Doing Clever Things with the Semantic Web

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Invited talk at the EPIA 2011 conference

Invited talk at the EPIA 2011 conference

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  • 1. Doing Clever Things With the Semantic Web
    Mathieu d’Aquin
    Knowledge Media Institute, the Open University
  • 2. The Semantic Web
    Using the Web to publish, share and exploit information/knowledge
    From machines to machines
    Using graph-based data modeling, knowledge representation (ontologies) and reasoning
  • 3. Linked Data
    http://lucero-project.info/lb/what-is-linked-data/
    As set of principles and technologies for a Web of Data
    Putting the “raw” data online in a standard representation (RDF)
    Make the data Web addressable (URIs)
    Link to other Data
    http://linkeddata.org
  • 4. Galen
    NCI

    Music
    DC
    WORDNET
    RSS
    TAP
    FOAF




    Metadata
    <rdf:RDF>
    <channel rdf:about=“http://watson.kmi.open.ac.uk/blog”>
    <title>Elementaries - The Watson Blog</title>
    <link>http://watson.kmi.open.ac.uk:8080/blog/</link>
    <description>
    "Oh dear! Where the Semantic Web is going to go now?" -- imaginary user 23
    </description>
    <language>en</language>
    <copyright>Watson team</copyright>
    <lastBuildDate>Thu, 01 Mar 2007 13:49:52 GMT</lastBuildDate>
    <generator>Pebble (http://pebble.sourceforge.net)</generator>
    <docs>http://backend.userland.com/rss</docs>

    <rdf:RDF>
    <foaf:Imagerdf:about='http://static.flickr.com/132/400582453_e1e1f8602c.jpg'>
    <dc:title>Zen wisteria</dc:title>
    <dc:description></dc:description>
    <foaf:pagerdf:resource='http://www.flickr.com/photos/xcv/400582453/'/>
    <foaf:topicrdf:resource='http://www.flickr.com/photos/tags/vittelgarden/'/>
    <foaf:topicrdf:resource='http://www.flickr.com/photos/tags/wisteria/'/>
    <dc:creator>
    <foaf:Person><foaf:name>Mathieu d'Aquin</foaf:name>

    <rdf:RDF>
    <owl:Ontology rdf:about="">
    <owl:imports rdf:resource="http://usefulinc.com/ns/doap#"/>
    </owl:Ontology>
    <j.1:Organization rdf:ID="KMi">
    <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string"
    >The Knoledge Media Institute of the Open University, Milton Keynes UK</rdfs:comment>
    </j.1:Organization>
    <j.1:Document rdf:ID="KMiWebSite">

    UoD
  • 5. The Semantic Web
    Knowledge/
    Problem Solving Methods
    Semantic Web Applications
    Doing Clever Things With the Semantic Web
    Intelligent Agent
    Smart Features
    Clever Things…
  • 6. What I Want to Talk About
    Using the Semantic Web as A Knowledge Base
    KMi Watson and Finding Ontologies
    Doing More with Links
    Exploring the Web of Data
    Back to the Future
    AI + Linked Data = Semantic Web?
  • 7. Using the Semantic Web
    Need for a Gateway to the Semantic Web
    Dynamically retrieving, exploiting and combining relevant semantic resources from the Semantic Web
  • 8. KMi Watson
  • 9. Architecture
  • 10. Interface
    http://watson.kmi.open.ac.uk
    Watson: More than a Semantic Web Search Engine, Semantic Web Journal
  • 11. Watson as a Service
    Providing Web accessible APIs to a collection of online ontologies and semantic data sources
  • 12. Chose an entity to search
    Integrate statements
    Into the edited ontology
    Get entities from online ontologies
    Example Application: Ontology Construction
    Reusing Knowledge from the Semanrtic Web with the Watson Plugin, Demo at ISWC 2008
  • 13. Concept Relation Discovery
    SeaFood
    Meat
    wine.owl
    AcademicStaff
    Semantic Web
    Semantic Web
    Researcher
    ka2.rdf
    Meat
    SeaFood
    Ham
    pizza-to-go
    NALT
    AcademicStaff
    Researcher
    Ham
    SeaFood
    ISWC
    SWRC
    NALT
    Agrovoc
  • 14. Exploring the Semantic Web as Background Knowledge for Ontology Matching, Journal of Data Semantics
  • 15. PowerAqua: Question Answering
  • 16. From Semantic Web Research to Linked Data Applications
    Watson as a platform to research applications and techniques on top of semantic web resources
    But how can the Semantic Web be exploited and used in real-world application?
    Starting from what we know best…
  • 17. Applying Linked Data
    The Open University is the largest University in the UK, where all the courses are realized at a distance
    Creating the first University Linked Data platform: data.open.ac.uk
    Demonstrate the value of the technology and push the research through real-world scenarios
  • 18. data.open.ac.uk
  • 19. Applications
    Mobile and Personal Semantics
    Social
    Resource Discovery
    Research
    Exploration
  • 20. Example application: Finding relevant resources
    Zablithet al, LinkedLearning 2011
  • 21. Data as Web resources, accessible everywhere
  • 22. See also: Zablith et al., COLD 2011
  • 23. http://people.kmi.open.ac.uk/mathieu/about/discobro-discovering-linked-datat-resources-while-browsing/
    http://discovery.ac.uk/developers/competition/
  • 24. Supporting Researchers: The Reading Experience Database
    http://www.open.ac.uk/Arts/reading/
    40,000 accounts of somebody reading something at some time in some place
    Used by researchers in literature and history to explore research hypotheses
  • 25. Event
    Location
    locatedIn
    subClassOf
    subClassOf
    Experience
    City
    Country
    date: Date
    readerInvolved
    originCountry
    textInvolved
    occupation
    givesBackgroundTo
    Person
    religion
    gender
    creator/editor
    LinkedEvent Ontology
    Document
    CITO Citation Ontology
    Dublin Core
    title: String
    description: String
    published: Date
    providesExcerptFor
    FOAF
    DBPedia
  • 26.
  • 27. Back to the Future
    The Semantic Web is both a vision and a reality
    Making the Web more than a network of documents: the biggest, most distributed knowledge base ever
    What could AI do with such a knowledge base?
  • 28. Linked Data Mining
    Finding unexpected patterns in the use of the distributed data graph
  • 29. Linked Data Mining: Example
    Using Formal Concept Analysis + Reasoning to build a hierarchy of questions a linked dataset can answer
    Use statistical metrics to identify the ones that are most likely to be interesting
    Extracting Relevant Questions to an RDF Dataset Using Formal Concept Amalysis at KCAP 2011
    http://lucero-project.info/lb/2011/06/what-to-ask-linked-data/
  • 30.
  • 31.
  • 32. Reasoning
    To analyze and understand raw data in relation with online resources
    Example: Online personal information management
    Online Activities Ontology
    HTTP Ontology
    Parameters and Website info.
    Web Site Information
    Personal Information
    Trust Model
    Location Information
  • 33. Enriched with linked data
    Google Services
    Entertainment Websites
    Web Analytics
    Internet Search Engine
    subject/category
    Video sharing
    Video Hosting
    www.google-analytics.com
    Company
    developer
    Web Search Engine
    Search Engine
    type
    subject/category
    google
    owner
    subsediaryOf
    www.youtube.com
    www.google.com
    parent
    DBpedia
    freebase
  • 34. Basic processing/analysis
    Requests by time of day
    Requests by User Agents
    Interests
    Trust
  • 35. http://uciad.info
    http://uciad.info
  • 36. Understanding knowledge representation and data modeling
    The Semantic Web also represents a very large, collaborative base of formally represented knowledge
    This can also be mined, to discover things about knowledge representation and data modeling
  • 37. Ontologies on the Semantic Web
    Underlying description logic
    Number of entities
    Domain covered
  • 38. Relationships between ontologies
    DOOR: Towards a Formalization of Ontology Relations at KEOD 2009
  • 39. Detecting versions of ontologies
    When published on the Web, the information about the evolution of ontologies is lost
    Using URI patterns to find candidate versions of ontologies
    http://loki.cae.drexel.edu/wbs/ontology/2003/10/iso-metadata
    http://loki.cae.drexel.edu/wbs/ontology/2004/01/iso-metadata
    Applying machine learning algorithms (SVM, Naïve Bayes and Decision tree to recognize chains of versions of ontologies
    Allocca at ESWC 2011
    Obtained 90% Precision (SVM)
    Collected thousands of ontology version sequences to be analysed
    For example, distribution of similarity in version and non-version ontologies (right)
  • 40. Agreement/Disagreement between ontologies
    Ontologies are knowledge artifacts, they express opinions and beliefs and contradict each others
    Assessing (dis)agreement in ontologies is very useful to understand how to combine knowledge from different sources
  • 41. Assessing Statements related to SeaFood
    Nb1: #ontologies in which the statement appears.Nb2: #ontologies containing entities matching the subject and object of the statement.
    a: global agreement, d: global disagreement, cs: consensus, ct: controversy
  • 42. 21 different ontologies with a SeaFood concept
    Disagreement
    Agreement
    Formally Measuring Agreement and Disagreement in Ontologies at K-CAP 2009
  • 43. http://uciad.info
    Vegan subClass Vegetarian
    SeaFoodsubClassOf Meat
    SeaFooddisjointWith Meat
  • 44. The brighter the blue the higher the positive consensus (higher agreement)
    The brighter the red the lower the negative consensus (higher disagreement)
    Dark = controversy: no clear cut between disagreement and agreement
    Example: The statements attached to the class Employee are controversial: some ontologies agree, others disagree (often due to alternative representations of roles)
    AKT Portal
    Using consensus to assess an ontology(a new NeOn toolkit plugin
    Visualizing Consensus with Online Ontologies to Support Quality in Ontology Development at ONTOQUAL@EKAW 2010
  • 45. So my point is…
    The Semantic Web is a fantastic open field for AI
    It is going to become omnipresent, hidden, personal
    Exploring, Exploiting and Excavating the Semantic Web for
    Research in technology (creating it and studying it)
    Research in other areas
    Everyday tasks
    Still, after 10 years of research, represent new directions for many fields of the AI community, with their own issues, challenges and applications
  • 46. Thank You!
    More at:
    http://people.kmi.open.ac.uk/mathieu
    m.daquin@open.ac.uk
    @mdaquin