The Semantic Web for Knowledge and Data Management: Technologies and Practices | Date: 28 April 2011, 03:40
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The Semantic Web for Knowledge and Data Management: Technologies and Practices (Premier Reference Source)
By Zongmin Ma, Huaiqing Wang
* Publisher: Information Science Reference
* Number Of Pages: 386
* Publication Date: 2008-08-22
* ISBN-10 / ASIN: 1605660280
* ISBN-13 / EAN: 9781605660288
Product Description:
While the current Web provides access to an enormous amount of information, it is currently only human-readable. In response to this problem, the Semantic Web allows for explicit representation of the Semantics of data so that it is machine interpretable.
Semantic Web for Knowledge and Data Management: Technologies and Practices provides a single record of technologies and practices of the Semantic approach to the management, organization, interpretation, retrieval, and use of Web-based data. This groundbreaking collection offers state-of-the-art information to academics, researchers, and industry practitioners involved in the study, use, design, and development of advanced and emerging Semantic Web technologies.
Table of Contents:
Section I
Chapter I: Contextual Hierarchy Driven Ontology Learning
This chapter defines a hierarchical clustering algorithm namely Contextual Concept Discovery (CCD), based on an incremental use of the partitioning algorithm Kmeans, guided by a structural contextual definition and providing a web driven evaluation task to support the domain experts. The CCD algorithm is implemented and tested to incrementally extract the ontological concepts. It is shown that the context-based hierarchical algorithm gives better results than a usual context definition and an existing clustering method.
Chapter II: A Review of Fuzzy Models for the Semantic Web
Based on fuzzy set theory, this chapter reviews the existing proposals for extending the theoretical counterpart of the Semantic Web languages, description logics (DLs), and the languages themselves. The expressive power of the fuzzy DLs formalism and its syntax and semantic, knowledge base, the decidability of the tableaux algorithm and its computational complexity as well as the fuzzy extension to OWL are discussed.
Chapter III: Improving Storage Concepts for Semantic Models and Ontologies
This chapter presents an improved database schema to store ontologies. More specifically, it proposes an intuitive and efficient way of storing arbitrary relationships, shows that their database schema is well suited to store both RDF and Topic Maps, and explains why it is more efficient by comparing it to other approaches.
Chapter IV: Ontologies and Intelligent Agents: A Powerful Bond
This chapter provides an exhaustive description of fundamentals regarding the combination of the Semantic Web and intelligent agent technologies. It is shown that agents build ontologies and agents integrate ontologies in an automatic way.
Chapter V: Probabilistic Models for the Semantic Web – A Survey
Focusing on probabilistic approaches for representing uncertain information on the Semantic Web, this chapter surveys existing proposals for extending semantic web languages or formalisms underlying Semantic Web languages in terms of their expressive power, reasoning capabilities as well as their suitability for supporting typical tasks associated with the Semantic Web.
Chapter VI: Automatic Semantic Annotation Using Machine Learning
This chapter studies the problems of semantic annotation and introduces the state-of-the-art methods for dealing with the problems. It also gives a brief survey of the developed systems based on the methods. Several real-world applications of semantic annotation are introduced as well.
Section II
Chapter VII: Paving the Way to an Effective and Efficient Retrieval of Data over Semantic Overlay Networks
This chapter presents a semantic infrastructure for routing queries effectively in a network of SONs (Semantic Overlay Networks). The chapter defines a fully distributed indexing mechanism which summarizes the semantics underlying whole subnetworks, in order to be able to locate the semantically best directions to forward a query to. It is demonstrated through a rich set of experiments that the proposed routing mechanism overtakes algorithms which are usually limited to the only knowledge of the peers directly connected to the querying peer.
Chapter VIII: SWARMS: A Platform for Domain Knowledge Management and Applications
This chapter describes the architecture and the main features of SWARMS, a platform for domain knowledge management. The platform aims at providing services for (1) efficiently storing and accessing the ontological information; (2) visualizing the networking structure in the ontological data; (3) searching and mining the semantic data.
Chapter IX: Modeling and Querying XML-based P2P Information Systems: A Semantics-based Approach
This chapter first focuses its attention on the definition and the formalization of the XML-based P2P Information Systems class, also deriving interesting properties on such systems. Then the chapter presents a knowledge-representation-and-management-based framework that can efficiently process knowledge and support advanced IR techniques in XML-based P2P Information Systems.
Section III
Chapter X: Applying Semantic Web to E-Tourism
This chapter presents Travel Guides - a prototype system for tourism management to illustrate how semantic web technologies combined with traditional E-Tourism applications help integration of tourism sources dispersed on the Web, and enable creating sophisticated user profiles.
Chapter XI: Semantic Web in Ubiquitous Mobile Communications
This chapter provides background information on the Semantic Web field, discusses other research fields that bring semantics into play for reaching the ontology-enabled ubiquitous mobile communication vision, and exemplifies the state of the art of ontology development and use in telecommunication projects.
Chapter XII: Design Diagrams as Ontological Sources: Ontology Extraction and Utilization for Software Asset Reuse
This chapter discusses how semantic web technologies can help solution-building organizations achieve software reuse by first learning ontologies from design diagrams of existing solutions and then using them to create design diagrams for new solutions. The proposed technique, called OntExtract, extracts domain ontology information (entities and their relationship(s)) from class diagrams and further refines the extracted information using diagrams that express dynamic interactions among entities such as sequence diagram.
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