This paper proposes a method for supporting an exploratory analysis of spatiotemporal trend information by focusing on comparative analysis. Recent growth of the Web has brought us various kinds of information, among which trend information is one of crucial information for our decision-making. In order to develop an exploratory analysis support system for spatiotemporal trend information, visualization cube has been proposed. It is an abstract data structure with 4 axes, on which 5 operations for generating views are defined. The prototype system has been developed based on the concept of visualization cube, of which usability has been evaluated through the experiments conducted at an elementary school. However, it was also pointed out that the data space that can be explored with a single interaction by the existing system is not so large. In order to solve this problem, the proposed method employs dual views by considering two visualization cubes. By operating two visualization cubes at the same time, six kinds of comparative analyses in a broad sense can be supported. The exploratory analysis support system is developed based on the proposed concept, of which the effectiveness is evaluated through experiments with test participants. Although no synchronization mechanism between views is implemented in the prototype system, effective synchronization functions are discussed based on the experimental results.
Here is discussed how to build up ontologies with many properties from Japanese Wikipedia. The ontologies include is-a relationship (rdfs:subClassOf), class-instance relationship (rdf:type) and synonym relation (skos:altLabel) moreover it includes property relations and types. Property relations are triples, property domain (rdfs:domain) and property range (rdfs:range). Property types are object (owl:ObjectProperty), data (owl:DatatypeProperty), symmetric (owl:SymmetricProperty), transitive (owl:TransitiveProperty), functional (owl:FunctionalProperty) and inverse functional (owl:InverseFunctionalProperty). Experimental case studies show us that the built Japanese Wikipedia Ontology goes better than DBpedia from utility when we use, such as Hub of Linked Data, especially in Japan.