Spatial Relations for Semantic Similarity Measurement
نویسندگان
چکیده
Measuring semantic similarity among concepts is the core method for assessing the degree of semantic interoperability within and between ontologies. In this paper, we propose to extend current semantic similarity measures by accounting for the spatial relations between different geospatial concepts. Such integration of spatial relations, in particular topologic and metric relations, leads to an enhanced accuracy of semantic similarity measurements. For the formal treatment of similarity the theory of conceptual vector spaces— sets of quality dimensions with a geometric or topologic structure for one or more domains—is utilized. These spaces allow for the measurement of semantic distances between concepts. A case study from the geospatial domain using Ordnance Survey's MasterMap is used to demonstrate the usefulness and plausibility of the approach.
منابع مشابه
Semantic Similarity of Natural Language Spatial Relations
Communication problems between humans and machines are often the reason for failures or wrong computations. While machines use well-defined languages and rules in formal models to compute information, humans prefer natural language expressions with only vaguely specified semantics. Similarity comparisons are a central construct of the human way of thinking. For instance, humans are able to act ...
متن کاملمدلسازی روابط توپولوژیک سه بعدی فازی در محیط GIS
Nowadays, geospatial information systems (GIS) are widely used to solve different spatial problems based on various types of fundamental data: spatial, temporal, attribute and topological relations. Topological relations are the most important part of GIS which distinguish it from the other kinds of information technologies. One of the important mechanisms for representing topological relations...
متن کاملEvaluation of a Semantic Similarity Measure for Natural Language Spatial Relations
Consistent and flawless communication between humans and machines is the precondition for a computer to process instructions correctly. While machines use well-defined languages and formal rules to process information, humans prefer natural language expressions with vague semantics. Similarity comparisons are central to the human way of thinking: we use similarity for reasoning on new informati...
متن کاملEmergence of ontological relations from visual data with Self-Organizing Maps
In this paper we examine how Self-Organizing Maps (SOMs) can be used in detecting and describing emergent ontological relations between semantic objects and object classes in a visual database. The ontological relations we have studied include co-existence, taxonomies of visual and semantic similarity and spatial relationships. The used database contains 2618 images, each of which belongs to on...
متن کاملMetric of intrinsic information content for measuring semantic similarity in an ontology
Measuring information content (IC) from the intrinsic information of an ontology is an important however a formidable task. IC is useful for further measurement of the semantic similarity. Although the state-of-art metrics measure IC, they deal with external knowledge base or intrinsic hyponymy relations only. A current complex form of ontology conceptualizes a class (also often called as a con...
متن کامل