Ontological Foundations for Geographic Information Science

We propose as a UCGIS research priority the topic of “Ontological Foundations for Geographic Information.” Under this umbrella we unify several interrelated research subfields, each of which deals with different perspectives on geospatial ontologies and their roles in geographic information science. While each of these subfields could be addressed separately, we believe it is important to address ontological research in a unitary, systematic fashion, embracing conceptual issues concerning what would be required to establish an exhaustive ontology of the geospatial domain, issues relating to the choice of appropriate methods for formalizing ontologies, and considerations regarding the design of ontology-driven information systems. This integrated approach is necessary, because there is a strong dependency between the methods used to specify an ontology, and the conceptual richness, robustness and tractability of the ontology itself. Likewise, information system implementations are needed as testbeds of the usefulness of every aspect of an exhaustive ontology of the geospatial domain. None of the current UCGIS research priorities provides such an integrative perspective, and therefore the topic of “Ontological Foundations for Geographic Information Science” is unique

Global Landmarks in a Complex Indoor Environment

Wayfinding in complex indoor environments can be a difficult and disorienting activity. Many factors contribute to this difficulty, including the variable number of floors and half-floors paired with many different and often unpredictable ways to get from one floor to another. In order to explore how the spatial information of floor to floor transitions is represented cognitively, a user study was conducted at the Carnegie Museums of Art and Natural History that drew on experienced participants from the Visitor Services Department. The participants were asked to give wayfinding descriptions to and from several landmarks in the museums with the majority of the routes spanning multiple floors. It was found that floor to floor transition points were often represented as landmarks with notable locations in the Museums being represented with both functional and referential aspects. A functional aspect of a floor to floor transition points meant that its purpose in the wayfinding description was to provide a means to get from one floor to another. A referential quality meant that a floor to floor transition points was simply an indemnity and did not serve as a way to move vertically through the environment. This finding informs the discussion on global landmarks and their representation and salience in large complex indoor environments

Visualization Tools for Clustering, Trees and Ordered Trees

Consider the situation of a set of individuals visiting a library and browsing through a collection. Each individual will, of course, select different books to browse depending on the original purpose of the visit. In addition, the order in which they search through a catalog will differ. The order is often ignored, yet might provide interesting cues as to the importance of certain books or topics. This scenario is not limited to browsing physical collections, but rather is repeated almost continually in other information-seeking behaviors including searching of on-line databases and hypenext/hypermedia systems

Ordered Trees: A Structure for the Mental Representation of Information

This paper reviews the ordered tree technique for deriving a structured mental representation, and discusses potential applications for infonnation science. The ordered tree clustering algorithm that we are developing provides an alternative to more traditional approaches of clustering based on similarity matrices. This algorithm is based on patterns of linear strings, typically free-recall orders, but potentially any material organized in a linear fashion. The technique has proven useful in representing ordered classifications, such as an alphabet or a numerical sequence, imbedded within a hierarchical semantic structure. Furthermore, the ordered tree technique, its corresponding data structures, and the data analysis procedures, can be generalized to include cross-classification used by human subjects and the classification of subjects themselves into categories such as experts and novices. discusses potential applications for infonnation science. The ordered tree clustering algorithm that we are developing provides an alternative to more traditional approaches of clustering based on similarity matrices. This algorithm is based on patterns of linear strings, typically free-recall orders, but potentially any material organized in a linear fashion. The technique has proven useful in representing ordered classifications, such as an alphabet or a numerical sequence, imbedded within a hierarchical semantic structure. Furthermore, the ordered tree technique, its corresponding data structures, and the data analysis procedures, can be generalized to include cross-classification used by human subjects and the classification of subjects themselves into categories such as experts and novices

Lattice-based similarity measures between ordered trees

A clustering algorithm has recently been developed by Reitman and Rueter to express both the structure of chunking in multi-trial free recall and the order of chunk production. The resulting ordered trees differ from ordinary rooted trees in that the elements of a chunk, at any level, may be restricted to a specific ordering. In order to make comparisons of long-term memory structures between subjects, a measure of the similarity between trees is needed. Previously developed similarity measures are shown to be inadequate for ordered trees. Lattice theory is used to generate new similarity measures suited to these richer structures. First, ordered trees are shown to form a nonmodular, graded lattice. Then, moves through this lattice are defined and used to produce several distance measures. These new measures are compared both to each other, and to existing measures, by examining the properties of each measure, and through application to hypothetical trees. The lattice-based measures prove to be theoretically superior, but lack computational ease. The general problem of describing paths in a nonmodular lattice is discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/23962/1/0000211.pd

This is the tricky part: When directions become difficult

Automated route guidance systems, both web-based systems and en-route systems, have become commonplace in recent years. These systems often replace human-generated directions, which are often incomplete, vague, or in error. However, human-generated directions have the ability to differentiate between easy and complex steps through language in a way that is more difficult in automated systems. This article examines a set of human-generated verbal directions to better understand why some parts of directions are perceived as being more difficult than the remaining steps. Insights from this analysis will lead to recommendations to improve the next generation of automated route guidance systems

Knowledge organization and skill differences in computer programmers

Like experts in other fields, expert computer programmers can recall at a glance far more information relevant to their field than novices can. One explanation for this difference is that experts not only have more information, they have it better organized into meaningful chunks. In this paper, we infer the details of individual programmers' chunks of key programming concepts using the Reitman--Rueter (Cognitive Psychology, 1980, 12(4), 554-581.) technique for inferring tree structures from recall orders. Differences in organizations accompany skill-level differences. Beginner programmers' organizations show a rich variety of common-language associations to these programming concepts; Intermediate programmers show mixtures of programming and common-language associations; and Experts show remarkably similar, but not identical, organizations based clearly on programming knowledge.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24336/1/0000603.pd