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

An Overview of the BFO - Basic Formal Ontology - and Its Applicability for Satellite Systems

This work aims to present an overview of the top-level ontology BFO - Basic Formal Ontology - and its applicability for Satellite Systems. As an upper level ontology, the BFO was designed to be extended, providing the basis for the specification of detailed representational artifacts about scientific information domains. These aspects and the challenges of satellite systems complexity and large size compose a suitable scenario for the creation of a specialized dialect to improve efficiency and accuracy when modeling such systems. By analyzing BFO based ontologies in other disciplines and existing satellite models it is possible to describe an application for satellite systems, which can provide a foundation for the creation of a concrete ontology to be applied on satellite modeling

A Geographic Ontology and GIS Model for Carolina Bays

Carolina bays are a unique geomorphologic entity located along the Atlantic coastal plain. Even without the benefit of an overhead view, they have been noted as a distinct feature of the coastal plain as first described by the South Carolina Geological Survey of South Carolina in 1848. The first aerial photographs in the 1930 coastal South Carolina region revealed that the unique depression wetlands were more than just a strange local phenomenon. Aerial photos enabled observers to see qualities in addition to their relative distribution that make them unique: their oval shape, northwest to southeast orientation and the presence of raised sand rims along their eastern and southeastern edges in many instances. Being such a distinctive surface feature and recognized for their ecological value, it would seem that Carolina bays would have been defined within their own map coverage across the Atlantic Coastal plain. However, just two statewide inventories have been completed for South Carolina and Georgia, and one for North Carolina has never been conducted. While previous inventories have employed onscreen digitization with Geographic Information Systems (GIS) in order to inventory bays, researchers have raised concerns over how individuals define Carolina bay as a geographic entity. The differences in human perception make the classification of geographic entities that exist on a continuum such as Carolina bays a challenge and may have contributed to widely varying estimates of their numbers. In order to explore the classification issues related to Carolina bays, and the usefulness of geographic ontology and cartographic modeling for inventory, a cartographic model was constructed for use within the Ocean Bay quad in Francis Marion National Forest in Berkeley and Charleston Counties, South Carolina. To test the model’s selective ability, a comparison was made between Carolina bay features that a researcher selected and bays identified by a cartographic model. The model positively identified 76 percent of Carolina bays that a researcher identified in an image within a single quadrangle. The approach used in this model showed that the initial identification rule of any pixel within a bay’s border counted as a positive identification was inadequate. Other aspects not accounted for, including false positive identification, neither researcher nor model being able to identify a bay, or bays that the model was able to select that the researcher was not were added into a subsequent model. Results from the amended model show fewer researcher identified instances of Carolina bays, but a slightly higher rate of mutual identification by the model and the researcher. With these complications in mind, a similar approach was taken with Bladen County North Carolina, but with significant revisions. A cartographic model was created for Bladen County North Carolina in which bay characteristics were selected from the North Carolina Gap Analysis Program (GAP) land use/landcover dataset, the Soil Survey Geographic Database (SSURGO) and National Wetlands Inventory (NWI). The predictive ability of the model was assessed by manually selecting Carolina bays from a high resolution image and comparing the manually selected bays with the model identifications. In order to remedy the issue of forcing all instances of bays into one of two categories (either an object is a bay or it is not), a ranking system was developed that was based upon a core/radial cognitive model, and the approach taken with the Savannah River Ecology Lab (SREL) inventory. The rule for positive identification was changed from a single pixel to a visual estimation of 50 percent coverage of a Carolina bay. As a whole, the predictive model identified 57 percent of the features also identified manually by the researcher, but the bay ranking system gives a different breakdown of how well the model worked within each category: exemplar (86 percent), less distinct (79 percent), bay-like (53 percent), and destroyed (19 percent) show significant differences. In addition to the ranking system, other attributes were assessed, such as the presence or absence of a sand rim, water visibility, overlap, diverging, long axis length and orientation. The analysis shows that the model has the potential to identify well defined bays with at least 50 percent areal coverage, and as such offers the first iteration of a computational ontology for the Carolina bays of Bladen County, North Carolina. Results from this research may provide a basis for modeling the entire range of Carolina bays, defining one of the most curious features of the Atlantic Coastal Plain and uniting differing definitions under one digital concept

Semantic categories underlying the meaning of ‘place’

This paper analyses the semantics of natural language expressions that are associated with the intuitive notion of ‘place’. We note that the nature of such terms is highly contested, and suggest that this arises from two main considerations: 1) there are a number of logically distinct categories of place expression, which are not always clearly distinguished in discourse about ‘place’; 2) the many non-substantive place count nouns (such as ‘place’, ‘region’, ‘area’, etc.) employed in natural language are highly ambiguous. With respect to consideration 1), we propose that place-related expressions should be classified into the following distinct logical types: a) ‘place-like’ count nouns (further subdivided into abstract, spatial and substantive varieties), b) proper names of ‘place-like’ objects, c) locative property phrases, and d) definite descriptions of ‘place-like’ objects. We outline possible formal representations for each of these. To address consideration 2), we examine meanings, connotations and ambiguities of the English vocabulary of abstract and generic place count nouns, and identify underlying elements of meaning, which explain both similarities and differences in the sense and usage of the various terms

Semantic Interoperability of Geospatial Ontologies: A Model-theoretic Analysis

People sometimes misunderstand each other, even when they use the same language to communicate. Often these misunderstandings happen when people use the same words to mean different things, in effect disagreeing about meanings. This thesis investigates such disagreements about meaning, considering them to be issues of semantic interoperability. This thesis explores semantic interoperability via a particular formal framework used to specify people’s conceptualizations of a given domain. This framework is called an ‘ontology,’ which is a collection of data and axioms written in a logical language equipped with a modeltheoretic semantics. The domain under consideration is the geospatial domain. Specifically, this thesis investigates to what extent two geospatial ontologies are semantically interoperable when they ‘agree’ on the meanings of certain basic terms and statements, but ‘disagree’ on others. This thesis defines five levels of semantic interoperability that can exist between two ontologies. Each of these levels is, in turn, defined in terms of six ‘compatibility conditions,’ which precisely describe how the results of queries to one ontology are compatible with the results of queries to another ontology. Using certain assumptions of finiteness, the semantics of each ontology is captured by a finite number of models, each of which is also finite. The set of all models of a given ontology is called its model class. The five levels of semantic interoperability are proven to correspond exactly to five particular relationships between the model classes of the ontologies. The exact level of semantic interoperability between ontologies can in some cases be computed; in other cases a heuristic can be used to narrow the possible levels of semantic interoperability. The main results are: (1) definitions of five levels of semantic interoperability based on six compatibility conditions; (2) proofs of the correspondence between levels of semantic interoperability and the model-class relation between two ontologies; and (3) a method for computing, given certain assumptions of finiteness, the exact level of semantic interoperability between two ontologies. These results define precisely, in terms of models and queries, the often poorly defined notion of semantic interoperability, thus providing a touchstone for clear definitions of semantic interoperability elsewhere

Marinas and other ports and facilities for the recreational craft sector: an ontology domain to support spatial planning.

Marinas and other ports and facilities for the recreational craft sector in Sardinia (Italy) can host more than 19,000 pleasure boats and yachts, according to a recent estimate (Osservatorio Nautico Nazionale, 2010); this capacity, at the national level, is second only to that of the Liguria region. However, Sardinian infrastructures and facilities are not part of a coherent network. Moreover, they are unevenly scattered along the coastline and are very diverse, in terms of type, dimension, and endowment of facilities for sailors. A key issue to be taken into account in the early stages of the preparation of a plan for the pleasure craft sector, which might create the conditions for the setting up of a coherent network, is the lack of a proper, detailed knowledge of the system of Sardinian marinas and other facilities. To this end, this paper begins with an analysis of current information (both spatial and non-spatial) and attempts to build a spatial database that integrates available data. The analysis identifies differences in structure and semantics, together with differences in purpose and date of production/update of the data, as the roots of inconsistencies among existing data produced by different sources. Such differences in structure and semantics risk, if not properly identified, considered and handled, to cause an incorrect integration of data. Following the methodology provided by the guidelines produced by the Ordnance Survey with regards to domain ontologies (Hart et al., 2007; Hart e Goodwin, 2007; Kovacs et al., 2006), the construction of an ontology of the domain of infrastructure and facilities for the recreational craft sector is therefore proposed as a possible solution to the problem. By applying this methodology, a ‘knowledge glossary,’ consisting of a shared vocabulary of core and secondary concepts and of relationships (some of which spatial) among concepts is developed, leading to the construction of a conceptual model of the domain, later formalized by means of the software Protégé.