Software and Dataset mention recognizer client

Simple Python client for using the Softcite software mention recognition service and the DataStet dataset mention recognition service. It can be applied to:

• individual PDF files

• recursively to a local directory, processing all the encountered PDF

• to a collection of documents harvested by biblio-glutton-harvester and article-dataset-builder, with the benefit of re-using the collection manifest for injectng metadata and keeping track of progress. The collection can be stored locally or on a S3 storage.

The client can call either one of the two services or both, parallelizing queries efficiently for individual or combined services.

Requirements

The client has been tested with Python 3.6-3.8.

The client requires a working Softcite software mention recognition service and/or a working Datastet dataStet mention recognition service. Service host and port can be changed in the config.json file of the client.

The easiest is to use docker images for running these services:

• Softcite software mention recognition service:

• DataStet dataset mention recognition service:

For acceptable performance, these two services must typically be deployed on two different servers. For good performance, GPU are required to boost the usage of the different involved Deep Learning models.

Install

> git clone https://github.com/softcite/softdata_mentions_client.git
> cd softdata_mentions_client/

It is advised to setup first a virtual environment to avoid falling into one of these gloomy python dependency marshlands:

> virtualenv --system-site-packages -p python3 env

> source env/bin/activate

Install the dependencies, use:

> pip3 install -r requirements.txt

Finally install the project in editable state

> pip3 install -e .

Usage and options

usage: client.py [-h] [--repo-in REPO_IN] [--file-in FILE_IN] [--file-out FILE_OUT]
                 [--data-path DATA_PATH] [--config CONFIG] [--reprocess] [--reset] [--load]
                 [--diagnostic] [--scorched-earth]
                 target

Software and Dataset mention recognizer client for Softcite and Datastet services

positional arguments:
  target                one of [software, dataset, all], mandatory

optional arguments:
  -h, --help            show this help message and exit
  --repo-in REPO_IN     path to a directory of PDF files to be processed by the Softcite
                        software mention recognizer
  --file-in FILE_IN     a single PDF input file to be processed by the Softcite software
                        mention recognizer
  --file-out FILE_OUT   path to a single output the software mentions in JSON format, extracted
                        from the PDF file-in
  --data-path DATA_PATH
                        path to the resource files created/harvested by biblio-glutton-
                        harvester
  --config CONFIG       path to the config file, default is ./config.json
  --reprocess           reprocessed failed PDF
  --reset               ignore previous processing states and re-init the annotation process
                        from the beginning
  --load                load json files into the MongoDB instance, the --repo-in or --data-path
                        parameter must indicate the path to the directory of resulting json
                        files to be loaded, --dump must indicate the path to the json dump file
                        of document metadata
  --diagnostic          perform a full count of annotations and diagnostic using MongoDB
                        regarding the harvesting and transformation process
  --scorched-earth      remove a PDF file after its sucessful processing in order to save
                        storage space, careful with this!

The logs are written by default in a file ./client.log, but the location of the logs can be changed in the configuration file (default ./config.json).

Processing local PDF files

For processing a single file for both software and dataset mentions, the resulting json being written as file at the indicated output path:

python3 softdata_mentions_client/client.py all --file-in toto.pdf --file-out toto.json

For processing recursively a directory of PDF files, the results will be:

• written to a mongodb server and database indicated in the config file

• and in the directory of PDF files, as json files, together with each processed PDF

python3 softdata_mentions_client/client.py all --repo-in /mnt/data/biblio/pmc_oa_dir/

The default config file is ./config.json, but could also be specified via the parameter --config:

python3 softdata_mentions_client/client.py all --repo-in /mnt/data/biblio/pmc_oa_dir/ --config ./my_config.json

To process document for only software mentions:

python3 softdata_mentions_client/client.py software --file-in toto.pdf --file-out toto.json

and for only dataset mentions:

python3 softdata_mentions_client/client.py dataset --file-in toto.pdf --file-out toto.json

Processing a collection of PDF harvested by biblio-glutton-harvester

biblio-glutton-harvester and article-dataset-builder creates a collection manifest as a LMDB database to keep track of the harvesting of large collection of files. Storage of the resource can be located on a local file system or on a AWS S3 storage. The software-mention client will use the collection manifest to process these harvested documents.

• locally:

python3 softdata_mentions_client/client.py all --data-path /mnt/data/biblio-glutton-harvester/data/

--data-path indicates the path to the repository of data harvested by biblio-glutton-harvester.

The resulting JSON files will be enriched by the metadata records of the processed PDF and will be stored together with each processed PDF in the data repository.

If the harvested collection is located on a S3 storage, the access information must be indicated in the configuration file of the client config.json. The extracted software mention will be written in a file with extension .software.json and the extracted dataset mentions in a file with extension .dataset.json , for example:

-rw-rw-r-- 1 lopez lopez 1.1M Aug  8 03:26 0100a44b-6f3f-4cf7-86f9-8ef5e8401567.pdf
-rw-rw-r-- 1 lopez lopez  485 Aug  8 03:41 0100a44b-6f3f-4cf7-86f9-8ef5e8401567.software.json
-rw-rw-r-- 1 lopez lopez  485 Aug  8 03:41 0100a44b-6f3f-4cf7-86f9-8ef5e8401567.dataset.json

If a MongoDB server access information is indicated in the configuration file config.json, the extracted information will additionally be written in MongoDB.

License and contact

Distributed under Apache 2.0 license. The dependencies used in the project are either themselves also distributed under Apache 2.0 license or distributed under a compatible license.

If you contribute to this project, you agree to share your contribution following these licenses.

Main author and contact: Patrice Lopez (patrice.lopez@science-miner.com)