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A. Vckovski, K. Brassel, and H.-J. Schek (eds.), Assessing Semantic Similarities Among Geospatial Feature Class Definitions
Abstract
The assessment of semantic similarity among objects is a basic requirement for semantic interoperability. This paper presents an innovative approach to semantic similarity assessment by combining the advantages of two different strategies: featurematching process and semantic distance calculation. The model involves a knowledge base of spatial concepts that consists of semantic relations (is-a and part-whole) and distinguishing features (functions, parts, and attributes). By taking into consideration cognitive properties of similarity assessments, this model expects to represent a cognitively plausible and computationally achievable method for measuring the degree of interoperability.
... When calculating semantic similarity according toFeng and Flewelling (2004)(Equation 5), weights can be assigned in two different manners: the a or the weights that are assigned to each pair of A\B, A/B and B/A in Equation (5). For estimating a of categories within a single categorisation system,Rodríguez et al. (1999)suggested that the number of links from both categories to the immediate category that includes both categories can be used. But when using different categorisation systems, this is impossible and a value of 0.5 can be assigned to a (thus 1 – a is also 0.5). ...
Harmonisation of land-cover data relates to spatial data integration and therefore needs to consider the data concepts adopted and the spatial, temporal, semantic and quality aspects of the data. Differences in semantic concepts are often considered the key obstacle to data integration and interoperability. If the problem of harmonisation is limited to the variation in the semantic content of data expressed as differences in categorisation, then various approaches have been developed to address the methodological issues and for computing semantic similarity. Five Nordic class sets were selected for establishing correspondences between their semantic class contents using the parameterised land cover classification system (LCCS) as a reference system. Subsequently, semantic similarities between pair-wise classes were calculated using a module of LCCS. This article first examines the aspects of land-cover harmonisation and the LCCS methodologies for categorisation and semantic similarity. It then discusses the functioning of LCCS as a reference system in which the more or less corresponding class of the original Nordic classes was determined and the semantic similarity indices computed. Suggestions are provided for improvements in the LCCS methodology, both in establishing correspondences and for computing semantic similarity. Recommendations are given for the way forward in land-cover harmonisation and for measures to express the quality of harmonisation of the semantic contents of class sets at class set level and individual class level.
This chapter describes what is meant by semantic interoperability of geographic information, the elements involved in semantic interoperability of geographic information, and how Geographic Information (GI) interoperability semantic interoperability of geographic information has been addressed in spatial data infrastructures Spatial Data Infrastructure (SDI) (SDIs), standards, and in the Semantic Web. It is formed of ten sections. Semantics is a term used frequently in many contexts. Section 15.2 is dedicated to the description of semantics. Cognition is an area of study about the abstraction of phenomena by human beings. It is significantly related to semantics. Section 15.3 introduces basic notions of cognition related to semantics. Ontology is a well-grounded notion in the study of semantic ontology interoperability and in the Semantic Web. Section 15.4 explains what ontology is about. This will lead to the description, in Sect. 15.5, of semantic interoperability in the context of geographic information, i.e., geosemantic geosemantic interoperability interoperability. A spatial data infrastructure is the typical way in which interoperability of Geographic Information (GI) standard geographic information is implemented. Section 15.6 addresses geosemantic interoperability with respect to spatial data infrastructures. Standards underpin interoperability for geographic information and spatial data infrastructures. Section 15.7 provides an overview of the current contribution of standards bodies with respect to geosemantic interoperability. The Semantic Web makes an important contribution to semantic interoperability. Section 15.8 gives an account of the Semantic Web and its development. The Geospatial Semantic Web brings together the effort of geosemantic interoperability with the effort of the Semantic Web, as presented in Sect. 15.9. Section 15.10 provides concluding remarks on the chapter.
Reusing data produced by other sources is the main motivation of system interoperability and data integration; enterprises tend to reduce their investment for producing data by integrating outsourced data. With the geographic information systems (GIS) community the scenario is not different. Data integration process has to resolve the differences in data structures, as well as, solving semantic heterogeneities. We consider ontologies as knowledge structures that specify terms, their properties and relations among them to enable integration. The presented approach integrates schemata of different GIS, where each GIS is using its own Geo-ontology. To reach our goal the concept of semantic similarity is used and the similarity relations between heterogeneous ontologies are discovered by reasoning system using the high level ontology WordNet. The output of the system is semantic similarity measure that is a confidence score that reflects the semantic relation between the meanings of two concepts.
This research addresses a major shortcoming in todays requirements analysis techniques—the lack of a rigorous and comprehensive process to explicitly capture the relationship structure of the problem domain. Whereas other analysis techniques lightly address the relationship discovery process, relationship analysis (RA) is a systematic, domain-independent analysis technique focusing exclusively on a domains relationship structure. This paper describes RAs taxonomy of relationship types and corresponding brainstorming questions for eliciting the relationship structure from a domain expert. A preliminary case study analysis of online bookstores using RA as well as a formal experiment have both confirmed RAs effectiveness in helping the analyst produce significantly higher quality requirements. RA should become an invaluable tool for analysts, irrespective of the software engineering approach taken during systems analysis.
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.
Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness of place as well as semantic closeness with respect to the information of interest. Here we present an ontology of place that combines limited coordinate data with semantic and qualitative spatial relationships between places. This parsimonious model of geographical place supports maintenance of knowledge of place names that relate to extensive regions of the Earth at multiple levels of granularity. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical spatial distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This is integrated with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects.
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