Semantic Web

About: Semantic Web is a research topic. Over the lifetime, 26987 publications have been published within this topic receiving 534275 citations. The topic is also known as: Sem Web & SemWeb.

Interoperability in Personalized Adaptive Learning

Abstract: Personalized adaptive learning requires semantic-based and context-aware systems to manage the Web knowledge efficiently as well as to achieve semantic interoperability between heterogeneous information resources and services. The technological and conceptual differences can be bridged either by means of standards or via approaches based on the Semantic Web. This article deals with the issue of semantic interoperability of educational contents on the Web by considering the integration of learning standards, Semantic Web, and adaptive technologies to meet the requirements of learners. Discussion is m ade on the state of the art and the main challenges in this field, including metadata access and design issues relating to adaptive learning. Additionally, a way how to integrate several original approaches is proposed.

Data mining for Web intelligence

Abstract: Searching, comprehending, and using the semistructured HTML, XML, and database-service-engine information stored on the Web poses a significant challenge. This data is more sophisticated and dynamic than the information commercial database systems store. To supplement keyword-based indexing, researchers have applied data mining to Web-page ranking. In this context, data mining helps Web search engines find high-quality Web pages and enhances Web click stream analysis. For the Web to reach its full potential, however, we must improve its services, make it more comprehensible, and increase its usability. As researchers continue to develop data mining techniques, the authors believe this technology will play an increasingly important role in meeting the challenges of developing the intelligent Web. Ultimately, data mining for Web intelligence will make the Web a richer, friendlier, and more intelligent resource that we can all share and explore. The paper considers how data mining holds the key to uncovering and cataloging the authoritative links, traversal patterns, and semantic structures that will bring intelligence and direction to our Web interactions.

Law and the Semantic Web: legal Ontologies, Methodologies, Legal Information Retrieval, and Applications

Abstract: Context of the Book.- Law and the Semantic Web, an Introduction.- Introduction: Legal Informatics and the Conceptions of the Law.- Statistical Study of Judicial Practices.- Theoretical Papers: Legal Ontologies and Methodologies.- Use and Reuse of Legal Ontologies in Knowledge Engineering and Information Management.- Types and Roles of Legal Ontologies.- CAUSATI O NT : Modeling Causation in AI&Law.- A Constructive Framework for Legal Ontologies.- On the Ontological Status of Norms.- Building Legal Ontologies with METHONTOLOGY and WebODE.- Institutional Pragmatics and Legal Ontology Limits of the Descriptive Approach of Texts.- Practice Papers: Information Retrieval and Applications.- Using NLP Techniques to Identify Legal Ontology Components: Concepts and Relations.- A Methodology to Create Legal Ontologies in a Logic Programming Information Retrieval System.- Iuriservice: An Intelligent Frequently Asked Questions System to Assist Newly Appointed Judges.- NetCase: An Intelligent System to Assist Legal Services Providers in Transnational Legal Networks.- No Model Behaviour: Ontologies for Fraud Detection.

Bayesian semantics for the semantic web

Abstract: Uncertainty is ubiquitous. Any representation scheme intended to model real-world actions and processes must be able to cope with the effects of uncertain phenomena. A major shortcoming of existing Semantic Web technologies is their inability to represent and reason about uncertainty in a sound and principled manner. This not only hinders the realization of the original vision for the Semantic Web (Berners-Lee & Fischetti, 2000), but also raises an unnecessary barrier to the development of new, powerful features for general knowledge applications. The overall goal of our research is to establish a Bayesian framework for probabilistic ontologies, providing a basis for plausible reasoning services in the Semantic Web. As an initial effort towards this broad objective, this dissertation introduces a probabilistic extension to the Web ontology language OWL, thereby creating a crucial enabling technology for the development of probabilistic ontologies. The extended language, PR-OWL (pronounced as “prowl”), adds new definitions to current OWL while retaining backward compatibility with its base language. Thus, OWL-built legacy ontologies will be able to interoperate with newly developed probabilistic ontologies. PR-OWL moves beyond deterministic classical logic (Frege, 1879; Peirce, 1885), having its formal semantics based on MEBN probabilistic logic (Laskey, 2005). By providing a means of modeling uncertainty in ontologies, PR-OWL will serve as a supporting tool for many applications that can benefit from probabilistic inference within an ontology language, thus representing an important step toward the World Wide Web Consortium's (W3C) vision for the Semantic Web. In addition, PR-OWL will be suitable for a broad range of applications, which includes improvements to current ontology solutions (i.e. by providing proper support for modeling uncertain phenomena) and much-improved versions of probabilistic expert systems currently in use in a variety of domains (e.g. medical, intelligence, military, etc.).

Semantic web architecture: stack or two towers?

Abstract: We discuss language architecture for the Semantic Web, and in particular different proposals for extending this architecture with a rules component. We argue that an architecture that maximises compatibility with existing languages, in particular RDF and OWL, will benefit the development of the Semantic Web, and still allow for forms of closed world assumption and negation as failure.