Synthesizing Knowledge Graphs for Link and Type Prediction Benchmarking
published: July 10, 2017, recorded: May 2017, views: 935
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.
Despite the growing amount of research in link and type prediction in knowledge graphs, systematic benchmark datasets are still scarce. In this paper, we propose a synthesis model for the generation of benchmark datasets for those tasks. Synthesizing data is a way of having control over important characteristics of the data, and allows the study of the impact of such characteristics on the performance of different methods. The proposed model uses existing knowledge graphs to create synthetic graphs with similar characteristics, such as distributions of classes, relations, and instances. As a first step, we replicate already existing knowledge graphs in order to validate the synthesis model. To do so, we perform extensive experiments with different link and type prediction methods. We show that we can systematically create knowledge graph benchmarks which allow for quantitative measurements of the result quality and scalability of link and type prediction methods.
Download slides: eswc2017_de_oliveira_melo_knowledge_graphs_01.pdf (1.3 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !