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Exploring Linked Data with Contextual Tag Clouds

10 Pages Posted: 3 Jul 2018 Publication Status: Accepted

See all articles by Xingjian Zhang

Xingjian Zhang

Lehigh University - Department of Computer Science and Engineering

Dezhao Song

Lehigh University - Department of Computer Science and Engineering

Sambhawa Priya

Lehigh University - Department of Computer Science and Engineering

Zachary Daniels

Lehigh University - Department of Computer Science and Engineering

Kelly Reynolds

Lehigh University - Department of Computer Science and Engineering

Jeff Heflin

Lehigh University - Department of Computer Science and Engineering

Abstract

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 pre-processing 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.

Keywords: Linked Data, Tag Cloud, Semantic Data Exploration, Scalability

Suggested Citation

Zhang, Xingjian and Song, Dezhao and Priya, Sambhawa and Daniels, Zachary and Reynolds, Kelly and Heflin, Jeff, Exploring Linked Data with Contextual Tag Clouds (January 1, 2014). Journal of Web Semantics First Look, Available at SSRN: https://ssrn.com/abstract=3199085 or http://dx.doi.org/10.2139/ssrn.3199085

Xingjian Zhang (Contact Author)

Lehigh University - Department of Computer Science and Engineering ( email )

19 Memorial Drive West
Bethlehem, PA 18015
United States

Dezhao Song

Lehigh University - Department of Computer Science and Engineering

19 Memorial Drive West
Bethlehem, PA 18015
United States

Sambhawa Priya

Lehigh University - Department of Computer Science and Engineering

19 Memorial Drive West
Bethlehem, PA 18015
United States

Zachary Daniels

Lehigh University - Department of Computer Science and Engineering

19 Memorial Drive West
Bethlehem, PA 18015
United States

Kelly Reynolds

Lehigh University - Department of Computer Science and Engineering ( email )

19 Memorial Drive West
Bethlehem, PA 18015
United States

Jeff Heflin

Lehigh University - Department of Computer Science and Engineering ( email )

19 Memorial Drive West
Bethlehem, PA 18015
United States

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