LAW: Link-AWare Source Selection for Virtually Integrating Linked Data

Abstract

With the wide adoption of linked data principles, a large amount of structural data have emerged on World Wide Web. These data are interlinked and form a Web of Data. Yet, so far, only little attention has been paid to the effect of links on federated querying. This work presents LAW, a novel link-aware approach for federated SPARQL queries over the Web of Data. The source selection module (called LAWS) of LAW can be directly combined with existing federated query engines in order to achieve the same query recall values while querying fewer datasets. We extend three well-known federated query engines with LAWS and compare our extensions with the original approaches. The comparison shows that LAWS can greatly reduce the number of queries sent to the endpoints, while keeping high query recall values. Therefore, it can significantly improve the performance of federated query processing engines. We also have implemented LAW as an independent system. A wide experimental study shows that LAW has higher performance than state-of-the-art federated query systems.