Generating Data Wrapping Ontologies from Sensor Networks: a case study

Information coming from sensor networks is being increasingly used in a variety of systems (decision support systems, information portals, etc), normally combined with information coming from more traditional sources (e.g., relational databases, web documents, etc). However, existing ontology based information integration approaches cannot be easily used for this combination task since they are mainly focused on the integration of information coming from these traditional sources, and do not support sensor network data. In this paper we make a first step towards enabling the inclusion of sensor network data into these integration approaches, with the automatic generation of data wrapping ontologies for sensor networks. Our approach extends existing ones used for extracting data wrapping ontologies from relational databases, using the schema of sensor network queries and external ontology search and relation discovery services

Semantic Web Techniques to Support Interoperability in Distributed Networked Environments

We explore two Semantic Web techniques arising from ITA research into semantic alignment and interoperability in distributed networks. The first is POAF (Portable Ontology Aligned Fragments) which addresses issues relating to the portability and usage of ontology alignments. POAF uses an ontology fragmentation strategy to achieve portability, and enables subsequent usage through a form of automated ontology modularization. The second technique, SWEDER (Semantic Wrapping of Existing Data sources with Embedded Rules), is grounded in the creation of lightweight ontologies to semantically wrap existing data sources, to facilitate rapid semantic integration through representational homogeneity. The semantic integration is achieved through the creation of context ontologies which define the integrations and provide a portable definition of the integration rules in the form of embedded SPARQL construct clauses. These two Semantic Web techniques address important practical issues relevant to the potential future adoption of ontologies in distributed network environments

an approach for semantic integration of heterogeneous data sources

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view