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Lightweight ontology-based tools for managing observational data
Abstract
We describe recent ontology and annotation editing capabilities to a specialized data management system for observational data. The system supports observations and measurements explicitly, allowing users to upload observational data sets as well as semantically describe and query data sets using formal OWL-DL ontologies. Recent extensions allow users to extend observational ontologies with domain-specific terms as well as provide detailed semantic annotations using a "markdown"-based approach. In addition, we describe a new implementation of the system using standard semantic web technologies for managing OWL-DL ontologies and RDF triples. Our approach supports a wide variety of observational data, and is especially targeted at helping scientists manage heterogeneous biodiversity and ecological data by allowing access to data through a common and generic observations and measurements data model.
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Observational data (i.e., data that records observations and measurements) plays a key role in many scientific disciplines.
Observational data, however, are typically structured and described in ad hoc ways, making its discovery and integration difficult. The wide range of data collected, the variety of ways the data are
used, and the needs of existing analysis applications make it impractical to define “one-size-fits-all” schemas for most observational
data sets. Instead, new approaches are needed to flexibly describe observational data for effective discovery and integration.
In this paper, we present a generic conceptual-modeling framework for capturing the semantics of observational data. The framework
extends standard conceptual modeling approaches with new constructs for describing observations and measurements. Key to the
framework is the ability to describe observation context, including complex, nested context relationships. We describe our
proposed modeling framework, focusing on context and its use in expressing observational data semantics.
Earth and environmental scientists collect and use a wide range of observational data. This data often exhibits high structural and semantic heterogeneity due to the variety of data collected and the ways in which observational datasets are structured in practice. However, to address questions at broad temporal, geographic, and biological scales, researchers often need to access and combine data from many observational datasets. This paper presents a system called ObsDB that helps to address these challenges by providing an integrated environment for storing, querying, and analyzing heterogeneous data based on a semantic observational model. The model allows for ontology-based descriptions of observational datasets and provides a common representation for storing observational data. The obsdb system is built on top of standard relational database technology and provides a declarative query language for accessing observations. Integrated support is also provided for exploratory data analysis, allowing users to call analytical scripts created using the R system over stored observational data.
Annotations play a central role in the curation of scientific databases. Despite their importance, data formats and schemas are not designed to manage the increasing variety of annotations. Moreover, DBMSs often lack support for storing and querying annotations. Furthermore, annotations and data are only loosely coupled. This paper introduces an annotation-oriented data model for the manipulation and querying of both data and annotations. In particular, the model allows for the specification of annotations on sets of values and for effectively querying the information on their association. We use the concept of block to represent an annotated set of values. Different colors applied to the blocks represent different annotations. We introduce a color query language for our model and prove it to be both complete (it can express all possible queries over the class of annotated databases), and minimal (all the algebra operators are primitive). We present MONDRIAN, a prototype implementation of our annotation mechanism, and we conduct experiments that investigate the set of parameters which influence the evaluation cost for color queries.
Recently, an initiative within the hydrologic science and environmental engineering communities has emerged for the establishment of cooperative, large-scale environmental observatories. Scientists’ ability to access and use data collected within observatories to address broad research questions depends on the successful implementation of cyberinfrastructure. In this paper, we describe the architecture and functional requirements for an environmental observatory information system that supports collection, organization, storage, analysis, and publication of hydrologic observations. We then describe a unique system that has been developed to meet these requirements and that has been implemented within the Little Bear River, Utah environmental observatory test bed, as well as across a nation-wide network of 11 similar observatory test bed sites. The components demonstrated comprise an observatory information system that enables not only the management, analysis, and synthesis of environmental observations data for a single observatory, but also publication of the data on the Internet in simple to use formats that are easily accessible, discoverable by others, and interoperable with data from other observatories.
We have developed a semantic data framework that supports interdisciplinary virtual observatory projects across the fields of solar physics, space physics and solar-terrestrial physics. This work required a formal, machine understandable representation for concepts, relations and attributes of physical quantities in the domains of interest as well as their underlying data representations. To fulfill this need, we developed a set of solar-terrestrial ontologies as formal encodings of the knowledge in the Ontology Web Language–Description Logic (OWL–DL) format.We present our knowledge representation and reasoning needs motivated by the context of Virtual Observatories, from fields spanning upper atmospheric terrestrial physics to solar physics, whose intent is to provide access to observational datasets. The resulting data framework is built upon semantic web methodologies and technologies and provides virtual access to distributed and heterogeneous sets of data as if all resources appear to be organized, stored and retrieved from a local environment. Our conclusion is that the combination of use case-driven, small and modular ontology development, coupled with free and open-source software tools and languages provides sufficient expressiveness and capabilities for an initial production implementation and sets the stage for a more complete semantic-enablement of future frameworks.