The Semantic Web is growing fast and is rapidly emerging as a large-scale platform for publishing and sharing formalized knowledge models. It already contains a large amount of data, measured in millions of semantic documents and billions of triples. In addition, our analysis of the semantic documents crawled by the Watson Semantic Web search engine1 shows that hundreds of ontologies containing several thousands of classes have been published online in the last few years. Given such large scale availability of ontologies, ontology reuse is becoming commonplace and indeed tools such as the Watson plug-in for the NeOn toolkit are now available, which facilitate the task of locating and directly integrating ontologies or ontology fragments, which are relevant to an ontology under development.
In this reuse-centric context, it is highly desirable to have mechanisms that can efficiently help users in making sense of the content of an ontology. However, the empirical studies carried out in the NeOn project show that the visualization and navigation facilities available in today’s ontology engineering environments are not necessarily able to provide effective overviews of ontologies and often end up hindering rather than helping users. In particular our analysis shows that, not surprisingly, this is a problem especially for non-expert users.
To address this issue in NeOn we have developed a novel approach to visualizing ontologies, which exploits automatically created ontology summaries, based on the idea of key concepts . Informally, key concepts can be seen as the best descriptors of an ontology, i.e., the most information-rich concepts2, which are most effective in summarizing what an ontology is about. As described in , our algorithm for identifying key concepts in an ontology combines a number of criteria, drawn from cognitive psychology, lexical statistics and from the formal properties of an ontology.
The initial version of the KC-Viz tool for ontology visualization and navigation based on key concepts was presented in the NeOn Deliverable D4.5.4 . In this document we present a revision of KC-Viz, which improves over the earlier version, in particular by providing a more flexible set of functionalities for key-concept-based navigation of ontologies and also by addressing the performance and scalability issues exhibited by the earlier version. In addition, this new version is also compliant with the new API of the NeOn Toolkit, which is based on the OWL API. In what follows we provide an overview of the new version of the KC-Viz plugin, which is compatible with version 2.1 of the NeOn Toolkit, or above.
1 http://watson.kmi.open.ac.uk.
2 In this report we will use the terms “concept”, “class”, and “node” interchangeably, given that when applying the Key Concept Extraction algorithm to OWL ontologies, the “key concepts” analysed by the algorithm are actually OWL classes, and the KC-Viz tool displays classes/key concepts as nodes in a graph.