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Lecture #11: Ontology Engineering Dr. Bhavani Thuraisingham
Building Trustworthy Semantic Webs Lecture #11: Ontology Engineering Dr. Bhavani Thuraisingham September 25, 2006
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Objective of the Unit This unit will provide an overview of the software engineering aspects of ontologies. The field is called Ontology Engineering
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Outline of the Unit Summary of semantic web technologies discussed so far Applications discussed so far Ontology Engineering Directions
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Semantic web technologies discussed so far
XML, XML Schema RDF, RDF Schema Ontology, OWL Logic, Rules, Inference Some discussion of security issues for each technology Policy specification, Securing documents
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Types of Application Horizontal Information Products at Elsevier: Integration Data integration at Audi: Integration Skill finding at Swiss Life: Search Think Tank Portal at EnterSearch: Knowledge man agent E-Learning: Knowledge management Web Services: Web services (for any of the other applications discussed) Multimedia Collection at Scotland Yard: Searching Online Procurement at Daimler Chrysler: E-Business Device Interoperability at Nokia: Interoperability
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Revisiting Ontology Common definitions for any entity, person or thing
Several ontologies have been defined and available for use Defining common ontology for an entity is a challenge Mappings have to be developed for multiple ontologies Specific languages have been developed for ontologies RDF, OWL, DAML+OIL, etc.
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What is Ontology Engineering?
Tools and Techniques to Create Ontologies Specify Ontologies Maintain Ontologies Query Ontologies Evolve Ontologies Reuse Ontologies Incorporate features such as security, data quality, integrity
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Manual Constructiob of Ontologues
Determine Scope Consider Reuse Enumerate Terms Define Taxonomy Define Properties Define facets Define Instances Check for Anomalies
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Reuseing Exitsing Ontologies
The goal is not to reinvent the wheel Several ontologies have been developed for different domains Codieid Bodies of Expert Knowledge Integrated Vocabularies Upper Level Ontologies Topic Hierarchies Linguistic Resources Ontology Libraries
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Semi/Automatics Methods for Ontology Generation
Much of the research is focusing on developing ontologies using tools from multiple heterogeneous data sources Essentially extracting concepts and expanding on concepts from the data sources Uses combination of data integration, metadata extraction, and machine learning techniques E.g. Clustering of concepts, Classification of concepts etc. Text Book describes Semantic Web Knowledge Management Architecture
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What is Knowledge Management?
Knowledge management, or KM, is the process through which organizations generate value from their intellectual property and knowledge-based assets KM involves the creation, dissemination, and utilization of knowledge Ontologies are a form of Knowledge? Reference: management.htm?source=google
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Directions Need tools for developing semantic web technologies
XML documents, RDF documents, Ontologies, etc. How to integrate the multiple ontologjes and tools? Role of Agents – agents are processes that reasons with semantic web technologies Semantic web services, data mining, knowledge management integrated
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