Taxonomies & Classifications for Organizing ContentWhat do we know about taxonomies?Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Information versus KnowledgeSlide 22Information versus KnowledgeSlide 24BibliographySlide 26Slide 27Slide 28Taxonomies & Classifications Taxonomies & Classifications for for Organizing ContentOrganizing ContentQuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.Ontology comes from the Greek ontologia. Onto = the science of existence Logia =talking about being Who gets credit for taxonomies? What do we know about taxonomies?Aristotle is the founder of taxonomy.•His ideas represent the foundation for object-oriented systems•He introduced a number of inference rules (syllogisms) used in modern logic-based reasoning systemsWhy is it, that in the last decade ( 2000 years after A) that knowledge representations & ontologies have gained importance? •Agent communication (Automated data mining)•Artificial Intelligence (Cyc)•Description of content to facilitate its retrieval (Intelligent searches)•Ecommerce (Amazon)•E-science experiments•E-learning systems•Information integration (Personalized newspapers & journals)•Intelligent devices (Management of Remote equipment)•Knowledge management (Corporate Intranet)•Speech and natural language understanding•Web Service discovery (Mobile devices)•Etc, etc, etc, whatever the humankind concocts (the MATRIX)•Automated data mining•Artificial Intelligence •Intelligent searches•Amazon•E-science experiments•E-learning systems•Personalized newspapers & journals•Intelligent devices •Knowledge management •Speech and natural language understanding•Web Service discovery Through the use of ONTOLOGIES, they attempt to represent knowledge in such a way that it can be understood by a computer and have the computer use this knowledge in real time.What do all of these things have in common?•Multiple groups of people are conceptualizing different ways to represent knowledge and the programs they write have different conceptual backgrounds:learning theory, psychology, philosophy, logic, computer science What are the ontological challenges?•Ontologies can differ depending on the needs/conventions of the producers & the consumers of the knowledge being represented.•The word ontology is used to describe different degrees of structureFor example the word APPLIANCE has many different meanings:QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.QuickTime™ and aTIFF (Uncompressed) decompressorare needed to see this picture.•Ontologies can differ depending on the needs/conventions of the producers & the consumers of the knowledge being represented.An ontology about the domain of APPLIANCE could model:•Household Appliances (small & major) - blenders, expresso machine, stoves, washer/dryers, etc. •Computer Appliances - 1U, software, virtual, etc.•Orthodontic Appliances - braces, retainers, etc.Domain ontologies represent concepts in very specific and often eclectic ways, thus they are often incompatible. Furthermore, different ontologies in the same domain can also arise due to different perceptions of the domain based on cultural background, education, ideology, or because a different representation language was chosen•The word ontology has been used to describe artifacts with different degrees of structure. Simple taxonomies Metadata schemes Logical theories YAHOO DUBLIN CORE CYCAIDAML DARPAGLHACHTMLIEILPIRJSKBKMKRLSILSAMRDMTMUCNERNLPNPOILOWLPLSIPMIPOSPPRDF(S)SVMsVPQAUMLXMLXML-DTDWSDArtificial IntelligenceAgent Markup LanguageGenerative LexiconHierarchical Agglomerative Knowledge ClusteringHyperText Markup LanguageInformation ExtractionInductive Logic ProgrammingInformational RetrievalJensen-Shannon divergenceKnowledge BaseKnowledge ManagementKnowledge RepresentationLatent Semantic IndexingLatent Semantic Analysis (=LSI)Machine Readable DictionaryMachine TranslationMessage Understanding ConferencesNamed Entity RecognitionNatural Language ProcessingNoun PhraseOntology Inference LayerWeb Ontology LanguageProbabilistic Latent Semantic IndexingPointwise Mutual InformationPart Of SpeechPrepositional PhrasesResource Description Framework (Schema)Support Vector MachinesVerb PhraseQuestioning AnsweringUnified Modeling LanguageeXtensible Markup LanguageXML-Document Type DefinitionWord Sense DisambiguationRegardless of these differences, in one way or another an ontology looks at a domain in terms of: • Classes (general things) in the many domains of interest• The relationships that can exist among things• The properties (or attributes) those things may haveCyc A project started in Austin, Texas by Doug Lenat as part of Microelectonics and Computer Technology. It is an AI project that attempts to assemble a comprehensive ontology and database of everyday common sense knowledge, with the goal of enabling AI applications to perform human-like reasoning. The original knowledge base is proprietary, but now there is an open version.WordNet A semantic lexicon for the English language. The purpose is twofold: •to produce a combination of dictionary and thesaurus that is more intuitively usable•to support automatic text analysis and AI applications.The Dublin Core A metadata element set is a standard for cross-domain information resource description. It provides a simple and standardized set of conventions for describing things online in ways that make them easier to find. Dublin Core is widely used to describe:• Digital materials such as video•Sound•Image•Text•Composite media like web pages.Suggested Upper Merged Ontology or SUMO It was originally developed by the Teknowledge Corporation and now is maintained by Articulate Software. SUMO originally concerned itself with meta-level concepts and thereby would lead naturally to a categorization scheme for encyclopedias. It has now been considerably expanded to include a mid-level ontology and dozens of domain ontologies. SUMO was first released in December 2000.Web Ontology Language or OWL W30 trying to define an ontology that can be used across all domains and applications:•Agent communication•Artificial Intelligence •Description of content to facilitate its retrieval •Ecommerce •E-science