Exploring Linked Data with Contextual Tag Clouds

Xingjian Zhang, Dezhao Song, Sambhawa Priya, Zachary Daniels, Kelly Reynolds, Jeff Heflin


In this paper we present the contextual tag cloud system: a novel application that helps users explore a large scale RDF dataset. Unlike folksonomy tags used in most traditional tag clouds, the tags in our system are ontological terms (classes and properties), and a user can construct a context with a set of tags that defines a subset of instances. Then in the contextual tag cloud, the font size of each tag depends on the number of instances that are associated with that tag and all tags in the context. Each contextual tag cloud serves as a summary of the distribution of relevant data, and by changing the context, the user can quickly gain an understanding of patterns in the data. Furthermore, the user can choose to include RDFS taxonomic and/or domain/range entailment in the calculations of tag sizes, thereby understanding the impact of semantics
on the data. In this paper, we describe how the system can be used as a query building assistant, a data explorer for casual users, or a diagnosis tool for data providers. To resolve the key challenge of how to scale to Linked Data, we combine a scalable preprocessing approach with a specially-constructed inverted index, use three approaches to prune unnecessary counts for faster online computations, and design a paging
and streaming interface. Together, these techniques enable a responsive system that in particular holds a dataset with more than 1.4 billion triples and over 380,000 tags. Via experimentation, we show how much our design choices benefit the responsiveness of our system.

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Type of Paper: Research Paper
Keywords: Linked Data, Tag Cloud, Semantic Data Exploration, Scalability
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