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2007 | Buch

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Semantic Web: Concepts, Technologies and Applications

verfasst von: Karin Koogan Breitman, MSc, DSc, Marco Antonio Casanova, PhD, Walter Truszkowski, MA, BA

Verlag: Springer London

Buchreihe : NASA Monographs in Systems and Software Engineering

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Although the Web is growing at an astounding pace, surpassing the 8 billion page mark, most pages are still designed for human consumption and cannot be processed by machines. Computers are used to display the information, but human intervention is still required to interpret the results. The Semantic Web unleashes a revolution of new possibilities in which content is given formal, machine processable semantics.

This book provides a well-paced introduction to the Semantic Web. It covers a wide range of topics, from new trends (ontologies, rules) to existing technologies (Web Services and software agents) to more formal aspects (logic and inference). It includes: real-world (and complete) examples of the application of Semantic Web concepts; how the technology presented and discussed throughout the book can be extended to other application areas, i.e. Geographic Information Sciences, Bioinformatics and Fine Arts.

Inhaltsverzeichnis

Frontmatter

Introduction

Frontmatter

1. The Future of the Internet

Abstract

In the beginning of the Internet days, software programmers developed all Web pages. Today, the Web provides perhaps the simplest way to share information, and literally everyone writes Web pages, with the help of authoring tools, and a large number of organizations disseminate data coded in Web pages. The Hypertext Markup Language (HTML) is typically the language used to code information about renderization (font size, color, position on screen, etc.) and hyperlinks to other Web pages or resources on the Web (multimedia files, text, e-mail addresses, etc.).

Concepts

Frontmatter

2. Ontology in Computer Science

Abstract

The word ontology comes from the Greek ontos (being) + logos (word). The Merriam Webster online dictionary defines the term ontology as:

A branch of metaphysics concerned with the nature and relations of being.

A particular theory about the nature of being or the kinds of existents.

The term ontology was introduced in philosophy, in the nineteenth century, by the German philosopher Rudolf Gockel, in his Lexicon Philosophicum, to distinguish the study of “being” from the study of various kinds of beings in the natural sciences. As a philosophical discipline, ontology building is concerned with providing category systems that account for a certain vision of the world.

3. Knowledge Representation in Description Logic

Abstract

Description logic denotes a family of knowledge representation formalisms that model the application domain by defining the relevant concepts of the domain and then using these concepts to specify properties of objects and individuals occurring in the domain (Baader and Nutt 2003). As the name implies, research on description logic emphasizes a careful formalization of the notions involved, and a preoccupation with precisely defined reasoning techniques. Note that we prefer the singular form, description logic, rather than the plural form, description logics, in spite of the fact that we are talking about a family of formalisms.

4. RDF and RDF Schema

Abstract

The Resource Description Framework (RDF) is a general-purpose language for representing information about resources in the Web. It is particularly intended for representing metadata about Web resources, but it can also be used to represent information about objects that can be identified on the Web, even when they cannot be directly retrieved from the Web. To some extent, RDF is a lightweight ontology language designed to support interoperability between applications that exchange machine-understandable information on the Web. RDF is currently defined by a set of W3C recommendations, published on February 10th, 2004.

5. OWL

Abstract

The Web Ontology Language (OWL) describes classes, properties, and relations among these conceptual objects in a way that facilitates machine interpretability of Web content. OWL is the result of the Web Ontology Working Group (now closed) and descends from DAML+Oil, which is in turn an amalgamation of DAML and OIL.

6. Rule Languages

Abstract

Knowledge representation languages, such as RDF Schema and OWL, are designed to specify descriptions of application domains. They typically offer constructs to describe classes, properties, and relationships, as well as constructs to capture class and property restrictions and to define complex classes. By contrast, rule languages are designed to specify data transformation rules that define how to synthesize new facts from those stored in the knowledge base. In this chapter, we introduce four rule languages, Datalog, the Rule Markup Language (RuleML), the Semantic Web Rule Language (SWRL), and TRIPLE, the last three developed in the context of the Semantic Web.

7. Semantic Web Services

Abstract

In its early days, the role of the Internet was that of a data provider: weather, finances, education, institutional, government, in addition to text and images about a myriad of other subjects, could be found on Web pages. As it evolved, the Web is no longer just a data provider, but a provider of services as well: buying and selling, auctions, bank transactions, and travel arrangements are among the services that can be performed online today. During the last decade, industry, government, and education sectors have been trying to establish standards for developing and using Web resources through what came to be known as Web services. There is no single definition for the expression. We list below those we find more adequate.

Technologies

Frontmatter

8. Methods for Ontology Development

Abstract

Ontology, as defined by Gruber, is an “explicit specification of a conceptualization.” This definition is enough to convey an understanding of what ontologies are and for what they are. The main reasons to build an ontology are information sharing and the possibility of reusing knowledge about specific domains (Goméz-Peréz et al. 2004). Understanding what ontologies are for, however, does not provide much help in building them. According to Guarino and Welty (2002), “the ontology discipline is evolving into a discipline of its own and in this process the need for a methodology is clearly arising.”

9. Ontology Sources

Abstract

For many, the greatest benefit of the Web is to allow a growing number of services to be accessed from offices and homes. Today’s Web grants direct access to financial, travel, commercial, and trading information at the same time that it facilitates buying and selling books, electronic equipment, stocks, bonds, and airline tickets, just to mention a few among myriad other options. The possibilities seem infinite, but the underlying Web technology falls short at a crucial point: there is no information about information. Any ordinary search will typically bring, together with the desired results, a large amount of worthless information. This situation, known to database experts as the “low precision, high recall” scenario, is hindering the usability of search engines. To ameliorate this situation, it is paramount to properly index Web resources, in other words, to add annotation elements that explain what the Web resources are about.

10. Semantic Web Software Tools

Abstract

Today, there is a series of available tools that support the Semantic Web. Such tools can be classified into three major groups: ontology and metadata editors, plugins and APIs, and inference mechanisms. The last group contains software tools with the ability to derive new facts or associations from other facts. There is a myth that such tools are capable of emulating how humans think and derive conclusions on their own. In fact, they do not implement magical artificial intelligence capabilities, but rather straightforward data-processing strategies. Inferences are as good as the quality of the data available for processing. In the case of the Semantic Web, inference mechanisms have a very restricted set of primitives, that is, classes and properties, which are essentially what can be described using OWL or RDF. Examples of inference mechanisms for the Semantic Web are JESS, FaCT, Pellet, and RACER.

Applications

Frontmatter

11. Software Agents

Abstract

Technological advance, allied with a consistent reduction in hardware costs, is accelerating the automatization of society as a whole. Hand in hand with this process, we are faced with the problem of coping with the rapidly growing complexity of the processes of integrating, managing, and operating computer-based systems (Sterritt and Hinchey 2005). Today’s distributed systems demand software applications that do more than simply coping with service demands; they must also be able to anticipate, predict, and adapt themselves to respond to user needs. Indeed, researchers from both industry and academia are investigating the development of autonomous software agents that may take over tasks once conducted by humans. The development of autonomic (i.e., self-managing) systems is only part of this effort (Rouff 2006). According to the Software Agents Group of the MIT Media Laboratory, software agents are very different from conventional software because they are semi-autonomous, pro-active, adaptive, and long-lived.

12. Semantic Desktop

Abstract

In this chapter, we exemplify the use of Semantic Web technologies in personal computing applications called semantic desktops. These applications combine ontologies, taxonomies, and metadata in general to enhance personal information management, software application usage, and collaboration. According to the semantic desktop community (URL: semanticdesktop.org):

The use of ontologies, metadata annotations, and semantic Web protocols on desktop computers will allow the integration of desktop applications and the Web, enabling a much more focused and integrated personal information management as well as focused information distribution and collaboration on the Web beyond sending e-mails. The vision of the Semantic Desktop for personal information management and collaboration has been around for a long time: visionaries like Vannevar Bush and Doug Engelbart have formulated and partially realized these ideas.

13. Ontology Applications in Art

Abstract

Cataloguing our cultural heritage has naturally been a major activity of museums and other cultural institutions throughout the world. Today, almost all major museums make their collections available over the Web, often with remarkable quality, such as the Hermitage Museum Web site (Mintzer et. al. 2001)

14. Geospatial Semantic Web

Abstract

The large volume of geospatial data available on the Web opens up unprecedented opportunities for data access and data interchange, facilitating the design of new geospatial applications and claiming for the redesign of traditional ones. However, to interoperate, geospatial applications must be able to locate and access the data sources, and agree both on the syntax and on the semantics of the data that flow between them. Achieving interoperability is more difficult in this case simply because geospatial data are much more complex than conventional data, with respect to both syntax and semantics.

Backmatter

Titel: Semantic Web: Concepts, Technologies and Applications
verfasst von: Karin Koogan Breitman, MSc, DSc
Marco Antonio Casanova, PhD
Walter Truszkowski, MA, BA
Verlag: Springer London
Electronic ISBN: 978-1-84628-710-7
Print ISBN: 978-1-84628-581-3
DOI: https://doi.org/10.1007/978-1-84628-710-7