Significant efforts have been made to understand and document knowledge related to scientific measurements. Many of those efforts resulted in one or more high-quality ontologies that describe some aspects of scientific measurements, but not in a comprehensive and coherently integrated manner. For instance, we note that many of these high-quality ontologies are not properly aligned, and more challenging, that they have different and often conflicting concepts and approaches for encoding knowledge about empirical measurements. As a result of this lack of an integrated view, it is often challenging for scientists to determine whether any two scientific measurements were taken in semantically compatible manners, thus making it difficult to decide whether measurements should be analyzed in combination or not. In this paper, we present the Human-Aware Sensor Network Ontology that is a comprehensive alignment and integration of a sensing infrastructure ontology and a provenance ontology. HASNetO has been under development for more than one year, and has been reviewed, shared and used by multiple scientific communities. The ontology has been in use to support the data management of a number of large-scale ecological monitoring activities (observations) and empirical experiments.
Geographic information is based on observations or measurements. The Open Geospatial Consortium (OGC) has developed an implementation specification for observations and measurements (O&M). It specifies precisely how to encode information. Yet, the O&M conceptual model does not specify precisely which real-world entities are denoted by the specified information objects. We provide formal semantics for the central O&M terms by aligning them to the foundational ontology DOLCE. The alignment to a foundational ontology restricts the possible interpretations of the central elements in the O&M model and establishes explicit relations between categories of real world entities and classes of information objects. These relations are essential for assessing semantic interoperability between geospatial information sources. The understanding of complex environmental phenomena, such as deforestation and epidemics, requires observations at multiple scales. This scale dependency is not handled well by today's rather technical sensor definitions. Geosensor networks are normally defined as distributed ad-hoc wireless networks of computing platforms serving to monitor phenomena in geographic space. Such definitions also do not admit animals as sensors. Consequently, they exclude human sensors, which are the key to volunteered geographic information, and they fail to support connections between phenomena observed at multiple scales. We propose definitions of sensors as information sources at multiple aggregation levels, relating physical stimuli to observations. An algebraic formalization shows their behavior as well as their aggregations and generalizations. It is intended as a basis for defining consistent application programming interfaces to sense the environment at multiple scales of observations and with different types of sensors. Days of Yore Naturalism Reification Checkpoints and Empirical Content Logic and Mathematics Denotation and Truth Semantic Agreement Things of the Mind Appendix: Predicate Functors References Index Being a part of the Information Age, users are challenged with a tremendously growing amount of Web data which generates a need for more sophisticated information retrieval systems. The Semantic Web provides necessary procedures to augment the highly unstructured Web with suitable metadata in order to leverage search quality and user experience. In this article, we will outline an approach for creating a web-scale, precise and efficient information system capable of understanding keyword, entity and natural language queries. By using Semantic Web methods and Linked Data the doctoral work will present how the underlying knowledge is created and elaborated searches can be performed on top.