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.

Project Team

Former Members

Publications

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News

SANSA 0.7.1 (Semantic Analytics Stack) Released ( 2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann)

2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann

We are happy to announce SANSA 0.7.1 – the seventh release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs. Read more about "SANSA 0.7.1 (Semantic Analytics Stack) Released"

More Complete Resultset Retrieval from Large Heterogeneous RDF Sources ( 2019-12-05T15:46:09+01:00 Andre Valdestilhas)

2019-12-05T15:46:09+01:00 Andre Valdestilhas

Over recent years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laundromat and SPARQL endpoints provide access to hundreds of thousands of RDF datasets, representing billions of facts. Read more about "More Complete Resultset Retrieval from Large Heterogeneous RDF Sources"

DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released ( 2019-09-24T22:41:46+02:00 by Simon Bin)

2019-09-24T22:41:46+02:00 by Simon Bin

Dear all, The Smart Data Analytics group [1] and the E.T.-db-MOLE sub-group located at the InfAI Leipzig [2] is happy to announce DL-Learner 1.4. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. Read more about "DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released"

DBpedia Day @ SEMANTiCS 2019 ( 2019-08-01T10:35:05+02:00 Sandra Bartsch)

2019-08-01T10:35:05+02:00 Sandra Bartsch

 We are happy to announce that SEMANTiCS 2019 will host the 14th DBpedia Community Meeting at the last day of the conference on September 12, 2019. Read more about "DBpedia Day @ SEMANTiCS 2019"

LDK conference @ University of Leipzig ( 2019-03-22T09:21:41+01:00 by Julia Holze)

2019-03-22T09:21:41+01:00 by Julia Holze

With the advent of digital technologies, an ever-increasing amount of language data is now available across various application areas and industry sectors, thus making language data more and more valuable. Read more about "LDK conference @ University of Leipzig"