SKOSMOS

SKOSMOS container setup

Run with docker-compose

Prepare

Create a new repo with a docker-compose.yml based on docker-compose-portainer-template.yml and a Dockerfile based on Dockerfile-portainer-template. Replace all instances of template appropiately.

Put your vocabularies as *.ttl in the data/ directory accompanied by a *.config file. See countries.ttl.example and countries.config.example for an example. To try out the example: remove the .example suffix.

Containers

This Skosmos setup requires two containers to run (plus additionally a triple store, if that is not hosted externally).

sd-skosmos

This is the container responsible for serving the actual Skosmos application. In order for GraphDB support to work this is based on our fork of Skosmos instead of the plain version. Other than GraphDB support, this doesn't change anything.

Env var	Description
SPARQL_ENDPOINT	The SPARQL endpoint which Skosmos needs to use to connect to the triple store. Only requires read-access
DATA	The location of the 'data' folder containing the vocabulary configuration files

sd-skosmos-loader

This container is responsible for configuring and importing the used vocabularies based on the configuration files in the data directory. It runs side by side with the Skosmos container and uses cron to run the import script for updating vocabularies from an external source.

Env var	Description
SPARQL_ENDPOINT	The SPARQL endpoint which Skosmos needs to use to connect. Should be the same as for the sd-skosmos container.
DATABASE_TYPE	graphdb or fuseki
STORE_BASE	The base endpoint of the triple store. Depends on the type, for GraphDB this is the same as the SPARQL endpoint. For fuseki, include the repository name (e.g. http://fuseki-domain:3030/skosmos)
ADMIN_USERNAME	The admin username for the triple store (we need write access).
ADMIN_PASSWORD	The admin password for the triple store.
DATA	The location of the 'data' folder containing the vocabulary configuration files

To start

$ docker-compose build
$ docker-compose up -d

To stop:

$ docker-compose down

adapt config.ttl

To overwrite config-docker-compose.ttl put a config.ttl in data/. To add classifications put them in a config-ext.ttl in data/.

Add new vocabularies

In order to add a vocabulary you will need three files. The first one is {vocabulary-name}.yaml which needs to be put in the data/ directory. The {vocabulary-name}.config and {vocabulary-name}.ttl files can be in there as well, but they can also be loaded from an external source. See the example yaml files below to see how to configure them.

Configuring the vocabulary with {vocabulary}.yaml

The yaml file is used for configuring how to load the vocabulary, and whether it needs to be refreshed at a certain interval. Refreshing is mainly useful when the vocabulary is loaded from an external source.

About supported types

When importing vocabularies, keep in mind that you can import any file type which is supported by the database you use. Some file types are treated differently by different databases. An example is TriG: GraphDB puts all triples and quads from the file into the graph we tell it to use (the skosmos:sparqlGraph from the {vocab-name}.config file), while Fuseki only imports the triples that are in the default graph in the TriG file. Any quads in the file will be ignored.

Please check the behavior of your chosen database to determine which format you wish to use.

Local Files Example

config:
  type: file
  location: my-vocabulary.config

source:
  type: file
  location: my-vocabulary.ttl

Get Request Example

config:
  refresh: Yes
  refreshInterval: 1 # Hours
  type: fetch
  location: https://example.com/path/to/vocabulary.config

source:
  type: fetch
  location: https://example.com/path/to/vocabulary.ttl
  headers:
    X-Custom-Header: Set header values here

Note that both config and source support the same configuration options, except refresh and refreshInterval can only be set for config (it will affect the entire vocabulary).

Get Request Example (private GitHub/GitLab repository)

For these get requests, there are two special cases where additional authentication settings can be used. These are GitHub and GitLab, when you need to access a file in a private repository.

# Auth Settings Example
config:
  type: fetch
  location: https://your-gitlab-domain.com/organisation/repository/-/blob/main/vocabulary.config
  auth:
    type: gitlab
    token: your-access-token

source:
  type: fetch
  location: https://raw.githubusercontent.com/organisation/repository/main/vocabulary.trig
  auth:
    type: github
    token: your-access-token

The GitLab example will use the file location in the repository to fabricate a call to the GitLab API instead, as that is the only way to access private repositories. For GitHub, it's just a shortcut for setting the correct headers (make sure to use the path to the 'raw' file in this case).

SPARQL Endpoint Example

You can also load vocabularies from a sparql endpoint. For this, you will need an additional file containing the sparql query itself, which needs to be in the data/ directory. For the example, assume we have the query saved in data/vocabulary.sparql.

config:
  refresh: Yes
  refreshInterval: 1 # Hours
  type: file
  location: vocabulary.config

source:
  type: sparql
  location: https://example.com/sparql-endpoint
  query_location: vocabulary.sparql
  format: ttl # Used to specify the file extension when it's not clear from the URL

POST request example

You can also retrieve the vocabulary by posting to an endpoint, using a json body.

config:
  refresh: Yes
  refreshInterval: 1 # Hours
  type: file
  location: vocabulary.config

source:
  type: post
  location: https://example.com/some-export-endpoint
  format: trig # Used to specify the file extension when it's not clear from the URL
  body: {
    "config-a": "some value",
    "config-b": "some other value"
  }
  headers:
    Authorization: Bearer your-token-here

Database/Triple Store Types

By default, this Skosmos setup supports two triple store types: Fuseki and GraphDB. However, it is possible to extend this by adding a custom database connector to the src/database_connectors folder (see below). The type of database can be set using the DATABASE_TYPE environment variable of the sd-skosmos-loader container, and adjusting the SPARQL_ENDPOINT of both the sd-skosmos and sd-skosmos-loader containers accordingly (see the container configuration options above). Some database types may require additional configuration. The two options supported out-of-the-box will be explained below.

Configure GraphDB

For using GraphDB, set the following environment variables for the sd-skosmos container:

SPARQL_ENDPOINT=http://graphdb-location:7200/repositories/skosmos

And these for the sd-skosmos-loader:

DATABASE_TYPE=graphdb
SPARQL_ENDPOINT=http://graphdb-location:7200/repositories/skosmos
ADMIN_USERNAME=admin-username
ADMIN_PASSWORD=super-secret-password

In both cases, skosmos can be replaced with whatever you wish to call your repository.

Configure Fuseki

For using Fuseki, set the following environment variables for the sd-skosmos container:

SPARQL_ENDPOINT=http://fuseki-location:3030/skosmos/sparql

And these for the sd-skosmos-loader:

DATABASE_TYPE=fuseki
SPARQL_ENDPOINT=http://fuseki-location:3030/skosmos/sparql
STORE_BASE=http://fuseki-location:3030/skosmos
ADMIN_USERNAME=admin-username
ADMIN_PASSWORD=super-secret-password

In both cases, skosmos can be replaced with whatever you wish to call your repository.

Note that the SPARQL_ENDPOINT should be the same as the one used for the sd-skosmos container, as that is used for generating the Skosmos config. It is added for consistency with the other.

Adding another database

Database connectors can be found in src/database_connectors. The value for DATABASE_TYPE corresponds to one of the python files in this directory. In order to add support for another kind of database, you can create a new file in this directory. It should have a function called create_connector which returns an instance of a class which extends the src.database.DatabaseConnector abstract base class. The other connectors (graphdb.py and fuseki.py) can be used for reference.

The base class deals with SPARQL operations which are used for keeping track of which vocabularies are loaded and when they were last updated. These methods should not need to be changed, if the database type correctly implements SPARQL. You will need to create a setup method for creating the Skosmos repository if it doesn't exist yet, and an add_vocabulary method which deals with importing vocabularies from a file. There is a helper method sparql_http_update which can be used if the database supports the SPARQL HTTP API.

License

MIT License