Managing contextual information in semantically-driven temporal information systems

Context-aware (CA) systems have demonstrated the provision of a robust solution for personalized information delivery in the current content-rich and dynamic information age we live in. They allow software agents to autonomously interact with users by modeling the user’s environment (e.g. profile, location, relevant public information etc.) as dynamically-evolving and interoperable contexts. There is a flurry of research activities in a wide spectrum at context-aware research areas such as managing the user’s profile, context acquisition from external environments, context storage, context representation and interpretation, context service delivery and matching of context attributes to users‘ queries etc. We propose SDCAS, a Semantic-Driven Context Aware System that facilitates public services recommendation to users at temporal location. This paper focuses on information management and service recommendation using semantic technologies, taking into account the challenges of relationship complexity in temporal and contextual information

Alignment Cubes: Towards Interactive Visual Exploration and Evaluation of Multiple Ontology Alignments

Ontology alignment is an area of active research where many algorithms and approaches are being developed. Their performance is usually evaluated by comparing the produced alignments to a reference alignment in terms of precision, recall and F-measure. These measures, however, only provide an overall assessment of the quality of the alignments, but do not reveal differences and commonalities between alignments at a finer-grained level such as, e.g., regions or individual mappings. Furthermore, reference alignments are often unavailable, which makes the comparative exploration of alignments at different levels of granularity even more important. Making such comparisons efficient calls for a “human-in-the-loop” approach, best supported through interactive visual representations of alignments. Our approach extends a recent tool, Matrix Cubes, used for visualizing dense dynamic networks. We first identify use cases for ontology alignment evaluation that can benefit from interactive visualization, and then detail how our Alignment Cubes support interactive exploration of multiple ontology alignments. We demonstrate the usefulness of Alignment Cubes by describing visual exploration scenarios, showing how Alignment Cubes support common tasks identified in the use cases

Actas del Taller de Trabajo Zoco’08 / JISBD Integración de Aplicaciones Web : XIII Jornadas de Ingeniería del Software y Bases de Datos Gijón, 7 al 10 de Octubre de 2008

Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-0260

Ontology alignment mechanisms for improving web-based searching

Ontology has been developed to offer a commonly agreed understanding of a domain that is required for knowledge representation, knowledge exchange and reuse across domains. Therefore, ontology organizes information into taxonomies of terms (i.e., concepts, attributes) and shows the relationships between them. In fact, it is considered to be helpful in reducing conceptual confusion for users who need to share applications of different kinds, so it is widely used to capture and organize knowledge in a given domain. Although ontologies are considered to provide a solution to data heterogeneity, from another point of view, the available ontologies could themselves introduce heterogeneity problems. In order to deal with these problems, ontologies must be available for sharing or reusing; therefore, semantic heterogeneity and structural differences need to be resolved among ontologies. This can be done, in some cases, by aligning or matching heterogeneous ontologies. Thus, establishing the relationships between terms in the different ontologies is needed throughout ontology alignment. Semantic interoperability can be established in ontology reconciliation. The original problem is called the ―ontology alignment‖. The alignment of ontologies is concerned with the identification of the semantic relationships (subsumption, equivalence, etc.) that hold between the constituent entities (which can be classes, properties, etc.) of two ontologies. In this thesis, an ontology alignment technique has been developed in order to facilitate communication and build a bridge between ontologies. An efficient mechanism has been developed in order to align entities from ontologies in different description languages (e.g. OWL, RDF) or in the same language. This approach tries to use all the features of ontologies (concept, attributes, relations, structure, etc.) in order to obtain efficiency and high quality results. For this purpose, several matching techniques have been used such as string, structure, heuristic and linguistic matchingtechniques with thesaurus support, as well as human intervention in certain cases, to obtain high quality results. The main aim of the work is to introduce a method for finding semantic correspondences among heterogeneous ontologies, with the intention of supporting interoperability over given domains. The approach brings together techniques in modelling, string matching, computation linguistics, structure matching and heuristic matching, in order to provide a semi-automatic alignment framework and prototype alignment system to support the procedure of ontology alignment in order to improve semantic interoperability in heterogeneous systems. This technique integrates some important features in matching in order to achieve high quality results, which will help when searching and exchanging information between ontologies. Moreover, an ontology alignment system illustrates the solving of the key issues related to heterogeneous ontologies, which uses combination-matching strategies to execute the ontology-matching task. Therefore, it can be used to discover the matching between ontologies. This thesis also describes a prototype implementation of this approach in many real-world case studies extracted from various Web resources. Evaluating our system is done throughout the experiments provided by the Ontology Alignment Evaluation Initiative. The system successfully achieved 93% accuracy for ontology matching. Finally, a comparison between our system and well-known tools is achieved so that our system can be evaluated

Онтологічний аналіз у Web

Монографію присвячено проблематиці розробки, дослідження та використання онтологій в розподілених застосуваннях. Проаналізовано моделі та методи подання онтологій, їх зв’язок з технологіями Semantic Web. В роботі аналізуються питання, що стосуються здобуттям онтологічних знань з відкритих джерел Web, Wiki-ресурсів та природномовних документів. Розглядається роль онтологічного аналізу в інтелектуалізації пошукових систем, Web–сервісів та програмних агентів. Наводяться приклади застосування онтологій в освіті та науці. Робота орієнтована на дослідників та науковців, які займаються розробками в галузі розподілених інтелектуальних систем