Aspect-Oriented Visual Ontology Editor (AVOnEd): Visual Language, Aspect-Oriented Editing Concept and Implementation

Abstract

When it comes to design and editing, complex ontologies have much in common with large and complex software systems. The ontology editor presented in this article adapts two solution approaches from software engineering to the task of ontology editing following the rationale that similar problems can be tackled with similar solutions. The first of the approaches is aspect orientation which allows to look at a problem from different perspectives and editing each perspective separately even with different names for one and the same entity. The article describes this approach’s theoretical foundations as well as the data model required for its implementation. The second approach is an auto-completion-like feature that checks whether editing steps on the ABox level are consistent with rules modeled on the TBox level and the ontology in general. The editor also features a visual language that is designed to facilitate editing OWL Lite based ontologies.

Intuitive Ontology-Based SPARQL Queries for RDF Data Exploration

“…Usually by means of the visual channel, this problem has been present in the field of the Semantic Web for different tasks: providing visual representations and tools that help the user to understand the underlying semantics (usually provided in the form of an ontology) [12], [13], navigating through different datasets available in LOD repositories [14], or performing a more in-depth exploration and analysis of the data [2], [4]- [6], [15].…”

“…Other tools such as [4], [14] integrate RDF in the ontology visualization, providing an exploration-oriented interface. Examples of the third approach, which provides data access and analysis together in the same application, can be found in [15], where a node-link diagram with the structure of the retrieved data is shown after the SPARQL queries are performed, and a second view is used to select the visualization which best summarizes and provides insight.…”

“…It can be seen how few of the reviewed works have covered most of the evaluated features. The main reason for this is the gap in the existing literature to tackle two different problems at the same time; although, much work has been done on ontology editing and visualization, such as [4], [5], [7], [15], and RDF data visualization [2], [6], [14]. However, fewer have covered the complete workflow, starting with ontology exploration, followed by query formulation based on the learned information, and a later exploration of the retrieved data, making use of different data visualizations and highlevel tasks such as linked-view manipulation and interactive filtering (described in [26]).…”