Gahegan, Mark

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Abstract: In information systems that support knowledge-discovery applications such as scientific exploration, reliance on highly structured ontologies as data-organization aids can be limiting. With current computational aids to science work, the human knowledge that creates meaning out of analyses is often only recorded when work reaches publication-or worse, left unrecorded altogether-for lack of an ontological model for scientific concepts that can capture knowledge as it is created and used. We argue for an approach to representing scientific concepts that reflects (1) the situated processes of science work, (2) the social construction of knowledge, and (3) the emergence and evolution of understanding over time. In this model, knowledge is the result of collaboration, negotiation, and manipulation by teams of researchers. Capturing the situations in which knowledge is created and used helps these collaborators discover areas of agreement and discord, while allowing individual inquirers to maintain different perspectives on the same information. The capture of provenance information allows historical trails of reasoning to be reconstructed, allowing end users to evaluate the utility and trustworthiness of knowledge representations. We present a proof-of-concept system, called Codex, based on this situated knowledge model. Codex supports visualization of knowledge structures through concept mapping, and enables inference across those structures. The proof-of-concept is deployed in the domain of geoscience to support distributed teams of learners and researchers.

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Abstract: Abstract In this paper we present an approach to conceiving of, constructing and comparing the concepts developed and used by geographers, environmental scientists and other earth science researchers to help describe, analyze and ultimately understand their subject of study. Our approach is informed by the situations under which concepts are conceived and applied, captures details of their construction, use and evolution and supports their ultimate sharing along with the means for deep exploration of conceptual similarities and differences that may arise among a distributed network of researchers. The intent here is to support different perspectives onto GIS resources that researchers may legitimately take, and to capture and compute with aspects of epistemology, to complement the ontologies that are currently receiving much attention in the GIScience community.

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Abstract: This paper describes a programmatic framework for representing, manipulating and reasoning with geographic semantics. The framework enables automating tool selection for user defined geographic problem solving, and evaluating semantic change in knowledge discovery environments. Methods, data, and human experts (our resources) uses, inputs, outputs, and semantic changes are described using ontologies. These ontological descriptions are manipulated by an expert system to select resources to solve a user-defined problem. A semantic description of the problem is compared to the services that each entity can provide to construct a graph of potential solutions. An optimal (least cost) solution is extracted from these solutions, and displayed in real-time. The semantic change(s) resulting from the interaction of resources within the optimal solution are determined via expressions of transformation semantics represented within the Java Expert System Shell. This description represents the formation history of each new information product (e.g. a map or overlay) and can be stored, indexed and searched as required. Examples are presented to show (1) the construction and visualization of information products, (2) the reasoning capabilities of the system to find alternative ways to produce information products from a set of data methods and expertise, given certain constraints and (3) the representation of the ensuing semantic changes by which an information product is synthesized.

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Abstract: The research reported here contrasts the roles, methodologies and capabilities of statistical methods with those of inductive machine learning methods, as they are used inferentially in geographical analysis. To this end, various established problems with statistical inference applied in geographical settings are reviewed, based on Gould's (1970) critique. Possible solutions to the problems outlined by Gould are suggested via reviews of: ( i ) improved statistical methods, and ( ii ) recent inductive machine learning techniques. Following this, some newer problems with inference are described, emerging from the increased complexity of geographical datasets and from the analysis tasks to which we put them. Again, some solutions are suggested by pointing to newer methods. By way of results, questions are posed, and answered, relating to the changes brought about by adopting inductive machine learning methods for geographical analysis. Specifically, these questions relate to analysis capabilities, methodologies, the role of the geographer and consequences for teaching and learning. Conclusions argue that there is now a strong need, motivated from many perspectives, to give geographical data a stronger voice, thus favouring techniques that minimize the prior assumptions made of a dataset.

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Abstract: One barrier to the uptake of geocomputation is that, unlike GIS, it has no system or toolbox that provides easy access to useful functionality. This paper describes an experimental environment, GeoVISTA Studio, that attempts to address this shortcoming. Studio is a Java-based, visual programming environment that allows for the rapid development of complex data exploration and knowledge construction applications to support geographic analysis. It achieves this by leveraging advances in geocomputation, software engineering, visualization and machine learning. At the time of writing, Studio contains full 3D rendering capability and has the following functionality: interactive parallel coordinate plots, scatterplot, visual classifier, 2D map and image viewer, sophisticated colour selection (including Munsell colour-space), spreadsheet, statistics package, and supervised and unsupervised neural networks. Through examples of Studio at work, this paper demonstrates the roles that geocomputation and visualization can play throughout the scientific cycle of knowledge creation, emphasising their supportive and mutually beneficial relationship. A brief overview of different types of inference used in such knowledge creation activities is given, and related to the exploratory analysis tools described. By way of results, a detailed account of the use of these tools is presented, and various findings and insights generated are pointed out. The domain of application is the process of uncovering useful categories by which a taxonomy of landuse/landcover can be created. The proposed categories are then evaluated using a combination of neural and visual methods, to ensure their viability.

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Abstract: Although many different types of data mining tools have been developed for geographic analysis, the broader perspective of geographic knowledge discovery?the stages required and their computational support?have been largely overlooked. This paper describes the process of knowledge construction as a number of inter-related activities and the support of these activities in an integrated visual and computational environment, GeoVISTA Studio. Results are presented showing examples of each stage in the knowledge construction process and a summary of the inter-relationships between visualization, computation, representation and reasoning is provided.

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Abstract: This paper explores the development of categories shared in the field logging of a region by a team of geologists. Visualization, neural networks and spatial statistical tools are employed to gain insight into the complex space of attributes observed, and into the categories developed. Background material and a discussion of results examines the findings in the light of research into category development, and specifically how categories are thought to be formed and modified as part of the (geo)scientific process and the situations encountered. Results show that (1) category discrepancy exists between individuals; (2) category development or revision is evident among individuals; and (3) that some categories do not seem to be totally defined by observed data alone. The results imply that contextual factors should also be considered when adopting ontological approaches to information representation.

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