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QuWeDa 2022 : 6th Workshop on Storing, Querying and Benchmarking Knowledge Graphs @ISWC2022 | |||||||||||
Link: https://sites.google.com/view/quweda2022 | |||||||||||
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Call For Papers | |||||||||||
he constant growth of Knowledge Graphs (KGs) on the Web raises new challenges for querying and integrating massive amounts of data across multiple KGs. Such KGs are available through various interfaces, such as data dumps, Linked Data Platform, SPARQL endpoints and Triple Pattern Fragments. In addition, various sources produce streaming data. Efficiently querying these sources is of central importance for the scalability of Linked Data and Semantic Web technologies. To exploit the massive amount of data to its full potential, users should be able to query and combine this data easily and effectively. This workshop at the International Semantic Web Conference (ISWC) seeks original articles describing theoretical and practical methods and techniques for fostering, querying, consuming, and benchmarking KGs.
This workshop at the International Semantic Web Conference 2022 (ISWC 2022) seeks original articles describing theoretical and practical methods and techniques for fostering, querying, and consuming the Data Web. Topics relevant to this workshop include -- but are not limited to -- the following: Representing and Storing the Web of Data as Knowledge Graphs Efficient representation Indexing Caching and replication Storage techniques Real-time data warehousing from Web data Querying the Web of Data as Knowledge Graphs Centralized, decentralized, federated, and distributed Source selection Lightweight Linked Data interfaces Web streams processing Big Data techniques Entailment regimes Read and write queries Linked Data documents and embedded Linked Data Query relaxation and rewriting Spatial Knowledge Graphs and GeoSPARQL querying Benchmarking the Web of Data as Knowledge Graphs Benchmarks Ranking Measures and metrics Performance evaluation Integrating different sources Querying non-Linked Data sources Combining public and private Linked Data Querying personal Linked Data stores Query languages for the Web Domain-specific query languages (e.g., temporal and spatial queries) Alternative languages for representing and querying the Web of Data GraphQL applications and optimizations |
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