Abstract
Semantic Web is the name of the next generation World Wide Web, that has been recently proposed by Tim Berners-Lee and the World Wide Web Consortium (W3C)1. In this new Web architecture, information and Web services will be easily understandable and usable by both humans and computers. The objective is not to make computers understand the human language, but to define a universal model for the expression of the information and a set of inference rules that machines can easily use in order to process and relate the information as if they really understood it (Berners-Lee, 1998). Though, as the current Web provided sharing of documents among previously incompatible computers, the Semantic Web intends to go beyond, allowing stovepipe systems, hardwired computers, and other devices to share contents embedded in different documents. The most known architecture for Semantic Web is based on a stack of related technologies, each one being a whole research area by itself (Berners-Lee, Hendler, & Lassila. 2001; Pereira & Freire, 2005). Accomplishment of the Semantic Web is considered a great challenge, not only due to the complexity of implementation but also because of the vast applicability in several areas. In spite of this, Semantic Web is still one of the most promising research areas among those which aim to define a new architecture for the Web. Semantic Web goes far beyond previous information retrieval and knowledge representation projects, presenting a non-centralized way to represent and contextualize real-world concepts, unambiguously, for several areas of knowledge. Semantic Web-enabled machines will handle information at our communication level. It is clear that the ability to interpret reality is still very primitive, however, Semantic Web points a way towards machine interaction and learning (Pereira et al., 2005). Semantic Web will integrate, interact with, and bring benefits to most human activities. Its full potential will go beyond the Web to real-world machines, providing increased interaction between machines and with humans—smarter phones, radios, and other electronic devices. Semantic Web will bring a different kind of approach in the understanding of reality by the machines and will constitute a mark in the evolution of human knowledge (Pereira et al., 2005).
TopSemantic Web Technologies By Category
In this section we present eleven tables with technologies grouped by each category and up to ten of their main characteristics:
Key Terms in this Chapter
Ontology: The word ontology comes from the Greek ontos (being) and logia (written or spoken discourse). It is in use since Empedocles described the four elements – air, earth, fire, and water. In artificial intelligence, ontology is defined as a working model of concepts and interactions from a particular domain of knowledge, like medicine, mathematics, automobile repair, and so forth, which is used to easily describe the meaning of different contents that can be exchanged in information systems. Any ontology can be easily extended, refined, and reused by other ontologies, providing expressive representation for a wide diversity of real-world concepts.
Ontology-Centric API: Adding direct support for the kinds of objects expected to be in an ontology: classes (in a class hierarchy), properties (in a property hierarchy), and individuals.
Resource-centric API: Presents an RDF model as resources having properties. •
OWL: The concept of Web Ontology Language, the most expressive of ontology languages currently defined for the Semantic Web. It has been developed by the W3C’s Web Ontology Working Group and intended to be the successor of the DAML+May 5, 2005 OIL language. It is an extension of RDF Schema and a W3C Recommendation since 2004.
RDF API-Paradigm: There are four types of API-Paradigm for RDF: •
RDF Schema: Expresses a hierarchy class data model used for the classification and description of standard RDF resources. The role of RDF Schema is to facilitate the definition of metadata by providing a data model, much like many object-oriented programming languages, to allow the creation of data classes. It is a simple language that enables people to create their own RDF vocabularies in RDF/XML syntax.
Statement-Centric API: RDF data is manipulated as a set of RDF triples each consisting of a subject, predicate, and object. •
Stovepipe Systems: A system where all the components are hardwired to only work with each other.
W3C Recommendation: A Recommendation W3C is interpreted by the industry and by the Web community, as being one synonym of normalization for the Web. Each Recommendation W3C is not more of the one than a steady specification developed by a Work group W3C (W3C Working Group) and reviewed by the members of the W3C. This type of recommendation promotes the interoperability of Web technologies from consensus gotten between the industry and the academy.
RDF: It is the concept of Resource Description Framework, a W3C Recommendation since 1999. It is a XML-based language that uses a triple-based assertion model and syntax to describe resources. RDF model is called “triple” because it can be described in terms of subject, predicate, and object, like grammatical parts of a sentence.
XML: The concept of Extensible Markup Language, a small set of rules in human-readable plaintext used to describe and share common structured platform-independent information. Its structure main components are elements and attributes of elements that are nested to create a hierarchical tree that can be easily validated. XML is extensible because, unlike HTML, anyone can define new tags and attribute names to parameterize or semantically qualify contents. It is a formal recommendation from W3C since 1998 playing an increasingly important role in the exchange of a wide variety of data on the Web.
Model-centric API: Only allows loading, saving, and deleting whole RDF models. •