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A high-throughput ontology-based pipeline for data integration

A flexible, scalable pipeline for integration and alignment of multiple data sources. The code is written to be adaptable to all kinds of data, ontologies (OWL), or reasoning profiles, and output is compatible with any type of storage technology.

Applications developed to use ontology-data-pipeline

A good way to start with the ontology-data-pipeline is to fork or clone one of the applications which use this code. This includes:

Quick Start

Step 1: Install docker

Step 2: Run the application. On the commandline, you can execute the script like so:

# make sure you have the latest docker container
docker pull jdeck88/ontology-data-pipeline
# run the pipeline help in the docker container
docker run -v "$(pwd)":/process -w=/app -ti jdeck88/ontology-data-pipeline python pipeline.py -h 

usage: pipeline.py [-h] [--drop_invalid] [--log_file]
                   [--reasoner_config REASONER_CONFIG] [-v] [-c CHUNK_SIZE]
                   [--num_processes NUM_PROCESSES]
                   data_file output_dir ontology config_dir

ontology data pipeline command line application.

positional arguments:
  data_file             Specify the data file to load.
  output_dir            path of the directory to place the processed data
  ontology              specify a filepath/url of the ontology to use for
                        reasoning/triplifying
  config_dir            Specify the path of the directory containing the
                        configuration files.

optional arguments:
  -h, --help            show this help message and exit
  --drop_invalid        Drop any data that does not pass validation, log the
                        results, and continue the process
  --log_file            log all output to a log.txt file in the output_dir.
                        default is to log output to the console
  --reasoner_config REASONER_CONFIG
                        optionally specify the reasoner configuration file.
                        Default is to look for reasoner.config in the
                        configuration directory
  -v, --verbose         verbose logging output
  -c CHUNK_SIZE, --chunk_size CHUNK_SIZE
                        chunk size to use when processing data. optimal
                        chunk_size for datasets with less then 200000
                        recordscan be determined with: num_records / num_cpus
  --num_processes NUM_PROCESSES
                        number of process to use for parallel processing of
                        data. Defaults to cpu_count of the machine

As an alternative to the commandline, params can be placed in a file, one per
line, and specified on the commandline like 'pipeline.py @params.conf'.

Configuring Your Environment

Ontology Data Pipeline Diagram

The ontology-data-pipeline operates on a set of configuration files, which you can specify in the configuration directory.

The following text describes the operation of the pipeline and the steps involved.

  1. Triplifier

    This step provides provides basic data validation and generates the RDF triples, assuming validation passes, needed for the reasoning phase. Each project will need to contain a config directory with the following files that will be used to triplify the preprocessed data:

    NOTE: Wherever there is a uri expressed in any of the following files, you have the option of using ontology label substitution. If the uri is of the format {label name here}, the appropriate uri will be substituted from the provided ontology. See the ROBOT for details term identifier abbreviations.

    1. entity.csv
    2. mapping.csv
    3. relations.csv
    4. rules.csv
  2. Reasoning

    This step uses the ROBOT project to perform reasoning on the triplified data in the triplifier step, in conjunction with logic contained in the provided ontology. An example of the use of the ROBOT file is given below, calling the robot.jar which is in the /lib directory. Please note that the pipeline code calls this command for you and normally you do not need to call this command directly. Here we are illustrating a direct-use of the robot command if you want to explore how this works within the ontology-data-pipeline environment.

 java -jar lib/robot.jar reason -r elk \
    --axiom-generators "InverseObjectProperties ClassAssertion" \
    -i sample_data/unreasoned_data.ttl \
    --include-indirect true \
    --exclude-tautologies structural \
    reduce \
    -o sample_data/reasoned_data.ttl

In the above example, the reasoner use is ELK, with InverseObjectProperties and ClassAssertion axioms specified. The input file is sample_data/unreasoned_data.ttl. We tell the reasoning engine to include indirect inferences, which lets us assert recursive SubClass relationships. Exclude tautoligies tells the reasoner to not include assertions which will always be true. The reduce command eliminates redundant assertions. Finally, the output file is sample_data/reasoned_data.ttl. The most critical step of the reasoning process is including the indirect inferences: this is the step that lets us export our end-results to a simple Document Store and not rely on "smart" applications which are able to iterate recursive relationships. Examples included in the test directory.

  1. Data Formatting

    This step takes a custom SPARQL query and generates csv files for each file outputted in the Reasoning step using ROBOT. You must write a SPARQL query yourself to format data as you wish it to appear. You can refer to the example below for a query that turns the sample input (reasoned) file into CSV. If no sparql query is found, then this step is skipped. An example of the data Formatting step is given below calling the robot.jar which is in the /lib directory. Please note that the pipeline code calls this command for you and normally you do not need to call this command directly. Here we are illustrating a direct-use of the robot command if you want to explore how this works within the ontology-data-pipeline environment.

java -jar lib/robot.jar query --input sample_data/reasoned_data.ttl \
    --query sample_data/fetch_reasoned.sparql \
    sample_data/reasoned_data.ttl.csv

In the above example, we call the ROBOT query sub-command and give an input file of sample_data/reasoned_data.ttl (this is the output of the above command), and tell it to use the sample_data/fetch_reasoned.sparql sparql command as a guide to produce the output file sample_data/reasoned_data.ttl.csv.

Config Files

Project configuration files include entity.csv, mapping.csv, relations.csv, and any files defining controlled vocabularies that we want to map rdf:types to. The remaining configuration files below are found in the config directory. Together, these are the required configuration files we use for reasoning against the application ontology (e.g. Plant Phenology Ontology). These files configure the data validation, triplifying, reasoning, and rdf2csv converting.

The following files are required:

  1. entity.csv (found in each project directory) - This file specifies the entities (instances of classes) to create when triplifying. The file expects the following columns:

    • alias

      The name used to refer to the entity. This is usually a shortened version of the class label.

    • concept_uri

      The uri which defines this entity (class).

    • unique_key

      The column name that is used to uniquely identify the entity. Whenever there is a unique value for the property specified by "unique key", a new instance will be created. e.g. "record_id"

    • identifier_root

      The identifier root for each unique entity (instance created). E.g. urn:observingprocess/ would be the root of urn:observingprocess/record1

  1. mapping.csv (found in each project directory)

    • column

      The name of the column in the csv file to be used for triplifying

    • uri

      The uri which defines this column. These generally are data properties.

    • entity_alias

      The alias of the entity (from entity.csv) this column is a property of

  1. relations.csv (found in each project directory)

    • subject_entity_alias

      The alias of the entity which is the subject of this relationship

    • predicate

      The uri which defines the relationship

    • object_entity_alias

      The alias of the entity which is the object of this relationship

The following files are optional:

  1. rules.csv - This file is used to setup basic validation rules for the data. The file expects the following columns:

    • rule

      The name of the validation rule to apply. See rule types below. Note: a default ControlledVocabulary rule will be applied to the phenophase_name column for the names found in the phenophase_descriptions.csv file

    • columns

      Pipe | delimited list of columns to apply the rule to

    • level

      Either WARNING or ERROR. ERROR will terminate the program after validation. WARNINGS will be logged. Case-Insensitive. Defaults to WARNING

    • list

      Only applicable for ControlledVocabulary rules. This refers to the name of the file that contains the list of the controlled vocab

    Rule Types
    • RequiredValue - Specifies columns which can not be empty
    • UniqueValue - Checks that the values in a column are unique
    • ControlledVocabulary - Checks columns against a list of controlled vocabulary. The name of the list is specified in the list column in rules.csv
    • Integer - Checks that all values are integers. Will coerce values to integers if possible
    • Float - Checks that all values are floating point numbers (ex. 1.00). Will coerce values to floats if possible
  2. Any file specified in rules.csv list column is required. The file expects the following columns:

    • field - Specifies a valid value. This is the values expected in the input data file
    • defined_by - Optional value which will replace the field when writing triples
  3. fetch_reasoned.sparql - Sparql query used to convert reasoned data to csv

Developers

The ontology-data-pipeline is designed to be run as a Docker container. However, you can also run the codebase from sources by checking out this repository and following the instructions at python instructions. Information on building the docker container is contained at docker instructions.

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