Querying Distributed RDF Data Sources with SPARQL

By Bastian Quilitz, Ulf Leser. Extended Semantic Web Conference, 2008

598 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/b889b2854ea71db981c719f72860b35b79c8737e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sarasi Lalithsena
2. Tom Heath
3. Christian Bizer
4. Olaf Hartig
5. Michael Schmidt
6. Katja Hose
7. Peter Haase
8. Andreas Schwarte

In Computer Networks and Communications

1. Christian Bizer
2. Tom Heath
3. Sarasi Lalithsena
4. Olaf Hartig
5. Michael Schmidt
6. Katja Hose
7. Andreas Schwarte
8. Ralf Schenkel

In Information Systems and Management

1. Tom Heath
2. Giuseppe Rizzo
3. Raphael Troncy
4. Olaf Hartig
5. Richard Cyganiak
6. Michael Hausenblas
7. Christian Bizer
8. Johann Christoph Freytag

Citations in the corpus

• FedX: Optimization Techniques for Federated Query Processing on Linked Data (2011, methodology): “DARQ uses so-called service descriptions to summarize capabilities and statistics of data providers.”
• Executing SPARQL Queries over the Web of Linked Data (2009, methodology): “The DARQ engine [11], for instance, decomposes a SPARQL query in subqueries, forwards these subqueries to multiple, distributed query services, and, finally, integrates the results of the subqueries.”
• Linked Data - The Story So Far (2009, unknown): “Federated query architectures for Linked Data include DARQ ( Quilitz & Leser, 2008 ) and SemaPlorer (Schenk et al., 2008).”
• entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation (2017, methodology): “For each of these properties, we obtain the subgraph Kp through SPARQL [23] queries. e properties for which we obtain a su cient number of triples (> 15) are those reported in Tab.”
• Linked Data: Evolving the Web into a Global Data Space (2011, methodology): “Federated SPARQL engines include DARQ [96] and SemaPlorer [99].”
• BYU-EVE: Mixed Initiative Dialog via Structured Knowledge Graph Traversal and Conversational Scaffolding (2018, methodology): “... SPARQL 1.1, however, still lacks the ability to reference a path directly in a query (e.g., the ability to store the result of a property path query in a path variable).”
• Describing Linked Datasets (2009, methodology): “Regarding federated SPARQL queries, DARQ (Distributed ARQ) [22] proposes so called “service descriptions” that are able to specify capabilities of a SPARQL endpoints.”
• Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept. (2016, methodology): “Each hospital had its own triplestore with data installed and these triplestores can be queried using SPARQL [32], the query language of the semantic web.”