28 Hyperbolic Tree (Source:

29 29 The Brain Source:

30 30 Kohonen SOM Source:

31 31 Cluster Map

32 32 Cluster Map - Class Positioning Spring Embedder algorithm Spring Embedder algorithm Nodes attractNodes attract Edges repelEdges repel …until a stable state is attained…until a stable state is attained Semantic Closeness Semantic Closeness Two classes are close when they share many instancesTwo classes are close when they share many instances Two instances are close when they belong to the same classTwo instances are close when they belong to the same class

33 33 Cluster Map UI

34 34 Cluster Map Advantages All classes and class instances are displayed at one time All classes and class instances are displayed at one time Non-tree like hierarchies can be displayed (not just graph structures) Non-tree like hierarchies can be displayed (not just graph structures) Overlap between classes is exploited Overlap between classes is exploited Good for categorizing IR query results using light-weight ontology Good for categorizing IR query results using light-weight ontology

35 35 Cluster Map Weakness Light-weight ontologies Light-weight ontologies Number of classes small as compared to number of class instances Number of classes small as compared to number of class instances Some classes will be densely populatedSome classes will be densely populated Increasing specialization will helpIncreasing specialization will help Scaling to large number of instances Scaling to large number of instances Doesn’t show document similarity Doesn’t show document similarity Can only view by class membership Can only view by class membership

36 36 Displaying Document Similarity Document analysis is subordinate to navigation and querying Document analysis is subordinate to navigation and querying Can show document list with ranking Can show document list with ranking Seeling Proposal: Seeling Proposal: Document Map VisualizationDocument Map Visualization

37 37 Seeling Visualization Basic idea: Basic idea: Select ontology classSelect ontology class See all documents against the document spaceSee all documents against the document space

38 38 Seeling UI

39 39 Document Similarity – Volvox Extend Cluster Map Extend Cluster Map Replace document containers with volvox containersReplace document containers with volvox containers Retains global displayRetains global display No separate “document space” displayNo separate “document space” display Another benefit - unlimited nesting – allows drilldownAnother benefit - unlimited nesting – allows drilldown Named by Dr. McCain / Henry Small after similarly-shaped microorganismNamed by Dr. McCain / Henry Small after similarly-shaped microorganism Source:

40 40 Cluster Map with Volvox Extension

41 41 Non-class membership Views View data by combining classes View data by combining classes Use information (properties, sub-classes) that relate classes to one another Use information (properties, sub-classes) that relate classes to one another Example: Example: Data about people, projects, and organization- produced papersData about people, projects, and organization- produced papers Visualize people and papers together to show their interactionVisualize people and papers together to show their interaction

42 42 Semantic Views

43 43 Summary Ontologies useful in categorizing IR search results Ontologies useful in categorizing IR search results Cluster Map visualizes small document spaces effectively Cluster Map visualizes small document spaces effectively Can be adapted to handle larger document spacesCan be adapted to handle larger document spaces Alternate views, complex ontologies will require other visualization methods Alternate views, complex ontologies will require other visualization methods More research is needed to support the use of ontologies in visualization More research is needed to support the use of ontologies in visualization

44 44 Information Retrieval on the Semantic Web Using Ontology- based Visualization Questions Questions


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