A Scalable Approach for Efficiently Generating Structured Dataset Topic Profiles

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