Download JAR files tagged by sparql with all dependencies

Provides transparent Linked Data Caching in the LMF triple store. When a resource is queried using e.g. LDPath or SPARQL and it represents an external resource (e.g. on DBPedia or GeoNames), the resource is transparently fetched on demand and cached locally.

InteGraal has been designed in a modular way, in order to facilitate software reuse and extension. It should make it easy to test new scenarios and techniques, in particular by combining algorithms. The main features of Graal are currently the following: (1) internal storage to store data by using a SQL or RDF representation (Postgres, MySQL, HSQL, SQLite, Remote SPARQL endpoints, Local in-memory triplestores) as well as a native in-memory representation (2) data-integration capabilities for exploiting federated heterogeneous data-sources through mappings able to target systems such as SQL, RDF, and black-box (e.g. Web-APIs) (3) algorithms for query-answering over heterogeneous and federated data based on query rewriting and/or forward chaining (or chase)

InteGraal has been designed in a modular way, in order to facilitate software reuse and extension. It should make it easy to test new scenarios and techniques, in particular by combining algorithms. The main features of Graal are currently the following: (1) internal storage to store data by using a SQL or RDF representation (Postgres, MySQL, HSQL, SQLite, Remote SPARQL endpoints, Local in-memory triplestores) as well as a native in-memory representation (2) data-integration capabilities for exploiting federated heterogeneous data-sources through mappings able to target systems such as SQL, RDF, and black-box (e.g. Web-APIs) (3) algorithms for query-answering over heterogeneous and federated data based on query rewriting and/or forward chaining (or chase)

The HTTP server module (ks-server-http) implements the Web API of the KnowledgeStore, which includes the two CRUD and SPARQL endpoints. The CRUD Endpoint supports the retrieval and manipulation of semi-structured data about resource, mention, entity and axiom records (encoded in RDF, possibly using JSONLD), and the upload / download of resource representation. The SPARQL Endpoint supports SPARQL SELECT, CONSTRUCT, DESCRIBE and ASK queries according to the W3C SPARQL protocol. The two endpoints are implemented on top of a component implementing the KnowledgeStore Java API (the Store interface), which can be either the the KnowledgeStore frontend (ks-frontend) or the Java Client. The implementation of the module is based on the Jetty Web sever (run in embedded mode) and the Jersey JAX-RS implementation. Reference documentation of the Web API is automatically generated using the Enunciate tool.

The Virtuoso server module (ks-server-virtuoso) provides an implementation of the Triple Store server-side component (API in ks-triplestore) on top of the OpenLink Virtuoso triple store, a scalable native code triple store that is accessed as an external service. The Virtuoso Sesame driver, which builds on the Virtuoso JDBC driver, is used to remotely access the Virtuoso server for writing data and performing SPARQL queries. For performance reasons, data modification is performed in a non-transactional way, with the module managing possible failures in a way that trigger the external repopulation of the Virtuoso triple store starting from the master copy of data stored in the Data Store component.

The IntelligentGraph SAIL offers an extended capability for embedded calculation support within any RDF graph. When enabled as an RDF4J SAIL, it offers calculation functionality as part of the RDF4J engine, on top of any RDF4J repository, using a variety of script engines including JavaScript, Jython, and Groovy. It preserves the SPARQL capability of RDF4J, but with additional capabilities for calculation debugging and tracing. IntelligentGraph includes the PathQL query language. Just as a spreadsheet cell calculation needs to access other cells, an IntelligentGraph calculation needs to access other nodes within the graph. Although full access to the underlying graph is available to any of the scripts, PathQL provides a succinct, and efficient method to access directly or indirectly related nodes. PathQL can either return just the contents of the referenced nodes, or the contents and the path to the referenced nodes. PathQL can also be used standalone to query the IntelligentGraph-enabled RDF database. This supplements, rather than replaces, SPARQL and GraphQL, as it provides graph-path querying rather than graph-pattern querying capabilities to any IntelligentGraph-enabled RDF database. The principles of IntelligentGraph are described here: https://inova8.com/bg_inova8.com/intelligent-graph-knowledge-graph-embedded-analysis/ The full PathQL syntax is described here: https://inova8.com/bg_inova8.com/pathpatternql-intelligently-finding-knowledge-as-a-path-through-a-maze-of-facts/ Using Jupyter as an IDE to IntelligentGraph and RDF4J, shown here: https://inova8.com/bg_inova8.com/intelligentgraph-getting-started/ IntelligentGraph source is here in GitHub: https://github.com/peterjohnlawrence/com.inova8.intelligentgraph IntelligentGraph Docker containers are available here: https://hub.docker.com/repository/docker/inova8/intelligentgraph