From Web Data to Visualization via Ontology Mapping

Abstract

In this paper, we propose a novel approach for automatic generation of visualizations from domain-specific data available on the web. We describe a general system pipeline that combines ontology mapping and probabilistic reasoning techniques. With this approach, a web page is first mapped to a Domain Ontology, which stores the semantics of a specific subject domain (e.g., music charts). The Domain Ontology is then mapped to one or more Visual Representation Ontologies, each of which captures the semantics of a visualization style (e.g., tree maps). To enable the mapping between these two ontologies, we establish a Semantic Bridging Ontology, which specifies the appropriateness of each semantic bridge. Finally each Visual Representation Ontology is mapped to a visualization using an external visualization toolkit. Using this approach, we have developed a prototype software tool, SemViz, as a realisation of this approach. By interfacing its Visual Representation Ontologies with public domain software such as ILOG Discovery and Prefuse, SemViz is able to generate appropriate visualizations automatically from a large collection of popular web pages for music charts without prior knowledge of these web pages.

A Software Reference Architecture for Service-Oriented 3D Geovisualization Systems

“…Models for generating visual representations can be based on modeling the stepwise, functional transformation of data into visual representations in the form of visualization pipelines and data flow graphs [39][40][41]. They can also be based on aspects more abstract, such as intent [50], users [51], tasks [52], context [51], data [53] or interaction [54], to automatically generate or recommend appropriate visual representations using encoded expert knowledge, heuristics or design principles [55]. Visualization models introduce specific abstractions as in the case of the visualization pipeline: pipeline, pipeline stage, operators providing stage functionality and operator data products [39][40][41].…”

Creating Visualizations through Ontology Mapping

“…Gilson et al describes an approach that uses ontology mapping techniques and probabilistic reasoning to automatically create visualizations for domain specific data from the web [11]. Three ontologies (domain, visual representation, and semantic bridging ontology) are used to capture expert knowledge.…”

Weighted faceted browsing for characteristics-based visualization selection through end users

“…InfoVis tools provide different levels of user support to find appropriate visualizations [4]. One of the most sophisticated is Tableau, comprising the "Show Me" mechanism which suggests graphic representations for the selected data variables [12].…”