drb-metadata 1.4.2

DRB Metadata Extractor

drb-metadata

drb-metadata is a DRB Addon (MetadataAddon) that extracts a product's metadata according to its topic. It sits on top of the drb core (topic resolution) and drb-extractor (XQuery evaluation primitives).

Extracting metadata

from drb.metadata import MetadataAddon
import drb.topics.resolver as resolver

topic, node = resolver.resolve('<my_resource_url>')
metadata = MetadataAddon().apply(node, topic=topic)  # topic optional
for name, md in metadata.items():
    print(name, '--', md.extract())

MetadataAddon().apply(node, topic=…) returns a dict[str, DrbMetadata]. The topic keyword argument is optional; if omitted, the topic is resolved automatically from node.

Call .extract() on any DrbMetadata value to run its XQuery against the product and return the result.

Declaring metadata — the RDF model

Metadata are declared as RDF triples in the topic's descriptor graph using the metadataset: vocabulary:

namespace: http://knowledge-base.gael.fr/drb/addons/metadataset/
triple:    <topic-uri>  metadataset:<name>  """<xquery>"""

The XQuery string is evaluated against the product node at extraction time.

Inheritance and override. Topics inherit all metadataset: triples from their rdfs:subClassOf parents. A child topic can override any inherited metadata by redeclaring a triple with the same <name>.

Example (Turtle)

@prefix metadataset: <http://knowledge-base.gael.fr/drb/addons/metadataset/> .

<http://knowledge-base.gael.fr/drb/test/product>
    metadataset:platform_name """'SENTINEL-6'""" ;
    metadataset:filename      """name()""" .

Full TTL workflow

1. Write or extend a TTL file with metadataset: triples for your topic.

2. Expose the TTL to drb — two options:

   • Packaged descriptor (recommended for libraries): ship the file as cortex.ttl inside your Python package and declare a drb.topic entry point pointing to that package. drb loads it automatically on import.

   • Runtime registration (useful for dynamic or remote graphs): register the graph with ManagerDao before resolving:

     from drb.topics.dao import ManagerDao
     from drb.topics.dao.rdf_dao import RDFDao
     
     # From a local or remote TTL URL:
     ManagerDao().add_dao('<ttl-url>')
     
     # From a Fuseki / Graph Store endpoint (with optional auth):
     ManagerDao().add_dao_instance(RDFDao(['<sparql-endpoint-url>'],
                                          auth=('user', 'password')))
     

     See the core Resolution documentation for details on ManagerDao.

3. Resolve and apply:

   topic, node = resolver.resolve('<my_resource_url>')
   metadata = MetadataAddon().apply(node, topic=topic)
   

Out of scope. Server-side ingestion of TTL files into a Fuseki instance (e.g. ingest-kb-addons.sh) is handled by the drb-fuseki project and is not covered here.

extract_for_class(class_uri, graph)

When the class is not a resolvable DRB topic (e.g. a derived-table row class used by the Iceberg add-on), use extract_for_class to read metadataset: triples directly from a graph by URI:

from drb.metadata.core import extract_for_class
import rdflib

graph = rdflib.Graph().parse('<my-descriptor.ttl>')
md = extract_for_class('http://example.org/my-class', graph)
# md is a dict[str, DrbMetadata]; bind a node before calling .extract():
md['my_field'].node = some_node
print(md['my_field'].extract())

Migration note

The cortex.yaml format and the drb.metadata Python entry point have been removed. Metadata are now declared exclusively as metadataset: triples in the topic's RDF descriptor (TTL file). Remove any cortex.yaml files and drb.metadata entry points from your packages and replace them with metadataset: triples in a cortex.ttl (or equivalent TTL) exposed via a drb.topic entry point.