Hydra Scraper

Comprehensive scraper for paginated APIs, RDF, XML, file dumps, and Beacon files

This scraper provides a command-line toolset to pull data from various sources, such as Hydra paginated schema.org APIs, Beacon files, or file dumps. The tool differentiates between feeds and their elements in RDF-compatible formats such as JSON-LD or Turtle, but it can also handle XML files using, for example, the LIDO schema. Command-line calls can be combined and adapted to build fully-fledged scraping mechanisms, including the ability to output a set of triples. The script was originally developed as an API testing tool for of the Corpus Vitrearum Germany (CVMA) at the Academy of Sciences and Literature Mainz. It was later expanded for use in the Culture Knowledge Graph at NFDI4Culture around the Culture Graph Interchange Format (CGIF) and the NFDIcore ontology with the CTO module.

Licence

Written and maintained by Jonatan Jalle Steller.

This code is covered by the MIT licence.

Workflows

Installation

There are two options to run this script: as a regular Python command-line tool or in an OCI container (Podman/Docker). For the regular install, clone this repository (e.g. git clone https://github.com/digicademy/hydra-scraper.git or the SSH equivalent). Then open a terminal in the resulting folder and run pip install -r requirements.txt to install the dependencies. To use the script, use the commands listed under "Examples" below.

To run the container instead, which brings along all required Python tooling, clone this repo and open a terminal in the resulting folder. In the command examples below, replace python go.py with podman compose run hydra-scraper.

Usage

The scraper is a command-line tool. Use these main configuration options to indicate what kind of scraping run you desire.

• -l or --location <url or folder or file>: source URI, folder, or file path
• -f or --feed <value>: type of feed or starting point for the scraping run:
  • beacon: a local or remote text file listing one URI per line (Beacon)
  • cmif: a local or remote CMIF file
  • folder: a local folder or a local/remote ZIP archive of individual files
  • schema: an RDF-based, optionally Hydra-paginated schema.org API or embedded metadata (CGIF)
  • schema-list: same as above, but using the triples in individual schema.org files
• -e or --elements <value>: element markup to extract data from during the scraping run (leave out to not extract data):
  • lido: use LIDO files
  • schema: use RDF triples in a schema.org format (CGIF)
• -o or --output <value> <value>: outputs to produce in the scraping run:
  • beacon: a text file listing one URI per line
  • csv: a CSV table of data
  • cto: NFDI4Culture-style triples
  • files: the original files
  • triples: the original triples

In addition, and depending on the main config, you can specify these additional options:

• -n or --name <string>: name of the subfolder to download data to
• -d or --dialect <string>: content type to use for requests
• -i or --include <string>: filter for feed element URIs to include
• -r or --replace <string>: string to replace in feed element URIs
• -rw or --replace_with <string>: string to replace the previous one with
• -a or --append <string>: addition to the end of each feed element URI
• -af or -add_feed <uri>: URI of a data feed to bind members to
• -ac or -add_catalog <uri>: URI of a data catalog the data feed belongs to
• -ap or -add_publisher <uri>: URI of the data publisher
• -c or --clean <string> <string>: strings to remove from feed element URIs to build their file names
• -p or --prepare <string> <string>: prepare cto output for this NFDI4Culture feed and catalog ID
• -q or --quiet: do not display status messages

Examples

The commands listed below illustrate possible command-line arguments. They refer to specific projects that use this scraper, but the commands should work with any other page using the indicated formats. If you run the container, replace python go.py with podman compose run hydra-scraper. You may need to use docker instead of podman, or python3 instead of python.

NFDI4Culture

Original triples from the Culture Information Portal:

python go.py -l https://nfdi4culture.de/resource.ttl -f schema-list -o triples -n n4c-portal

NFDIcore/CTO triples from a local or remote CGIF/schema.org feed (embedded):

python go.py -l https://corpusvitrearum.de/cvma-digital/bildarchiv.html -f schema -e schema -o cto -n n4c-cgif -p E5308 E4229

NFDIcore/CTO triples from a local or remote CGIF/schema.org feed (API):

python go.py -l https://gn.biblhertz.it/fotothek/seo -f schema -e schema -o cto -n n4c-cgif-api -p E6064 E4244

NFDIcore/CTO triples from a local or remote Beacon-like feed of CGIF/schema.org files:

python go.py -l downloads/n4c-cgif/beacon.txt -f beacon -e schema -o cto -n n4c-cgif-beacon -a /about.cgif -p E5308 E4229

NFDIcore/CTO triples from a local or remote ZIP file containing CGIF/schema.org files:

python go.py -l downloads/n4c-cgif.zip -f folder -e schema -o cto -n n4c-cgif-zip -af https://corpusvitrearum.de/cvma-digital/bildarchiv.html -p E5308 E4229

NFDIcore/CTO triples from a local folder containing CGIF/schema.org files:

python go.py -l downloads/n4c-cgif/files -f folder -e schema -o cto -n n4c-cgif-folder -p E5308 E4229

NFDIcore/CTO triples from a local or remote Beacon-like feed of LIDO files (feed URI added because it is not in the data):

python go.py -l downloads/n4c-cgif/beacon.txt -f beacon -e lido -o cto -n n4c-lido -a /about.lido -af https://corpusvitrearum.de/cvma-digital/bildarchiv.html -p E5308 E4229

NFDIcore/CTO triples from a local or remote ZIP file containing LIDO files:

python go.py -l downloads/n4c-lido.zip -f folder -e lido -o cto -n n4c-lido-zip -af https://corpusvitrearum.de/cvma-digital/bildarchiv.html -p E5308 E4229

NFDIcore/CTO triples from a local folder containing LIDO files:

python go.py -l downloads/n4c-lido/files -f folder -e lido -o cto -n n4c-lido-folder -af https://corpusvitrearum.de/cvma-digital/bildarchiv.html -p E5308 E4229

Corpus Vitrearum Germany

Files and triples from JSON-LD data:

python go.py -l https://corpusvitrearum.de/id/about.json -f schema-list -o files triples -n cvma-jsonld -i https://corpusvitrearum.de/id/F -c https://corpusvitrearum.de/id/ /about.json

Files and triples from RDF/XML data:

python go.py -l https://corpusvitrearum.de/id/about.rdf -f schema-list -o files triples -n cvma-rdfxml -i https://corpusvitrearum.de/id/F -c https://corpusvitrearum.de/id/ /about.rdf

Files and triples from Turtle data:

python go.py -l https://corpusvitrearum.de/id/about.ttl -f schema-list -o files triples -n cvma-turtle -i https://corpusvitrearum.de/id/F -c https://corpusvitrearum.de/id/ /about.ttl

Beacon, CSV table, NFDIcore/CTO, files, and triples from CGIF/schema.org (embedded) data:

python go.py -l https://corpusvitrearum.de/cvma-digital/bildarchiv.html -f schema -e schema -o beacon csv cto files triples -n cvma-cgif -p E5308E4229  -c https://corpusvitrearum.de/id/

Beacon, CSV table, NFDIcore/CTO, files, and triples from CGIF/schema.org (API) data:

python go.py -l https://corpusvitrearum.de/id/about.cgif -f schema -e schema -o beacon csv cto files triples -n cvma-cgif-api -p E5308 E4229

Beacon, CSV table, NFDIcore/CTO, and files from LIDO data:

python go.py -l https://corpusvitrearum.de/cvma-digital/bildarchiv.html -f schema-list -e lido -o beacon csv cto files -n cvma-lido -a /about.lido -c https://corpusvitrearum.de/id/ /about.lido

Contributing

The file go.py executes a regular scraping run via several base modules that can also be used independently:

1. organise provides the Organise object to collect and clean configuration info. It also creates the required folders, sets up logging, and uses an additional Progress object to show progress messages to users.
2. job provides the Job object to orchestrate a single scraping run. It contains the feed pagination and data collation logic.
3. file provides the File object to retrieve a remote or local file. It also contains logic to identify file types and parse RDF or XML.
4. data provides the data storage objects Uri, UriList, Label, LabelList, UriLabel, UriLabelList, Date, DateList, and Incipit. They include data serialisation logic and namespace normalization.
5. extract is a special module to provide ExtractFeedInterface and ExtractFeedElementInterface. These include generic functions to extract XML or RDF data.
6. map is another special module to provide MapFeedInterface and MapFeedElementInterface. These include generic functions to generate text content or RDF triples.
7. lookup provides a Lookup object as well as type lists for authority files. These can be used to identify whether a URI refers to a person, an organisation, a location, an event, or something else.

Two additional sets of classes use the extract and map interfaces to provide extraction and mapping routines for particular formats. These routines provide a Feed and/or a FeedElement object depending on what the format provides. These format-specific objects are called from the Job object listed above.

If you change the code, please remember to document each object/function and walk other users or maintainers through significant steps. This package is governed by the Contributor Covenant code of conduct. Please keep this in mind in all interactions.

Releasing

Before you make a new release, make sure the following files are up to date:

• base/file.py: version number in user agent
• CHANGELOG.md: version number and changes
• CITATION.cff: version number, authors, and release date
• LICENCE.txt: release date
• requirements.txt: list of required libraries
• setup.py: version number and authors

Use GitHub to make the release. Use semantic versioning.

Roadmap

• Implement job presets/collections
• Convert test.py to something more sophisticated
• Automatically build OCI container via CI instead of on demand
• Add TEI ingest support based on Gregorovius
• Add OAI-PMH ingest support
• Add MEI ingest support
• Add DCAT serialisation

Further ideas

• Add support for ingesting CSV/JSON data
• Use the system's download folder instead of downloads and unpack to be able to distribute the package
• Fix setup file and release the package on PyPI
• Find a lightweight way to periodically update the RDF class lists