Presented at: 19th International World Wide Web Conference (WWW2010)
Typical approaches for search and querying over structured Web Data collect (crawl) and pre-process (index) large amounts of data before allowing for query answering in a central data warehouse. This time-consuming pre-processing phase decreases the freshness of query results and only uses to a limited degree the benefits of Linked Data where structured data is accessible live and up-to-date at distributed Web resources that may change constantly. An ideal query answering system for Linked Data should return always current answers in a reasonable amount of time, even on corpora as large as the web. Query processors evaluating queries directly on the life sources require knowledge of the contents of data sources. In the current paper we develop and evaluate a probabilistic index structure for covering graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on the web exploiting this structure, and evaluate the system using synthetically generated queries. We find that our lightweight index structure enable more complete query results over Linked Data compared to direct lookup approaches, while keeping the overhead for additional lookups and index maintenance low.
Keywords: Querying, distributed approaches, including combinations with statistics, searching the Semantic/Data Web, soft computing
Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2010/paper/main/570
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