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.

Semantic Web and education

Abstract: to Web-Based Education.- to the Semantic Web.- The Setting for Semantic Web-Based Education.- Architectural Issues.- Learning Technology Standardization Efforts.- Personalization Issues.- Ontological Engineering for Semantic Web-Based Education.- Applications and Research.

Cohesion Metrics for Ontology Design and Application

Abstract: Recently, domain specific ontology development has been driven by research on the Semantic Web. Ontologies have been suggested for use in many application areas targeted by the Semantic Web, such as dynamic web service composition and general web service matching. Fundamental characteristics of these ontologies must be determined in order to effectively make use of them: for example, Sirin, Hendler and Parsia have suggested that determining fundamental characteristics of ontologies is important for dynamic web service composition. Our research examines cohesion metrics for ontologies. The cohesion metrics examine the fundamental quality of cohesion as it relates to ontologies.

QUONTO: querying ontologies

Abstract: One of the most important lines of research in Description Logics (DLs) is concerned with the trade-off between expressive power and computational complexity of sound and complete reasoning. Research carried out in the past on this topic has shown that many DLs with efficient, i.e., worstcase polynomial time, reasoning algorithms lack the modeling power required for capturing conceptual models and basic ontology languages, while most DLs with sufficient modeling power suffer from inherently worst-case exponential time behavior of reasoning [1, 2]. Although the requirement of polynomially tractable reasoning might be less stringent when dealing with relatively small ontologies, we believe that the need of efficient reasoning algorithms is of paramount importance when the ontology system is to manage large amount of objects (e.g., from thousands to millions of instances). This is the case of several important applications where the use of ontologies is advocated nowadays. For example, in the Semantic Web, ontologies are often used to describe the relevant concepts of Web repositories, and such repositories may incorporate very large data sets, which constitute the instances of the concepts in the ontology. In such cases, two requirements emerge that are typically overlooked in DLs. First, the number of objects in the knowledge bases requires managing instances of concepts (i.e., ABoxes) in secondary storage. Second, significant queries to be posed to the knowledge base are more complex than the simple queries (i.e., concepts and roles) usually considered in DL research. Unfortunately, in these contexts, whenever the complexity of reasoning is exponential in the size of the instances (as for example in Fact1, Racer2 and in [3]), there is little hope for effective instance management and query answering algorithms. In [4] a new DL, called DL-Lite, was proposed specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. A DL-Lite knowledge base (KB) is constituted by two components: an intensional level (called TBox in DL jargon), used to model the concepts and the relations (roles) of the ontologies, and an exten-

Semantic analytics on social networks: experiences in addressing the problem of conflict of interest detection

Abstract: In this paper, we describe a Semantic Web application that detects Conflict of Interest (COI) relationships among potential reviewers and authors of scientific papers. This application discovers various 'semantic associations' between the reviewers and authors in a populated ontology to determine a degree of Conflict of Interest. This ontology was created by integrating entities and relationships from two social networks, namely "knows," from a FOAF (Friend-of-a-Friend) social network and "co-author," from the underlying co-authorship network of the DBLP bibliography. We describe our experiences developing this application in the context of a class of Semantic Web applications, which have important research and engineering challenges in common. In addition, we present an evaluation of our approach for real-life COI detection.

Ranking complex relationships on the semantic Web

Abstract: Industry and academia are both focusing their attention on information retrieval over semantic metadata extracted from the Web, and it is increasingly possible to analyze such metadata to discover interesting relationships. However, just as document ranking is a critical component in today's search engines, the ranking of complex relationships would be an important component in tomorrow's semantic Web engines. This article presents a flexible ranking approach to identify interesting and relevant relationships in the semantic Web. The authors demonstrate the scheme's effectiveness through an empirical evaluation over a real-world data set.