/ngsi-ld-converters

Primary LanguageJava

NGSI-LD Semantic Converter

Installing the Python script

  1. Clone this repository
git clone git@github.com:sparna-git/ngsi-ld-converters.git
cd ngsi-ld-converters
  1. Install pip
sudo apt install python3-pip

On wWindows, PIP is already included for versions of Python > 3.4.

  1. Install virtualenv
pip install virtualenv
# You may need to do this on Linux :
# sudo apt install python3.10-venv
  1. Create virtualenv
python3.10 -m venv virtualenv
  1. Activate virtualenv
Windows : virtualenv/Scripts/activate.bat
Linux : source virtualenv/bin/activate
  1. Once in the virtual env, installer the necessary dependencies from requirements.txt :
pip install -r requirements.txt

Running the Python script

  1. See the available options by running the script with --help flag
python3.10 convert_shacl_json.py --help

You will see the usage message.

  1. Run by passing as input the data file to convert, the SHACL rules to apply, and the JSON-LD framing spec to apply, and where to write the final output JSON file. Use the rules and JSON-LD framing files provided in the "rules" folder :
python3.10 convert_shacl_json.py --rules ../rules/datacube-2-statdcatap.ttl  --frame ../rules/framing-context.jsonld --data ../examples/structure-tourism-minimal/structures-tourism-minimal-pretty.ttl --output output.json

Approach

The approach taken to convert input RDF data to NGSI-LD is the following :

  1. Map source input data to target data structure at the semantic (= RDF triple) level
  2. Then serialize the output RDF in JSON-LD (any RDF library can do that)
  3. Then use JSON-LD Framing (https://www.w3.org/TR/json-ld11-framing/) to specify how the output triples should be serialized in a clean JSON-LD (use the JSON-LD contexts to map classes/properties to JSON keyx, specify which entity should come first, etc.)
  4. Then, if needed, split the JSON-LD in multiple files
    • splitting could be done earlier in the process, I don't know

Documentation pointers

Existing FIWARE converter documentation :

Smart data models :

DCAT problematics pointers :

Questions to Fernando and his answers

In existing converter, why are some properties reified (dct:description), and some not (dct:title, dct:identifier) ?

The problem is that some properties are defined in different contexts. There is a solution to correct it if inside the Smart Data Models Subject, we define unambiguously the property to be used in the scope of this subject. Then, we could dismiss the use of prefix in the JSON-LD.

It also means that the IoTAgent-Turtle need to make an update to align with this approach. We have started to modify the Data Models to reflect it and afterwards we will plan the steps to modify the parser with the new version of the data models.

In existing converter, why is URI mapped to dct:title and why is rdfs:label mapped to dct:description ?

First, it seems that something is missing in the question regarding the dct:title. We are mapping the SDMX into DCAT-AP/statDCAT-AP, this is the reason why we map rdfs:label to dct:description.

Existing converter generates separate files in the output. Is there a reason for this ? could the output be in a single file ?

ETSI NGSI-LD API defines the world based on entities. NGSI-LD Entity is the informational representative of something that is supposed to exist in the real world, physically or conceptually. Each of the data models are making reference to a concrete Entity and they are sent to the FIWARE Context Brokers using different Entities that in the end are different files.

The STAT-DCAT-AP json-ld context at https://smart-data-models.github.io/dataModel.STAT-DCAT-AP/context.jsonld maps keys to wrong URIs. Is it intended ? This is a blocking issue as it forces us to use these URIs (and not the typical dct ones) so that we can then regenerate a JSON-LD valid according to the spec.

In the context.jsonld there are automatically generated IRI based on the smart data models domain. These IRIs are provided for all terms and lead to a web page with basic information about the term, definition, data type, and data models where the term is also used. Now in some subjects, there is a complementary file ‘notes_context.jsonld’ with the original IRI whenever they are available. In order to create the proper context (with the original IRI) there is a service at the front page of smartdatamodels.org home -> tools -> map @context with external ontologies that whenever it detects a notes_context.jsonld file, make the change from the original terms producing a original @context.

I have the feeling that the JSON-LD context of StatDCAT-AP is incomplete; it does not contain the classes documented at https://github.com/INTERSTAT/Statistics-Contextualized/tree/main/pilots/Deliverable%203.2#dcat-ap-data-models

We can read at https://github.com/INTERSTAT/Statistics-Contextualized/tree/main/pilots/Deliverable%203.2#statdcat-ap-data-models that stat:numSeries or dqv:qualityAnnotation are not created in StatDCAT-AP Smart Data Model. Why ? what is the reason for not declaring a complete Stat DCAt-AP Smart Data Model ?

Not all StatDCAT-AP is used in the project and only those classes used in the project have been mapped. Keep in mind that the Smart Data Models program is based on real cases and therefore, we define the data models to cover those real cases. For those classes, It should have all the first-level terms in the context.jsonld, because it is generated automatically, but errors in the scripts are always possible if you could be more precise we could fix it. The other StatDACT-AP classes are not used because we are adopting the DCAT-AP analogue classes.

Same strange thing with dataModel.DCAT-AP at https://raw.githubusercontent.com/smart-data-models/dataModel.DCAT-AP/master/context.jsonld, which uses its own namespace, and fails to declare the types, e.g. CatalogueDCAT-AP used by the converter to convert Catalogs

Can you elaborate a little more the question. It is not clear for us.

It seems there is an NGSI-V2 with key-values syntax for which it is not necessary to decompose the properties : https://github.com/smart-data-models/dataModel.STAT-DCAT-AP/blob/master/Dataset/doc/spec.md#datasetstat-dcat-ap-ngsi-v2-key-values-example is this interesting ?

Smart Data Models program is agnostic to the data model representation, and its data models look compatible with several formats, including NGSIv2, NGSI-LD, but also SQL, DTDL, etc. Then, these examples in the spec are useful for NGSIv2 users.

The prefix stat in StatDCAT-AP is not yet assigned : http://data.europa.eu/(xyz)/statdcat-ap/

Correct, at the beginning of the implementation. We cannot find the URI in the specification. Nevertheless, during the modification of the data models to answer the first question, we discover that joinup has published the URI for the stat prefix (http://data.europa.eu/s1n). See https://joinup.ec.europa.eu/collection/semantic-interoperability-community-semic/solution/statdcat-application-profile-data-portals-europe/discussion/namespace-uri-still-be-finalised comment about it.

Is there any constraint on the URIs to generate ? the current generators construct URIs like urn:ngsi-ld:Measure:m1000 (using the local part of existing URI and crafting a new URI with urn:ngsi-ld prefix)

Only the limitation by the ETSI NGSI-LD standard (https://www.etsi.org/deliver/etsi_gs/CIM/001_099/009/01.06.01_60/gs_cim009v010601p.pdf, Annex A3). NGSI-LD defines a specific URN namespace. As it is based on URNs, the usage of this identification approach is not recommended when dereferenceable URIs are needed (fully-fledged linked data scenarios).

The referred namespace is defined as follows (to be registered with IANA): • Namespace identifier: NID = "ngsi-ld" • Namespace specific string: NSS = EntityTypeName ":" EntityIdentificationString

EntityTypeName shall be an Entity Type Name which can be expanded to a URI as per the @context. EntityIdentificationString shall be a string that allows uniquely identifying the subject Entity in combination with the other items being part of the NSS.

EXAMPLE: urn:ngsi-ld:Person:28976543.

It is recommended that applications use this URN namespace when applicable.

Should we create any dcat:Distribution ?

At the moment, taking into account the current real cases, we did not need to use dcat:Distribution. If in the incoming months, we see a special scenario in which it is needed the definition of this class we work on its definition.

Does generating the Catalogue automatically has any interest ?

SDMX DataStructureDefinition is mapped to the Dataset in DCAT-AP and the SDMX Dataset is mapped to the Catalogue DCAT-AP.

What is the scope exactly of what we need to transform ? the current converter converts properties, concepts, concept schemes, etc. but it looks like simple copy of the data. How should we deal with this ?

The scope of the IoTAgent-Turtle is to parse terse RDF TTL file format into the JSON-LD file format in order to apply ETSI NGSI-LD API and send the metadata and data to the FIWARE Context Brokers. It will demonstrate the integration of statistical scenarios into the FIWARE Architecture, facilitating the integration with third-parties services that can consume NGSI-LD API data.