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					OWL
Representing Information Using
the Web Ontology Language
Section 1
Section 1
• Chapter 1: Historical Web
 ▫ Web history, context, features, & shortcomings
• Chapter 2: Semantic Web
 ▫ Challenges, requirements, & solutions
• Chapter 3: Ontologies
 ▫ Concepts, purposes, relationships, features, &
   languages
• Chapter 4: OWL Introduction
 ▫ OWL language, layered architecture, & supporting
   technologies
Chapter 1
1 Current Web

• Publishing medium
• Dominated by HTML
 ▫ Hyper Text Markup Language
• Pages accessible using URLs
 ▫ Uniform Resource Locators
 ▫ http://www.w3.org/
• Supports human readers using browsers
1.1 Current Web History
• Internet infrastructure created by DARPA
• Mostly text-based (telnet, ftp, gopher)
• 1992: Tim Berners-Lee/CERT developed
 ▫ HTML & HTTP (Hyper Text Transfer Protocol)
 ▫ Web browser (Mosaic)
• Allows anyone to publish structured documents
  connected by hyperlinks
• Combined with TCP/IP and XML (eXtensible
  Markup Language) to create “killer app”
1.2 Current Web Characteristics

• Features

• Benefits

• Applications
1.2.1 Current Web Features
•   Diverse
•   Document-centric
•   Virtual repository of information
•   No controlling authority
•   Managed by open standards from W3C
    ▫ World Wide Web Consortium
• Intended for human access & reading
1.2.2 Current Web Benefits
•   Superior to private networks
•   Transactions are cheaper (self-service)
•   Cheap to communicate world-wide
•   Created online communities
    ▫ Open-source movement – free high-quality tools
    ▫ Countless online forums
1.2.3 Current Web Applications
• Most content designed for humans
• Variety of purposes
 ▫   E-commerce
 ▫   Education
 ▫   Financial services
 ▫   Auctions
 ▫   Music
• Many sites use generated HTML & XML
  generated from databases
1.3 The Web is Not Enough
• Not enough structure to support computer
  processing of content
• No way to connect information to enable
  complex queries
• HTML too focused on format/display
• Need to add markup to explain meaning
  (semantics)
• Semantics will enable automated interpretation
  of structured web content
1.3.1 Information Structure
• HTML documents
 ▫ Semi-structured formatting
 ▫ Unstructured text
• Natural Language Processing (NLP)
 ▫ Improving, but impractical on a large scale
• Structured database information must be shared
  in a computer-parseable maner
• Goal: allow automated software agents to mine
  the web, creating new functionality
1.3.2 Finding Requires Metadata
   “Find the cheapest Key lime pie within 5 miles.”

• Keyword-based search engines
▫ Find pages that might contain desired content
▫ Don’t provide answers to questions…the goal!
▫ Have to find local restaurants, then look at their menus
• Query engines aim to answer questions
▫ Should be able to filter restaurants within 5 miles, access
  menus, compare prices, get answer
▫ Show how answer gotten from reliable sources
1.3.3 Semantics Must Be Explicit

• Providing semantic information explicitly in
  documents enables software to:
 ▫ Manipulate information (filter, summarize)
 ▫ Infer new facts (inference)
 ▫ Link multiple distributed information
   representations (semantic join)
1.4 Current Web Summary
• Current Web
 ▫ Document-centric
 ▫ Focused on humans using browsers
 ▫ Insufficient for automated data processing
• New technologies needed
 ▫ Structure information for automated processing
 ▫ Improve searches
 ▫ Link disparate data sources with each other
• The Semantic Web!
Chapter 2
2 Semantic Web Introduction

• Web information representation challenges

• Requirements for a solution

• Semantic Web concepts that satisfy those
  requirements
2.1 Web Information Representation
Challenges

• Increased Need for Information Representation

• Ambiguous Human Descriptions

• Software Demands for Specificity
2.1.1 Information Representation

• Volume of information increasing exponentially
• User expectations of the Internet also growing
• To satisfy expectations, we need more than just
  HTML, XML & databases
2.1.2 Ambiguous Descriptions
• Many human information formats
 ▫   Specialized domains with unique terminology
 ▫   Regional language differences
 ▫   Many sublanguages within communities
 ▫   Difficult to get consensus
• Language agreement impossible
• Meta-language agreement possible
 ▫ Language to express language
• We need a language that can represent
  information from many domains
2.1.3 Demands for Specificity

• Computers need information to be
 ▫   Structured
 ▫   Consistent
 ▫   Well-formed
 ▫   Logical
2.2 Requirements for a Solution

• Minimize Human Investment

• Satisfy Computer Requirements

• Compromise between these goals
2.2.1 Minimize Human Investment

• Information Representation Producers

• Information Representation Consumers

• Requirements common to both
2.2.1.1 Representation Producers
• Provide content from existing sources
• Aim to generate information representations
 ▫ Quickly
 ▫ Effectively
 ▫ Inexpensively
• Represent data using natural models that are
 ▫ Extendable
 ▫ Versionable
 ▫ Configuration-managed
2.2.1.2 Representation Consumers
• Aim to create software to
 ▫ Parse information
 ▫ Interpret information
 ▫ Manipulate information
• Software should be able to
 ▫ Combine information from different domains
 ▫ Use others’ data without needing to understand
   the underlying data model
 ▫ Reduce human intervention

				
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posted:7/14/2011
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