The Geospatial Web: How Geobrowsers, Social Software and the Web 2.0 are Shaping the Network Society (Advanced Information and Knowledge Processing)

Semantic similarity plays an important role in geographic information systems as it supports the identification of objects that are conceptually close, but not identical. Similarity assessments are particularly important for retrieval of geospatial data in such settings as digital libraries, heterogeneous databases, and the World Wide Web. Although some computational models for semantic similarity assessment exist, these models are typically limited by their inability to handle such important cognitive properties of similarity judgements as their inherent asymmetry and their dependence on context. This paper defines the Matching-Distance Similarity Measure (MDSM) for determining semantic similarity among spatial entity classes, taking into account the distinguishing features of these classes (parts, functions, and attributes) and their semantic interrelations (is–a and part–whole relations). A matching process is combined with a semantic-distance calculation to obtain asymmetric values of similarity that ...

http://www.inf.udec.cl/~andrea/papers/IJGIS04.pdf

Comparing geospatial entity classes: an asymmetric and context-dependent similarity measure

This paper reviews research in geographical cognition that provides part of the theoretical foundation of geographical information science. Freestanding research streams in cognitive science, behavioural geography, and cartography converged in the last decade or so with work on theoretical foundations for geographical information systems to produce a coherent research community that advances geographical information science, geographical information systems, and the contributing fields and disciplines. Then, we review three high-priority research areas that are the topics for research initiatives within the NCGIA's Project Varenius. Other topics consider but ranked less important at this time are also reviewed.

/pdf/cognitive-models-of-geographical-space-3j1h69zu8s.pdf

Cognitive models of geographical space

A linguistic ontology of space for natural language processing

Cardinal directions are frequently used as selection criteria in spatial queries or for assessing similarities of spatial scenes. Current models for cardinal directions use crude approximations in the form of the objects' minimum bounding rectangles or their generalizations to points. To overcome the limitations of these models so that improved reasoning can be performed, the coarse direction-relation matrix is introduced. It partitions space around a reference object and records into which direction tiles an extended target object falls. The detailed direction-relation matrix captures more details by recording the ratio of the target object in each direction tile or the number of separations per tile. This multi-resolution model provides a better approximation for direction relations of complexly structured spatial objects than the approach with minimum bounding rectangles. 
In order to record directions between arbitrary pairs of point, line, and region objects, the model based on the coarse direction-relation matrix is extended to the deep direction-relation matrix. It additionally records information about the intersection of the target object with the boundaries of direction tiles, if necessary. This thesis demonstrates that directions recorded at smaller scales using this model are compatible with the directions recorded at larger scales. The compatibility makes this model useful for direction-based queries in spatial databases over multiple scales. 
To apply direction-relation matrices for the assessment of similarity between spatial scenes, this thesis develops a method to compute similarity between direction-relation matrices. The similarity between two direction-relation matrices depends on the distances between cardinal directions along a conceptual neighborhood graph, which has a node for each direction tile and edges connecting nodes corresponding to neighboring tiles. There are two types of graphs: the 4-neighborhood graph and the 8-neighborhood graph. The comparative study of the mappings from directions changes to similarity values provided by the graphs reveals that the 4-neighbohood graph provides a sounder mapping than the 8-neighborhood graph. The similarity assessment method gives cognitively plausible rankings of spatial scenes based on the cardinal direction between objects, and it is useful in retrieving spatially similar scenes in image databases, video databases, multimedia databases, and web databases.

Similarity assessment for cardinal directions between extended spatial objects

relations are the basis for many selections users perform when they query geographic information systems (GISs). Although such query languages use natural-language-like terms, the formal definitions of those spatial relations rarely reflect the same meaning people would apply when they communicate among each other. To bridge the gap between computational models for spatial relations and people's use of spatial terms in their natural languages, a model for the geometry of spatial relations was calibrated for a set of 59 English-language spatial predicates. The model distinguishes topological and metric properties. The calibration from sketches that were drawn by 34 human subjects identifies ten groups of spatial terms with similar properties and provides a mapping from spatial terms onto significant geometric parameters and their values. The calibration's results reemphasize the importance of topological over metric properties in the selection of English-language spatial terms. The model provides a basis for high-level spatial query languages that exploit natural-language terms and serves as a model for processing such queries.

Natural-Language Spatial Relations Between Linear and Areal Objects: The Topology and Metric of English- Language Terms *

Spatial relations often are desired answers that a geographic information system (GIS) should generate in response to a user's query. Current GIS's provide only rudimentary support for processing and interpreting natural-language-like spatial relations, because their models and representations are primarily quantitative, while natural-language spatial relations are usually dominated by qualitative properties. Studies of the use of spatial relations in natural language showed that topology accounts for a significant portion of the geometric properties. This article develops a formal model that captures metric details for the description of natural-language spatial relations. The metric details are expressed as refinements of the categories identified by the 9-intersection, a model for topological spatial relations, and provide a more precise measure than does topology alone as to whether a geometric configuration matches with a spatial term or not. Similarly, these measures help in identifying the spatial term that describes a particular configuration. Two groups of metric details are derived: splitting ratios as the normalized values of lengths and areas of intersections; and closeness measures as the normalized distances between disjoint object parts. The resulting model of topological and metric properties was calibrated for 64 spatial terms in English, providing values for the best fit as well as value ranges for the significant parameters of each term. Three examples demonstrate how the framework and its calibrated values are used to determine the best spatial term for a relationship between two geometric objects.

A. Rashid B. M. Shariff

Papers

Natural-Language Spatial Relations Between Linear and Areal Objects: The Topology and Metric of English- Language Terms *

Metric details for natural-language spatial relations