Sparqlify: a SPARQL-SQL rewriter

Sparqlify is a SPARQL-SQL rewriter that enables one to define RDF views on relational databases and query them with SPARQL. It is currently in alpha state and powers the Linked-Data Interface of the LinkedGeoData Server – i.e. it provides access to billions of virtual triples from the OpenStreetMap database.

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Key Features

  • A novel syntax for view definitions inspired by SQL's CREATE VIEW statement. We believe this to lower the learning curve for defining RDB-RDF mappings.
  • A query is rewritten into a single SQL statement, giving all control over query planning to the underlying database system.
  • Support of geo-spatial functions: In general, Sparqlify supports mapping custom SPARQL functions to relational ones. Some mappings for PostGIS are already provided (e.g. intersection with polygons).

Limitations

Please be aware that Sparqlify is currently in alpha state and the following limitations hold:

  • For the moment, only the PostgreSQL database system is supported.
  • Only a subset of SPARQL 1.0 + Sub-Queries is supported: For instance, the implementation of aggregate functions including COUNT is still pending.
  • Support for Sparql 1.1 property paths is very unlikely in the near future.

Publications

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News

AKSW Colloquium, 15.02.2016, Mandolin + X-Feasible ( 2016-02-13T20:48:58+01:00 TommasoSoru)

2016-02-13T20:48:58+01:00 TommasoSoru

On the 15th of February at 3 PM, Tommaso Soru will present his ongoing research titled “Mandolin: Markov Logic Networks for Discovering Links”. Read more about "AKSW Colloquium, 15.02.2016, Mandolin + X-Feasible"

DL-Learner 1.2 (Supervised Structured Machine Learning Framework) Released ( 2016-02-09T16:19:55+01:00 by Patrick Westphal)

2016-02-09T16:19:55+01:00 by Patrick Westphal

Dear all, we are happy to announce DL-Learner 1.2. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. Read more about "DL-Learner 1.2 (Supervised Structured Machine Learning Framework) Released"

AKSW Colloquium, 01.02.2016, Co-evolution of RDF Datasets ( 2016-02-01T15:53:23+01:00 by Natanael Arndt)

2016-02-01T15:53:23+01:00 by Natanael Arndt

At the todays colloquium, Natanael Arndt will discuss the the paper “Co-evolution of RDF Dataset” by Sidra Faisal, Kemele M. Endris, Saeedeh Shekarpour and Sören Auer (2016, available on arXiv) Link: http://arxiv.org/abs/1601. Read more about "AKSW Colloquium, 01.02.2016, Co-evolution of RDF Datasets"

Holographic Embeddings of Knowledge Graphs ( 2016-02-01T14:32:03+01:00 by Johannes Frey)

2016-02-01T14:32:03+01:00 by Johannes Frey

During the upcoming colloquium, Nilesh Chakraborty will give a short introduction on factorising RDF tensors and present a paper on “Holographic Embeddings of Knowledge Graphs”: Holographic Embeddings of Knowledge Graphs Authors: Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio Abstract: Learning embeddings … Continue reading → Read more about "Holographic Embeddings of Knowledge Graphs"

AKSW Colloquium, 25.01.2016, LargeRDFBench and Introduction To The Docker Ecosystem ( 2016-01-25T14:39:49+01:00 by Ivan Ermilov)

2016-01-25T14:39:49+01:00 by Ivan Ermilov

On the upcoming colloquium, Muhammad Saleem will present his paper “LargeRDFBench: A Billion Triples Benchmark for SPARQL Endpoint Federation” about the benchmarking of federated SPARQL endpoints. The other talk will be an introduction to the Docker ecosystem by Tim Ermilov. Read more about "AKSW Colloquium, 25.01.2016, LargeRDFBench and Introduction To The Docker Ecosystem"