Abstract. We present the architecture of an end-to-end semantic search engine that uses a graph data model to enable interactive query answering over structured and interlinked data collected from many disparate sources on the Web. In particular, we study distributed indexing methods for graph-structured data and parallel query evaluation methods on a cluster of computers. We evaluate the system on a dataset with 430 million statements collected from the Web, and provide scale-up experiments on 7 billion synthetically generated statements.

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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 the Web 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.

Abstract. Online community sites have replaced the traditional means of keeping a community informed via libraries and publishing. At present, online communities are islands that are not interlinked. We describe different types of online communities and tools that are currently used to build and support such communities. Ontologies and Semantic Web technologies offer an upgrade path to providing more complex services. Fusing information and inferring links between the various applications and types of information provides relevant insights that make the available information on the Internet more valuable. We present the SIOC ontology which combines terms from vocabularies that already exist with new terms needed to describe the relationships between concepts in the realm of online community sites.