Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -- where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -- only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.

Search engines focusing on particular media types face difficulties in discovering suitable URIs on the Web. Since the engines are only interested in a small fraction of the Web, a crawler should use heuristics to concentrate on that fraction. To devise such a heuristic, we postulate four hypotheses based on RFCs and W3C recommendations to find cues for certain content types. Tests on a corpus of 22m files (793GB content size) containing 630m URIs show that for the content types text, image, and application, the recommendations are mostly being followed, while results for audio and video are much less consistent. Our findings and recommendations can be implemented as heuristics for efficient discovery of structured content on the Web on top of existing crawlers.

Current search engines do not fully leverage semantically rich datasets, or specialise in indexing just one domain-specific dataset.We present a search engine that uses the RDF data model to enable interactive query answering over richly structured and interlinked data collected from many disparate sources on the Web.