The explosion in the amount of the available RDF data has lead to the need to explore, query and understand such data sources. Due to the complex structure of RDF graphs and their heterogeneity, the exploration and understanding tasks are significantly harder than in relational databases, where the schema can serve as a first step toward understanding the structure. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; each is better suited for some uses, and each presents specific challenges with respect to its construction. This survey is the first to provide a comprehensive survey of summarization method for semantic RDF graphs. We propose a taxonomy of existing works in this area, including also some closely related works developed prior to the adoption of RDF in the data management community; we present the concepts at the core of each approach and outline their main technical aspects and implementation. We hope the survey will help readers understand this scientifically rich area, and identify the most pertinent summarization method for a variety of usage scenarios.

The Linked Open Data (LOD) cloud brings together information described in RDF and stored on the web in (possibly distributed) RDF Knowledge Bases (KBs). The data in these KBs are not necessarily described by a known schema and many times it is extremely time consuming to query all the interlinked KBs in order to acquire the necessary information. But even when the KB schema is known, we need actually to know which parts of the schema are used. We solve this problem by summarizing large RDF KBs using top-K approximate RDF graph patterns, which we transform to an RDF schema that describes the contents of the KB. This schema describes accurately the KB, even more accurately than an existing schema because it describes the actually used schema, which corresponds to the existing data. We add information on the number of various instances of the patterns, thus allowing the query to estimate the expected results. That way we can then query the RDF graph summary to identify whether the necessary information is present and if it is present in significant numbers whether to be included in a federated query result.