Scalable Keyword Search on Big RDF Data

Keyword search is a useful tool for exploring large RDF datasets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summarization from the RDF graphs for query processing. In this work, we show that existing techniques have serious limitations in dealing with realistic, large RDF graphs with tens of millions of triples. Furthermore, the existing summarization techniques may also return incorrect results. To address these issues, we propose a new, succinct and effective summarization from the underlying RDF data. Given a keyword query, the summarization lends significant pruning powers to exploratory keyword search and leads to much better efficiency compared to previous work. Unlike existing summarization techniques, our summarization always returns exact results and can be updated incrementally and efficiently. Experiments on both benchmark and large real RDF datasets show that our techniques are highly scalable and efficient.