Dataset Recommendation for Data Linking: an Intensional Approach
published: July 28, 2016, recorded: June 2016, views: 11
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
With the growing quantity and diversity of publicly available web datasets, most notably Linked Open Data, recommending datasets, which meet specific criteria, has become an increasingly important, yet challenging problem. This task is of particular interest when addressing issues such as entity retrieval, semantic search and data linking. Here, we focus on that last issue. We introduce a dataset recommendation approach to identify linking candidates based on the presence of schema overlap between datasets. While an understanding of the nature of the content of specific datasets is a crucial prerequisite, we adopt the notion of dataset profiles, where a dataset is characterized through a set of schema concept labels that best describe it and can be potentially enriched by retrieving their textual descriptions. We identify schema overlap by the help of a semantico-frequential concept similarity measure and a ranking criterium based on the tf*idf cosine similarity. The experiments , conducted over all available linked datasets on the Linked Open Data cloud, show that our method achieves an average precision of up to 53% for a recall of 100%. As an additional contribution, our method returns the mappings between the schema concepts across datasets – a particularly useful input for the data linking step.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !