Description

Although the Semantic Web has sparked considerable attention, there is much confusion around what the Semantic Web represents, what its value is, and how it is achieved. Internet, Enterprise, and Desktop Search is extensively relied upon to find information. However, it is limited to keywords or indexing of the actual text - if a searcher does not know the actual words used in the source document, he or she may not be able to find the information desired, or they may retrieve a mountain of irrelevant information. The Semantic Web (Web 2.0 or 3.0) can enable much more sophisticated information discovery and usage.

Advances in information management are working to enable the development of the semantic web. It is important for IT Managers, Enterprise Architects, Data Architects, Data Modelers, DBA's, Application Developers, and others to understand the Semantic Web and how they can help the enterprise be more effective and efficient by leveraging these technologies. Semantic Web technologies aren't limited to finding information in web pages - a robust implementation of the semantic web will include databases, meta data repositories, and unstructured data sources.

This course will describe what the Semantic Web is and the technologies and standards involved. It will explain all the concepts of this capability and show the benefits: improving integration with ontologies, improving machine actionability, and identify appropriate applications for Semantic Web usage.

Objectives

The objectives of the course are to:

• Understand what is meant by "semantics", and the dis-integration caused by the lack of semantic interoperability
• Understand what the Semantic Web is and the value it provides
• Understand the standards and technology involved the Semantic Web
• Learn how to develop taxonomies and ontologies for effective use by the Semantic Web and other initiatives
• Learn how to tie these to data, meta data, and unstructured data sources to improve human interfaces and machine actionability
• Understand how data management changes with the Semantic Data Web
• When to apply governance to ontologies.. and when not to
• Identify real world applications of the Semantic Data Web

Audience

• Data management professionals
• Data Architects
• Enterprise Architects
• IT Managers
• Data Modelers
• Database Administrators (DBA)
• Application Developers new to semantic technologies
• Meta Data Management professionals
• Content Management professionals

You will:

• Be able to identify and understand the components of the Semantic Web stack, and how these can be used in an enterprise
• Understand how the Semantic Web can extend into databases, meta data repositories, and unstructured data sources
• Develop an architecture that demonstrates how Semantic Web technology can address a real world problem
• Develop a small ontology and demonstrate its use in solving the real world problem
• As a data professional, be able to collaborate more effectively with web developers to utilize appropriate skill sets more effectively and identify appropriate resources (e.g. shared taxonomies, ontologies)

Seminar Content

• Introduction
1. What are semantics?
2. What is the Semantic Web - and what does it mean to me?
3. What do we mean by Semantic Data Web?
• Components of the Semantic Web
  1. Defining ontologies, taxonomies, thesauri, glossaries, controlled vocabularies, and their effects on semantics
  2. Upper ontologies vs. domain specific ontologies
  3. Semantic Web stack components
     • XML, RDF, RDFS, OWL, SPARQL, etc
  4. Semantic Web and SOA
  5. What is open linked data and how can organizations benefit by it?
• Enterprise Information Management and the Semantic Web - overview
  1. Relationship of the Semantic Web to Enterprise Information Management (EIM)
  2. How data modeling is similar and dissimilar to ontologies
  3. Is traditional data modeling still relevant?
• Meta Data Management and the Semantic Web.
  1. Meta data standards (e.g. Dublin Core) and their relationships to the Semantic Web
  2. Using ODM (Ontology Definition Metamodel) to port ontologies to UML for visualization
  3. Using ODM to develop ontologies
  4. Using ODM to port data models to OWL (Web Ontology Language)
  5. Other taxonomy and ontology development tools
• Semantic Data Web and the relational database
• Ghost in the machine: using ontologies for machine actionability
• Real world Semantic technology applications
  1. Intelligence / Law Enforcement
  2. Legal Discovery
  3. Customer Sentiment Analysis
  4. Contract Management
  5. Compliance
  6. Healthcare (where ontologies and controlled vocabularies have been used for years, e.g. ICD, SNOMED CT)
  7. Government (e.g. Federal Enterprise Architecture Reference Model Ontology (FEA-RMO)
  8. Integration and information sharing
  9. and more…
• Extending and using the Semantic Data Web across the enterprise
  1. Data Warehousing/Business Intelligence
  2. Content Management / Unstructured Data Management
  3. Meta Data Repositories
  4. Enterprise Search
  5. Collaboration and automated ontology generation
• Ontologies and Text, Audio, Video Mining techniques
  1. Entity extraction
  2. Classification
  3. Topic tracking
  4. Summarization
  5. Categorization
  6. Clustering
  7. Concept Linkage
  8. Information Visualization
  9. Question Answering
• Semantic Data Web and Data Governance
  1. Data Governance and Stewardship and Ontologies
  2. Unstructured Data Management with the Semantic Web
• Practical next steps
  1. Building a business case for the Semantic Data Web
  2. Iterative approach to developing and deploying the Semantic Data Web
• Conclusion
  1. Additional resources, discussion, case study examination