Wireless sensor networks (WSN) generate large volumes of raw data which possess natural heterogeneity. WSNs are normally application specific with no sharing or reusability of sensor data among applications. In order for applications and services to be developed independently of particular WSNs, sensor data need to be enriched with semantic information. In this paper, we propose a Semantic Web Architecture for Sensor Networks (SWASN). This information oriented architecture allows the sensor data to be understood and processed in a meaningful way by a variety of applications with different purposes. We develop ontologies for sensor data and use the Jena API for processing which includes querying and inference over sensor data. By studying a building fire emergency scenario, we show that semantic web technologies can provide high level information extraction and inference of sensor data.

Abstract. Change operators are the building blocks of ontology evolution. Different layers of change operators have been suggested. In this paper, we present a novel approach to deal with ontology evolution, in particular, change representation as a pattern-based layered operator framework. As a result of an empirical study, we identify four different levels of change operators based on the granularity, domain-specificity and abstraction of changes. The first two layers are based on generic structural change operators, whereas the next two layers are domainspecific change patterns. These layers of change patterns capture the real changes in the selected domains. We discuss identification and integration of the different layers with correctness and consistency constraint.

Ontologies can support a variety of purposes, ranging from capturing the conceptual knowledge to the organisation of digital content and information. However, information systems are always subject to change and ontology change management can pose challenges. In this sense, the application and representation of ontology changes in terms of higher-level change operations can describe more meaningful semantics behind the applied change. In this paper, we propose a four-phase process that covers the operationalization, representation and detection of higher-level changes in ontology evolution life cycle. We present different levels of change operators based on the granularity and domain-specificity of changes. The first layer is based on generic atomic level change operators, whereas the next two layers are user-defined (generic/domain-specific) change patterns. We introduce layered change logs for the explicit operational representation of ontology changes. We formalised the change log using a graph-based approach. We introduce a technique to identify composite changes that not only assists in formulating ontology change log data in a more concise manner, but also helps in realizing the semantics and intent behind any applied change. Furthermore, we identify frequent change sequences that are applied as a reference to discover reusable, often domain-specific and usage-driven change patterns. We describe the pattern identification algorithms and evaluate their performance.

Changes in the characterization of instances in digital contents are one of the rationales to change or evolve ontologies which support the domain. These changes can impacts on one or more of interrelated ontologies. Before implementing changes, their impact on the target ontology, other dependent ontologies or dependent systems should be analysed. We investigate three concerns for the determination of impacts of changes in ontologies: representation of changes to ensure minimum impact, impact determination and integrity determination. Key elements of our solution are the operationalization of change operations to minimize impacts, a parameterization approach for the determination of impacts, a categorization scheme for identified impacts, and prioritization technique for change operations based on the severity of impacts.

Ontology change log data is a valuable source of information which reflects the changes in the domain, the user requirements, flaws in the initial design or the need to incorporate additional information. Ontology change logs can provide operational as well as analytical support in the ontology evolution process. In this paper, we present a novel approach to deal with change representation and knowledge discovery from ontology change logs. We look into different knowledge gathering aspects to capture every single facet of ontology change. The ontology changes are formalised using a graph-based approach. The knowledgebased change log facilitates detection of similarities within different time series, discovering implicit dependencies between ontological entities and reuse of knowledge. We analyse an ontology change log graph in order to identify frequent changes that occur in ontologies over time. We identify different types of change sequences based on their order and completeness. Analysis of change logs also assists in extracting new change patterns and rules which cannot be found by simply querying or processing ontology change logs.