TOP INTRO TO MULTIMEDIA NTWRK
TOP INTRO TO MULTIMEDIA NTWRK CS 410
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This 28 page Class Notes was uploaded by Orrin Rutherford on Wednesday September 2, 2015. The Class Notes belongs to CS 410 at Portland State University taught by Staff in Fall. Since its upload, it has received 10 views. For similar materials see /class/168283/cs-410-portland-state-university in ComputerScienence at Portland State University.
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Date Created: 09/02/15
Ontologies Overview Background Semantic Web What is an ontology Why are ontologies useful OWL overview Ontology Design Protege Semantic Web Web is increasingly important for scientists Locating papers Accessing online databases General search engines eg Google and domainsearch engines can help What are some limitations of search engines Semantic Web Vision The Semantic Web is an extension of the current Web in which information is given welldefined meaning enabling computers and people to work in better cooperation The web will reach its full potential when it becomes an environment where data can be shared and processed by automated tools as well as by people Tim BernersLee and Eric Miller Semantic Web in Science Researchers at one level of analysis may need to explore results from a different level Researchers may need to search results from another field May not know right keywords to search for Information integration from multiple sites Nontextual information Making web data machinereadable Semantic Web Improve communications between people using different technologies Extend interoperability of databases Tools for interacting with multimedia coHec ons Mechanisms to support agentbased computing People and machines working interactively Example National Cancer Institute Turn vocabulary of cancer research terms into machine readable ontology Expanded Thesaurus that delineates relationships between vocabulary items Build a knowledge base not restricted to particular keywords Situate knowledge base in a distributed way among documents and resources on the web Encme malyzer 533mb Image by J Hendler unuutmf lquotnnnillu rnanlsalf mull Em Hills antllrul 39 Eme uf1rll lliul Ina Chum in L Eme ismlwl lli ageHts 9 Da ahasae Elana339 55mm dmrip u Daiatrasa So how do we do this Ontologies Languages eg RDF OWL to express ontologies Software tools for developing and reasoning about ontologies Agents and search technologies What is an ontology Terms used to describe and represent an area of knowledge Used by people databases and applications to describe an area of knowledge Computeruseable definitions of basic concepts in a domain and relationships among them Ontology Examples Taxonomies on the Web Yahoo categories Catalogs for online shopping Amazoncom product catalog Domainspecific standard terminology SNOMED Clinical Terms terminology for clinical medicine UNSPSC terminology for products and services Slide by N Noy and 8 Tu More details Range from simple taxonomies to complex metadata schemes Descriptions for Classes general things in domain of interest Relationships that can exist among things Properties or attributes those things may have Why are ontologies useful Enable semantics of documents to be used by applications and agents Standardize metadata terms within a community Enable reuse of domain knowledge Make domain assumptions explicit Why are ontologies useful Associations between concepts Information extraction Information integration Automatic reasoning Reasoning Example Parent is a more general relationship than mother Mary is the mother of Bob Conclusion Mary is a parent of Bob Reasoning allows us to answer query Who are Bob s parents Why Develop an Ontology To share common understanding of the structure of information among people among software agents To enable reuse of domain knowledge to avoid reinventing the wheel to introduce standards to allow interoperability Slide by N Noy and 8 Tu More Reasons To make domain assumptions explicit easier to change domain assumptions consider a genetics knowledge base easier to understand and update legacy data To separate domain knowledge from the operational knowledge reuse domain and operational knowledge separately eg configuration based on constraints Slide by N Noy and 8 Tu An Ontology s Often Just the Beginning Declare OHtOIOQIGS structure Provide domain description Slide by N Noy and 8 Tu Background XM LSyntax for structured documents but no semantic constraints XML Schema language for restricting structure of XML documents RDF data model for objects resources and relations between them RDF Schema vocabulary for describing properties and classes of RDF resources What is missing more complex relationships Domainrange manytoone vs manytomany etc DAMLOL DAML DARPA Markup Language OIL Ontology Inference Layer Extension to XML and RDF to express precise constraints on classes and properties 20 OWL Web ontology language Derived from DAM LOL Adds capabilities compared to RDF Relations between classes eg disjointness Cardinality eg exactly one Equality Rich typing of properties Characteristics of properties Enumerated classes 21 OWL Components Individuals Objects in a domain Objects with different names might be the same Properties Binary relations on individuals eg hasSibling Can have inverses eg hasOwner and OwnedBy Can be transitive Can be symmetric 22 OWL Components continued Classes Sets that contain individuals Described using formal descriptions that state requirements for membership May be organized into superclasssubclass hierarchy taxonomy Cat is a subclass of animal Superclasssubclass relationships may be computed by a reasoner 23 OWL Schema Features Class group of individuals that share some properties rdfs39subClassOf create hierarchies rd 39Property relationships between individuals rdfssubPropertyOf create property hierarchies rdfsdomain and rdfsrange Vidual 24 OWL Equality equivalentCass equivalentPropen y sameAs differentFrom AllDifferent distinctMembers 25 OWL Property Characteristics ObjectPropen y DatatypePropen y inverse Of TransitivePropen y SymmetricPropen y FunctionalPropen y lnverseFunctionaProperty 26 OWL Sublanguages OWL Lite Syntactically simple Best when only simple hierarchy and simple constraints needed OWL DL Based on Description Logics Enables automated resoning Checking for inconsistencies OWL Full Highly expressive But can t guarantee decidability 27
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