Knowledge graphs are large networks of entities, their semantic types, properties, and relationships between entities. They have become a powerful asset for search, analytics, recommendations, and data integration. Rooted in academic research and community projects such as DBpedia, Freebase, Yago, BabelNet, ConceptNet, Nell, Wikidata, WikiTaxonomy, and others, knowledge graphs are now intensively used at big industrial stakeholders. Examples are the Google Knowledge Graph, Facebook's Graph Search, Microsoft Satori, Yahoo Knowledge, as well as thematically specialized knowledge bases in business, finance, life sciences, and more. Many of these knowledge sources are available as Linked Open Data or RDF exports.

The goal of this special issue is to provide a stage for research on recent advances in knowledge graphs and their underlying semantic technologies. Traditional challenges of scalability, information quality, and data integration are of interest, but also specific projects that publish, study, or use knowledge graphs in innovative ways. More specifically, we expect submissions on (but not restricted to) the following topics.

Creation and curation of knowledge graphs Automatic and semi-automatic creation of knowledge graphs Data integration, disambiguation, schema alignment Collaborative management of knowledge graphs Quality control: noisy data, uncertainty, incomplete information New kinds of knowledge graphs: common-sense, visual knowledge, etc.

Management and querying of knowledge graphs Architectures for managing big graphs Expressive query answering Reasoning with large-scale, dynamic data Data dynamics, update, and synchronization Synthetic graphs and graph benchmarks

Applications of knowledge graphs Innovative uses of knowledge graphs Understanding and analyzing knowledge graphs Semantic search Question answering Combining knowledge graphs with other information resources

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A quote saved on Feb. 15, 2016.


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