The Linked Open Data (LOD) cloud is rapidly becoming the largest interconnected source of structured data on diverse domains. The potential of the LOD cloud is enormous, ranging from solving challenging AI issues such as open domain question answering to automated knowledge discovery. However, due to an inherent distributed nature of LOD and a growing number of ontologies and vocabularies used in LOD datasets, querying over multiple datasets and retrieving LOD data remains a challenging task. In this paper, we propose a novel approach to querying linked data by using alignments for processing queries whose constituent data come from heterogeneous sources. We also report on our Alignment based Linked Open Data Querying System (ALOQUS) and present the architecture and associated methods. Using the state of the art alignment system BLOOMS, ALOQUS automatically maps concepts in users’ SPARQL queries, written in terms of a conceptual upper ontology or domain specific ontology, to different LOD concepts and datasets. It then creates a query plan, sends sub-queries to the different endpoints, crawls for co-referent URIs, merges the results and presents them to the user. We also compare the existing querying systems and demonstrate the added capabilities that the alignment based approach can provide for querying the Linked data.
Joshi, A. K.,
Yeh, P. Z.,
Sheth, A. P.,
& Damova, M.
(2012). Alignment-based Querying of Linked Open Data. Lecture Notes in Computer Science, 7566, 807-824.