Linked Data querying over cached indexes of Web data often suffers from stale or missing results due to infrequent updates and partial coverage of available sources. Conversely, live decentralised approaches offer fresh results taken directly from the Web, but suffer from slow response times due to the expense of numerous remote lookups at runtime. We thus propose a hybrid query approach that improves upon both paradigms, offering fresher results from a broader range of sources than Linked Data caches while offering faster results than live querying. Our hybrid query engine takes a cached and live query engine as black boxes, where a hybrid query planner splits an input query and delegates the appropriate sub-queries to each interface. In this paper, we discuss the core query-planning issues and their main strengths and weaknesses. We also present coherence measures to quantify the coverage and freshness for cached indexes of Linked Data, and show how these measures can be used during query planning to maximise the trade-off between fresh results and fast query execution.
Resource URI on the dog food server: http://data.semanticweb.org/conference/ekaw/2012/paper/research/71
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