Cover image for Incentive-Centric Semantic Web Application Engineering

Incentive-Centric Semantic Web Application Engineering

Author Martin Stein , Roberta Cuel , Elena Simperl
Release Date: 2013/01/01
ISBN: 9781608459957
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Book Description

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic.

This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.

Table of Contents

  1. Cover
  2. Half title
  3. Copyright
  4. Title
  5. Contents
  6. Preface
  7. 1 Semantic Data Management: A Human-driven Process
    1. 1.1 Fundamentals of Semantic Data Management
    2. 1.2 Creating, Managing, and Using Semantic Data
      1. 1.2.1 Overview of the Scenarios
      2. 1.2.2 Developing Ontologies
      3. 1.2.3 Creating Instance Data
      4. 1.2.4 Supporting Ontology Development
    3. 1.3 Attracting Human Contributions
    4. 1.4 Examples of Incentivized Semantic Web Applications
      1. 1.4.1 The Social Semantic Web
      2. 1.4.2 The Onto Tube Video Annotation Game
      3. 1.4.3 The Taste It! Try It! Restaurant Reviewing Application
  8. 2 Fundamentals of Motivation and Incentives
    1. 2.1 Introduction
    2. 2.2 Defining Motivation
    3. 2.3 The Concept of Motivation in Organizational Studies
    4. 2.4 Relevant Variables for Semantic Content Creation Tasks
      1. 2.4.1 The Goal of Semantic Content Creation
      2. 2.4.2 The Tasks
      3. 2.4.3 The Social Structure
      4. 2.4.4 The Nature of the Good
    5. 2.5 The Framework
  9. 3 Case Study: Motivating Employees to Annotate Content
    1. 3.1 Aims and Objectives
    2. 3.2 Methods Used
    3. 3.3 Case Study Description: The OKenterprise
      1. 3.3.1 First and Second Phases
      2. 3.3.2 Third Phase
      3. 3.3.3 Fourth Phase: Preliminary Results
      4. 3.3.4 Fourth Phase: The First Laboratory Experiment
      5. 3.3.5 Fourth Phase: The Gamification of the Task
      6. 3.3.6 Fourth Phase: The Second Laboratory Experiment
      7. 3.3.7 Fourth Phase: The Field Experiment
    4. 3.4 Results and Lessons Learned
  10. 4 Case Study: Building a Community of Practice Around Web Service Management and Annotation
    1. 4.1 Aims and Objectives
    2. 4.2 Methods Used
      1. 4.2.1 Usability Test
      2. 4.2.2 Interviews
      3. 4.2.3 Workshop
    3. 4.3 Case Study Description
      1. 4.3.1 Initial Requirement Analysis
      2. 4.3.2 Applying Open Participatory Design
      3. 4.3.3 Increase User Participation by Utilizing Crowdsourcing Mechanisms
      4. 4.3.4 Web Service Annotation Wizard for MTurk
    4. 4.4 Results and Lessons Learned
  11. 5 Case Study: Games with a Purpose for Semantic Content Creation
    1. 5.1 Aims and Objectives
    2. 5.2 Methods Used
    3. 5.3 Case Study Description
      1. 5.3.1 Core Components of GWAPs
      2. 5.3.2 SpotTheLink
      3. 5.3.3 Phrase Detectives
      4. 5.3.4 Who Knows?
      5. 5.3.5 Matchin
      6. 5.3.6 Universe Game
      7. 5.3.7 TubeLink
    4. 5.4 Building New Games
      1. 5.4.1 The OntoGame Generic Gaming Toolkit
      2. 5.4.2 Design Principles and Open Issues
  12. 6 Conclusions
  13. Bibliography
  14. Authors’ Biographies
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