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Extending SPARQL IRI Dereferencing with Virtuoso Sponger Middleware

The Virtuoso SPARQL engine (called for brevity just SPARQL below) supports IRI Dereferencing, however it understands only RDF data; that is, it can retrieve only files containing RDF/XML-, Turtle-, or N3-serialized RDF data. If the format is unknown, it will try mapping with the built-in WebDAV metadata extractor. In order to extend this feature for dereferencing web or file resources which naturally don't have RDF data (like PDF or JPEG files, for example), a special mechanism is provided in the SPARQL engine. This mechanism is called RDF Mappers (Sponger Middleware) for translation of non-RDF data files to RDF.

To instruct the SPARQL engine to call an RDF Mapper, the Mapper must be registered and set to be called for a given URL or MIME-type pattern. In other words, when an unknown data format is received during the URL dereferencing process, the engine will look into a special registry (a table) to match either the MIME type or IRI using a regular expression; if a match is found, the mapper function will be called.

Registry

The table DB.DBA.SYS_RDF_MAPPERS is used for registering RDF Mappers.

CREATE TABLE DB.DBA.SYS_RDF_MAPPERS
  (
             RM_ID  INTEGER IDENTITY       ,  -- mapper ID, designate order of execution
        RM_PATTERN  VARCHAR                ,  -- a REGEX pattern to match URL or MIME type
           RM_TYPE  VARCHAR DEFAULT 'MIME' ,  -- what property of the current resource to match: MIME or URL are supported at present
           RM_HOOK  VARCHAR                ,  -- fully qualified PL function name, e.g., DB.DBA.MY_MAPPER_FUNCTION
            RM_KEY  LONG VARCHAR           ,  -- API specific key to use
    RM_DESCRIPTION  LONG VARCHAR           ,  -- Mapper description, free text
        RM_ENABLED  INTEGER DEFAULT 1      ,  -- a flag integer, 0 or 1, to respectively include or exclude the given mapper from processing chain
       PRIMARY KEY  (RM_TYPE, RM_PATTERN)
  )
;

The current way to register/update/unregister a mapper is just a DML statement, e.g., INSERT/UPDATE/DELETE.

Execution order and processing

As said above, when SPARQL retrieves a resource with unknown content, it will look in the Mappers registry, and loop over every record having the RM_ENABLED flag set to true. The sequence of look-up is based on ordering by the RM_ID column. For every record, it will try matching the MIME type and/or URL against the RM_PATTERN value; if there is a match, the function specified in the RM_HOOK column will be called. If the function doesn't exist or signals an error, the SPARQL engine will look at the next record.

When does it stop looking? It will stop if the value returned by the mapper function is a positive or negative number. If the return is negative, processing stops meaning no RDF was supplied; if the return is positive, the meaning is that RDF data was extracted; if a zero integer is returned, then SPARQL will look for next mapper. The mapper function may also return zero if it is expected that the next mapper in the chain will get more RDF data.

If none of the mappers matches the signature (MIME type nor URL), the built-in WebDAV metadata extractor will be called.

Extension function

The mapper function is a Virtuoso PL stored procedure with the following signature:

THE_MAPPER_FUNCTION_NAME
  (
        IN  graph_iri                 VARCHAR,
        IN  origin_uri                VARCHAR,
        IN  destination_uri           VARCHAR,
     INOUT  content                   VARCHAR,
     INOUT  async_notification_queue  ANY,
     INOUT  ping_service              ANY,
     INOUT  keys                      ANY
  )
  {
    -- do processing here
    -- return -1, 0 or 1 (as explained above in Execution order and processing section)
  }
;

Parameters

  • graph_iri - the target graph IRI
  • origin_uri - the current URI of processing
  • destination_uri - get:destination value
  • content - the resource content
  • async_notification_queue - if parameter PingService is specified in SPARQL section of the INI file, this is a pre-allocated asynchronous queue to be used to call ping service
  • ping_service - the URL of the ping service configured in parameter PingService is specified in SPARQL section of the INI file
  • keys - a string value contained in the RM_KEY column for given mapper, can be single string or serialized array, generally can be used as mapper specific data.

Return value

  • 0 - no data was retrieved or some next matching mapper must extract more data
  • +1 - data is retrieved; stop looking for other mappers
  • -1 - no data is retrieved; stop looking for more data

Cartridges package content

Virtuoso supplies the cartridges_dav VAD package as a cartridge for extracting RDF data from many popular Web resources and file types. It can be installed (if not already) using VAD_INSTALL function or the Virtuoso Conductor; see the VAD chapter in the documentation for details on how this can be done.

HTTP-in-RDF

Maps the HTTP request response to HTTP Vocabulary in RDF.

This mapper is disabled by default. If enabled, it must be first in the order of execution.

Also it will always return 0, which means any other mapper should push more data.

HTML

This mapper is composite and looks for metadata which can specified in HTML pages as follows:

  • Embedded/linked RDF
    • scan for meta in RDF <link rel="meta" type="application/rdf+xml">
    • RDF embedded in xHTML (as markup or inside XML comments)
  • Feeds extraction
  • xHTML metadata transformation using FOAF (foaf:Document) and Dublin Core properties (dc:title, dc:subject, etc.)

The HTML page mapper will look for RDF data in the order listed above. It will try to extract metadata at each step, and will return a positive flag if any step returns RDF data. In cases where the page URL matches some of the other RDF mappers listed in the registry, it will return 0 so the next mapper can attempt to extract more data. In order to function properly, this mapper must be executed before any other specific mappers.

Flickr URLs

This mapper extracts metadata of the Flickr images, using Flickr REST API. To function properly it must have a configured key. The Flickr mapper extracts metadata using: CC license, Dublin Core, Dublin Core Metadata Terms, GeoURL, FOAF, EXIF ontology.

Amazon URLs

This mapper extracts metadata from Amazon articles, using Amazon REST API. It needs a Amazon API key in order to function.

eBay URLs

Implements eBay REST API for extracting metadata from eBay articles, it needs a key and user name to be configured in order to work.

Open Office (OO) documents

The OO documents contains metadata which can be extracted using UNZIP, so this extractor needs Virtuoso's unzip plugin to be configured on the server.

Yahoo traffic data URLs

Implements transformation of the result of Yahoo traffic data to RDF.

iCal files

Transform iCal files to RDF as per <http://www.w3.org/2002/12/cal/ical#>.

Binary content, PDF, PowerPoint

Unknown binary content, PDF, and Microsoft PowerPoint files can be transformed to RDF using the Aperture framework. This mapper needs Virtuoso with Java hosting support, Aperture framework, and the MetaExtractor.class installed on the host system in order to work.

The Aperture framework and the MetaExtractor.class must be installed on the system before the RDF mappers package is installed. If the RDF Mappers package was previously installed, then to activate this mapper, you can just re-install the VAD.

Setting-up Virtuoso with Java hosting to run Aperture framework

  1. Install Virtuoso with Java hosting.
  2. Download the Aperture framework from http://aperture.sourceforge.net.
  3. Unpack in the Virtuoso working directory, e.g., where the database file is, and make a symbolic link 'lib' to it
  4. Configure in the INI [Parameters] section

    JavaClasspath = lib/sesame-2.0-alpha-3.jar:lib/openrdf-util-crazy-debug.jar:lib/htmlparser-1.6.jar:lib/activation-1.0.2-upd2.jar:lib/bcmail-jdk14-132.jar:lib/poi-scratchpad-3.0-alpha2-20060616.jar:lib/openrdf-model-2.0-alpha-3.jar:lib/jacob-1.10-pre4.jar:lib/bcprov-jdk14-132.jar:lib/demork-2.0.jar:lib/commons-codec.jar:lib/fontbox-0.1.0-dev.jar:lib/pdfbox-0.7.3.jar:lib/applewrapper-0.1.jar:lib/junit-3.8.1.jar:lib/winlaf-0.5.1.jar:lib/aperture-test-2006.1-alpha-3.jar:lib/openrdf-util-fixed-locking.jar:lib/commons-logging-1.1.jar:lib/mail-1.4.jar:lib/aperture-2006.1-alpha-3.jar:lib/poi-3.0-alpha2-20060616.jar:lib/ical4j-cvs20061019.jar:lib/openrdf-util-2.0-alpha-3.jar:lib/rio-2.0-alpha-3.jar:lib/poi-contrib-3.0-alpha2-20060616.jar:lib/aperture-examples-2006.1-alpha-3.jar:.

  5. Make sure the MetaExtractor.class is in the Virtuoso working directory
  6. Start the Virtuoso server with Java hosting support
  7. Connect with the ISQL tool to check if the installation is complete:

    SQL> DB.DBA.import_jar (NULL, 'MetaExtractor', 1); Done. -- 466 msec. SQL> select "MetaExtractor"().getMetaFromFile ('some_pdf_in_server_working_dir.pdf', 5); ... some RDF must be returned ...

    Important: The above is verified to work with aperture-2006.1-alpha-3 on a Linux system. For different versions of Aperture and/or operating system, this may need some adjustments, e.g., to re-build MetaExtractor.class, change CLASSPATH, etc.

Examples & tutorials

How to write your own RDF mapper? Look at the Virtuoso tutorial RDF Cartridges (RDF Mappers or RDFizers).

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