Opening up Magpie via semantic services

Magpie is a suite of tools supporting a ‘zero-cost’ approach to semantic web browsing: it avoids the need for manual annotation by automatically associating an ontology-based semantic layer to web resources. An important aspect of Magpie, which differentiates it from superficially similar hypermedia systems, is that the association between items on a web page and semantic concepts is not merely a mechanism for dynamic linking, but it is the enabling condition for locating services and making them available to a user. These services can be manually activated by a user (pull services), or opportunistically triggered when the appropriate web entities are encountered during a browsing session (push services). In this paper we analyze Magpie from the perspective of building semantic web applications and we note that earlier implementations did not fulfill the criterion of “open as to services”, which is a key aspect of the emerging semantic web. For this reason, in the past twelve months we have carried out a radical redesign of Magpie, resulting in a novel architecture, which is open both with respect to ontologies and semantic web services. This new architecture goes beyond the idea of merely providing support for semantic web browsing and can be seen as a software framework for designing and implementing semantic web applications

Ontology selection: ontology evaluation on the real Semantic Web

The increasing number of ontologies on the Web and the appearance of large scale ontology repositories has brought the topic of ontology selection in the focus of the semantic web research agenda. Our view is that ontology evaluation is core to ontology selection and that, because ontology selection is performed in an open Web environment, it brings new challenges to ontology evaluation. Unfortunately, current research regards ontology selection and evaluation as two separate topics. Our goal in this paper is to explore how these two tasks relate. In particular, we are interested to get a better understanding of the ontology selection task and filter out the challenges that it brings to ontology evaluation. We discuss requirements posed by the open Web environment on ontology selection, we overview existing work on selection and point out future directions. Our major conclusion is that, even if selection methods still need further development, they have already brought novel approaches to ontology evaluatio

Accessing Information Sources using Ontologies

In this paper, we present a system that helps users access various types of information sources using ontologies. An ontology consists of a set of concepts and their relationships in a domain of interests. The system analyzes an ontology provided by a user so that the user can search and browse Wikipedia [1], DBpedia [4], PubMed [5], and the Web by utilizing the information in the ontology. In particular, terms defined in the ontology are mapped to Wikipedia pages and the navigation history of a user is saved so that it can serve as a personalized ontology. In addition, users can create and edit ontologies using the proposed system. We show that the proposed system can be used in an educational environment

Linked Open Data: State-of-the-Art Mechanisms and Conceptual Framework

Today, one of the state-of-the-art technologies that have shown its importance towards data integration and analysis is the linked open data (LOD) systems or applications. LOD constitute of machine-readable resources or mechanisms that are useful in describing data properties. However, one of the issues with the existing systems or data models is the need for not just representing the derived information (data) in formats that can be easily understood by humans, but also creating systems that are able to process the information that they contain or support. Technically, the main mechanisms for developing the data or information processing systems are the aspects of aggregating or computing the metadata descriptions for the various process elements. This is due to the fact that there has been more than ever an increasing need for a more generalized and standard definition of data (or information) to create systems capable of providing understandable formats for the different data types and sources. To this effect, this chapter proposes a semantic-based linked open data framework (SBLODF) that integrates the different elements (entities) within information systems or models with semantics (metadata descriptions) to produce explicit and implicit information based on users’ search or queries. In essence, this work introduces a machine-readable and machine-understandable system that proves to be useful for encoding knowledge about different process domains, as well as provides the discovered information (knowledge) at a more conceptual level

ODESeW. Automatic Generation of Knowledge Portals for Intranets and Extranets

This paper presents ODESeW (Semantic Web Portal based on WebODE platform [1]) as an ontology-based application that automatically generates and manages a knowledge portal for Intranets and Extranets. ODESeW is designed on the top of WebODE ontology engineering platform. This paper shows the service architecture that allows configuring the visualization of ontology-based information for different kinds of users, establishing reading and updating access policies to its content, and performing consistency checking between the portal information and the ontologies underlying it