Improving the Visualization of WordNet Large Lexical Database through Semantic Tag Clouds

Abstract

In the Big Data era, the visualization of large data sets is becoming an increasingly relevant task due to the great impact that data have from a human perspective. Since the visualization is the closer phase to the users within the data life cycles phases, there is no doubt that an effective, efficient and impressive representation of the analyzed data may result as important as the analytic process itself. Starting from previous experiences in importing, querying and visualizing WordNet database within Neo4J and Cytoscape, this work aims at improving the WordNet Graph visualization by exploiting the features and concepts behind tag clouds. The objective of this study is twofold: firstly, we argue that the proposed visualization strategy is able to put order in the messy and dense structure of nodes and edges of large knowledge bases as WordNet, showing as much as possible information from this knowledge source and in a clearer way; secondly, we think that the tag cloud approach applied to the synonyms rings reinforces the human cognition in recognizing the different usages of words in natural languages like English. In this regard, we also propose a formal strategy in order to evaluate the information perception in the use of our methodology by means of a questionnaire asked to a group of users. Finally, we compare these results with those resulting from the adoption of well known representations of WordNet within existing GUIs.