Semantic UnderstandingPresentation OutlineGrand ChallengeSlide 4Slide 5Slide 6Slide 7What is Semantic Understanding?Can We Achieve Semantic Understanding?Slide 10Information Value ChainFoundational DefinitionsSlide 13Slide 14Slide 15DataSlide 17Slide 18?Slide 20Slide 21Slide 22Digital CameraSlide 24Slide 25Slide 26Slide 27Car AdvertisementSlide 29Slide 30Airline ItinerarySlide 32Slide 33World Cup SoccerSlide 35Slide 36Slide 37Treadmill WorkoutSlide 39Slide 40Slide 41MapsSlide 43Information Extraction OntologiesWhat is an Extraction Ontology?CarAds Extraction OntologyExtraction Ontologies: An Example of Semantic UnderstandingSlide 48A Variety of ApplicationsApplication #1 Information ExtractionConstant/Keyword RecognitionHeuristicsKeyword ProximitySubsumed/Overlapping ConstantsFunctional RelationshipsNonfunctional RelationshipsFirst Occurrence without Constraint ViolationDatabase-Instance GeneratorApplication #2 Semantic Web Page AnnotationAnnotated Web PageOWLApplication #3 Free-Form Semantic Web QueriesStep 1. Parse QueryStep 2. Find Corresponding OntologyStep 3. Formulate XQuery ExpressionSlide 66Slide 67Step 4. Run XQuery Expression Over Ontology’s Extracted DataApplication #4 Task Ontologies for Free-Form Service RequestsChallenges for Web ServicesAn Ontological SolutionDomain OntologySlide 73Example: Appointment RequestExample: Car Purchase RequestExample: Apartment RequestApplication #5 High-Precision ClassificationAn Extraction Ontology SolutionDensity HeuristicExpected Values HeuristicVector Space of Expected ValuesGrouping HeuristicGroupingApplication #6 Schema Mapping for Ontology AlignmentProblem: Different SchemasSolution: Remove Internal FactoringSolution: Replace Boolean ValuesSolution: Form Attribute-Value PairsSolution: Adjust Attribute-Value PairsSolution: Do ExtractionSolution: Infer MappingsSlide 92Slide 93Slide 94Application #7 Accessing the Hidden WebObtaining Data Behind FormsHidden Web Extraction SystemApplication #8 Ontology GenerationTANGO: Table Analysis for Generating OntologiesRecognize Table InformationConstruct Mini-OntologyDiscover MappingsMergeApplication #9 Challenging Applications (e.g. BioInformatics)Large Extraction OntologiesComplex Semi-Structured PagesAdditional Analysis OpportunitiesSibling Page ComparisonSlide 109Slide 110Slide 111Semi-automatic Lexicon UpdateSeed Ontology RecognitionSlide 114Slide 115Limitations and PragmaticsBusiest Airport?Slide 118Slide 119Slide 120Slide 121Dow Jones Industrial AverageSlide 123Slide 124Some Key IdeasSome Research IssuesSemantic UnderstandingAn Approach Based onInformation-Extraction OntologiesDavid W. EmbleyBrigham Young UniversityPresentation OutlineGrand ChallengeMeaning, Knowledge, Information, DataFun and Games with DataInformation Extraction OntologiesApplicationsLimitations and PragmaticsSummary and ChallengesGrand ChallengeSemantic UnderstandingSemantic UnderstandingCan we quantify & specify the nature of this grand challenge?Grand ChallengeSemantic UnderstandingSemantic Understanding“If ever there were a technology that could generatetrillions of dollars in savings worldwide …, it wouldbe the technology that makes business informationsystems interoperable.”(Jeffrey T. Pollock, VP of Technology Strategy, Modulant Solutions)Grand ChallengeSemantic UnderstandingSemantic Understanding“The Semantic Web: … content that is meaningful tocomputers [and that] will unleash a revolution of newpossibilities … Properly designed, the Semantic Webcan assist the evolution of human knowledge …”(Tim Berners-Lee, …, Weaving the Web)Grand ChallengeSemantic UnderstandingSemantic Understanding“20th Century: Data Processing“21st Century: Data Exchange “The issue now is mutual understanding.”(Stefano Spaccapietra, Editor in Chief, Journal on Data Semantics)Grand ChallengeSemantic UnderstandingSemantic Understanding“The Grand Challenge [of semantic understanding] has become mission critical. Current solutions … won’t scale. Businesses need economic growth dependent on the web working and scaling (cost: $1 trillion/year).”(Michael Brodie, Chief Scientist, Verizon Communications)What is Semantic Understanding?Understanding: “To grasp or comprehend [what’s]intended or expressed.’’Semantics: “The meaning or the interpretation of a word, sentence, or other language form.”- Dictionary.comCan We Achieve Semantic Understanding?“A computer doesn’t truly ‘understand’ anything.”But computers can manipulate terms “in ways that are useful and meaningful to the human user.”- Tim Berners-LeeKey Point: it only has to be good enough.And that’s our challenge and our opportunity!…Presentation OutlineGrand ChallengeMeaning, Knowledge, Information, DataFun and Games with DataInformation Extraction OntologiesApplicationsLimitations and PragmaticsSummary and ChallengesInformation Value ChainMeaningKnowledgeInformationDataTranslating data into meaningFoundational DefinitionsMeaning: knowledge that is relevant or activatesKnowledge: information with a degree of certainty or community agreementInformation: data in a conceptual frameworkData: attribute-value pairs- Adapted from [Meadow92]Foundational DefinitionsMeaning: knowledge that is relevant or activatesKnowledge: information with a degree of certainty or community agreement (ontology)Information: data in a conceptual frameworkData: attribute-value pairs- Adapted from [Meadow92]Foundational DefinitionsMeaning: knowledge that is relevant or activatesKnowledge: information with a degree of certainty or community agreement (ontology)Information: data in a conceptual frameworkData: attribute-value pairs- Adapted from [Meadow92]Foundational DefinitionsMeaning: knowledge that is relevant or activatesKnowledge: information with a degree of certainty or community agreement (ontology)Information: data in a conceptual frameworkData: attribute-value pairs- Adapted from [Meadow92]DataAttribute-Value Pairs•Fundamental for information•Thus, fundamental for knowledge & meaningDataAttribute-Value Pairs•Fundamental for information•Thus, fundamental for knowledge & meaningData Frame•Extensive knowledge about a data itemEveryday data: currency, dates, time, weights & measuresTextual appearance, units, context, operators, I/O conversion•Abstract data type with an extended frameworkPresentation OutlineGrand