A Scalable Approach for Efficiently Generating Structured Dataset Topic Profiles

author: Besnik Fetahu, L3S Research Center, Leibniz University of Hannover
published: July 30, 2014,   recorded: May 2014,   views: 2112
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Description

The increasing adoption of Linked Data principles has led to an abundance of datasets on the Web. However, take-up and reuse is hindered by the lack of descriptive information about the nature of the data, such as their topic coverage, dynamics or evolution. To address this issue, we propose an approach for creating linked dataset pro les. A pro le consists of structured dataset metadata describing topics and their relevance. Pro les are generated through the con guration of techniques for resource sampling from datasets, topic extraction from reference datasets and their ranking based on graphical models. To enable a good trade-o between scalability and accuracy of generated pro les, appropriate parameters are determined experimentally. Our evaluation considers topic pro les for all accessible datasets from the Linked Open Data cloud. The results show that our approach generates accurate pro les even with comparably small sample sizes (10%) and outperforms established topic modelling approaches

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Download slides icon Download slides: eswc2014_fetahu_topic_profiles_01.pdf (4.1┬áMB)


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