ontoweaver 0.1.0

Exports from tables to Semantic Knowledge Graphs, with a simple declarative mapping.

OntoWeaver

Overview

OntoWeaver is a Python module for importing tables data in Semantic Knowledge Graphs (SKG) databases.

OntoWeaver allows to write a simple declarative mapping to express how columns from a Pandas table are to be converted as typed nodes or edges in a SKG.

It provides a simple layer of abstraction on top of Biocypher, which remains responsible for doing the ontology alignment, supporting several graph database backend, and allowing reproducible & configurable builds.

With a pure Biocypher approach, you would have to write a whole adapter by hand, with OntoWeaver, you just have to express a mapping in YAML, looking like:

row:
   map:
      columns: # Optional, you can also only write to_subject: 
        - <column_name> # which then uses indexes instead of cell values.
      to_subject: <line_node_type>
transformers:
    - map:
        columns:
            - <column_name>
        to_object: <col_node_type>
        via_relation: <edge_type>

Installation and quick setup guide

Python Module

The project uses Poetry. You can install like this:

git clone https://github.com/oncodash/ontoweaver.git
cd ontoweaver
poetry install

Poetry will create a virtual environment according to your configuration (either centrally or in the project folder). You can activate it by running poetry shell inside the project directory.

Database

Theoretically, any graph database supported by Biocypher may be used.

Graph visualization

Neo4j is a popular graph database management system that offers a flexible and efficient way to store, query, and manipulate complex, interconnected data. Cypher is the query language used to interact with Neo4j databases. In order to visualize graphs extracted from databases using OntoWeaver and BioCypher, you can download the [Neo4j Graph Database Self-Managed] (https://neo4j.com/deployment-center/) for your operating system. It has been extensively tested with the Community edition.

To create a global variable to Neo4j, add the path to neo4j-admin to PATH and PYTHONPATH. In order to use the Neo4j browser, you will need to install the correct Java version, depending on the Neo4j version you are using, and add the path to JAVA_HOME. OntoWeaver and BioCypher support versions 4 and 5 of Neo4j.

To run Neo4j (version 5+), use the command neo4j-admin server start after importing your results via the neo4j import sequence provided in the ./biocypher-out/ directory. Use neo4j-admin server stop to disconnect the local server.

Tests

Tests are located in the tests/ subdirectory and may be a good starting point to see OntoWeaver in practice. You may start with tests/test_simplest.py which shows the simplest example of mapping tabular data through BioCypher.

To run tests, use pytest:

poetry run pytest

or, alternatively:

poetry shell
pytest

Usage

OntoWeaver actually automatically provides a working adapter for BioCypher, without you having to do it.

The output of the execution of the adapter is thus what BioCypher is providing (see BioCypher's documentation). In a nutshell, the output is a script file that, when executed, will populate the configured database. By default, the output script file is saved in a subdirectory of ./biocypher-out/, which name is a timestamp from when the adapter have been executed.

To actually insert data in a SKG database, you will have to use Biocypher export API:

    import yaml
    import pandas as pd
    import biocypher
    import ontoweaver

    # Load ontology
    bc = biocypher.BioCypher(
        biocypher_config_path = "tests/simplest/biocypher_config.yaml",
        schema_config_path = "tests/simplest/schema_config.yaml"
    )

    # Load data
    table = pd.read_csv("tests/simplest/data.csv")

    # Load mapping
    with open("tests/simplest/mapping.yaml") as fd:
        mapping = yaml.full_load(fd)

    # Run the adapter
    adapter = ontoweaver.tabular.extract_all(table, mapping)

    # Write nodes
    bc.write_nodes( adapter.nodes )

    # Write edges
    bc.write_edges( adapter.edges )

    # Write import script
    bc.write_import_call()

    # Now you have a script that you can run to actually insert data.

Additionally, you will have to define a strategy for the naming of mapped items when creating nodes, by defining an affix and separator to be used during node creation. The affix used will represent the ontology type of the item in question. Unless otherwise defined, the affix defaults to suffix and separator defaults to :. This can be modified by changing the variables in the extract_all() function. Affix can be either a prefix, suffix or none - in case you decide not to include the ontology type in the node naming strategy. Special care should be exercised in case there are several types of the same name in the database. There is a possibility that nodes of the same name will be merged together during mapping, so an affix should be present. Below are some examples of node naming strategies. NAME refers to the name of the item in question in your database, and TYPE refers to the type of the item in the ontology.

...

   # Affix defaults to "suffix", and separator defaults to ":"
   # Node represented as [NAME]:[TYPE]
   adapter = ontoweaver.tabular.extract_all(table, mapping)
   
   # Node represented as [TYPE]-[NAME]
   adapter = ontoweaver.tabular.extract_all(table, mapping, affix = "prefix", separator = "-")
   
   # Node represented as [NAME] 
   adapter = ontoweaver.tabular.extract_all(table, mapping, affix = "none")

...

Mapping API

OntoWeaver essentially creates a Biocypher adapter from the description of a mapping from a table to ontology types. As such, its core input is a dictionary, that takes the form of a YAML file. This configuration file indicates:

• to which (node) type to map each line of the table,
• to which (node) type to map columns of the table,
• with which (edge) types to map relationships between nodes.

The following explanations assume that you are familiar with Biocypher's configuration, notably how it handles ontology alignment with schema configuration.

Common Mapping

The minimal configuration would be to map lines and one column, linked with a single edge type.

For example, if you have the following CSV table of phenotypes/patients:

phenotype,patient
0,A
1,B

and if you target the Biolink ontology, using a schema configuration(i.e. subset of types), defined in your shcema_config.yaml file, as below:

phenotypic feature:
    represented_as: node
    label_in_input: phenotype
case:
    represented_as: node
    label_in_input: case
case to phenotypic feature association:
    represented_as: edge
    label_in_input: case_to_phenotype
    source: phenotypic feature
    target: case

you may write the following mapping:

row:
   map:
      to_subject: phenotype
transformers:
    - map:
        columns: 
            - patient # Name of the column in the table.
        to_object: case # Node type to export to (most probably the same as in the ontology).
        via_relation: case_to_phenotype # Edge type to export to.

This configuration will end in creating a node for each phenotype, a node for each patient, and an edge for each phenotype-patient pair:

          case to phenotypic
          feature association
                    ↓
           ╭───────────────────╮
           │              ╔════╪════╗
           │              ║pati│ent ║
           │              ╠════╪════╣
╭──────────┴──────────╮   ║╭───┴───╮║
│phenotypic feature: 0│   ║│case: A│║
╰─────────────────────╯   ║╰───────╯║
                          ╠═════════╣
╭─────────────────────╮   ║╭───────╮║
│          1          │   ║│   B   │║
╰──────────┬──────────╯   ║╰───┬───╯║
           │              ╚════╪════╝
           ╰───────────────────╯

How to Add an Edge Between Column Nodes

If you need to add an edge between a column node to another (and not between the line node and a column node), you can use the from_subject predicate, for example:

row:
   map:
      to_subject: phenotype
transformers:
    - map:
        columns:
            - patient
        to_object: case
        via_relation: case_to_phenotype
    - map:
        columns:
            - disease
        from_subject: case # The edge will start from this node type...
        to_object: disease # ... to this node type.
        via_relation: disease_to_entity_association_mixin

           ╭───────────────────╮
           │              ╔════╪════╦════════════════════╗
           │              ║pati│ent ║      disease       ║
           │              ╠════╪════╬════════════════════╣
           │              ║    │    ║disease to          ║
           │              ║    │    ║entity              ║
╭──────────┴──────────╮   ║╭───┴───╮║  ↓    ╭───────────╮║
│phenotypic feature: 0│   ║│case: A├╫───────┤ disease: X│║
╰─────────────────────╯   ║╰───────╯║       ╰┬──────────╯║
                          ╠═════════╬════════╪═══════════╣
╭─────────────────────╮   ║╭───────╮║       ╭┼╌╌╌╌╌╌╌╌╌╌╮║
│          1          │   ║│   B   ├╫────────╯    X     ┆║
╰──────────┬──────────╯   ║╰───┬───╯║       ╰╌╌╌╌╌╌╌╌╌╌╌╯║
           │              ╚════╪════╩════════════════════╝
           ╰───────────────────╯

How to Add Properties to Nodes and Edges

If you do not need to create a new node, but simply attach some data to an existing node, use the to_property predicate, for example:

row:
   map:
      to_subject: phenotype
transformers:
    - map:
        columns:
            - patient
        to_object: case
        via_relation: case_to_phenotype
    - map:
        columns:
            - age
        to_property: 
            - patient_age
        for_objects:
            - case

This will add a "patient_age" property to nodes of type "case".

Note that you can add the same property to several types.

How to Use Transformers

If you want to transform a data cell before exporting it as one or several nodes, you will use transformers.

map

The mqp transformer simply extracts the value of the cell defined, and is the most common way of mapping cell values.

For ecxample:

    - map:
        columns:
            - patient
        to_object: case

split

The split transformer separates a string on a separator, into several items, and then inserts a node for each element of the list.

For example, if you have a list of treatments separated by a semicolon, you may write:

row:
   map:
      to_subject: phenotype
transformers:
    - map:
        columns:
            - variant
        to_object: variant
        via_relation: phenotype_to_variant
    - split:
        columns:
            - treatments
        from_subject: variant
        to_object: drug
        via_relation: variant_to_drug
        separator: ";"

     phenotype to variant      variant to drug
             ↓                       ↓
       ╭───────────────╮   ╭────────────────╮
       │         ╔═════╪═══╪═╦══════════════╪═════╗
       │         ║ vari│ant│ ║  treatments  │     ║
       │         ╠═════╪═══╪═╬══════════════╪═════╣
       │         ║     │   │ ║variant       │     ║
       │         ║     │   │ ║to drug       │     ║
╭──────┴─────╮   ║╭────┴───┴╮║  ↓    ╭──╮ ╭─┴────╮║
│phenotype: 0│   ║│variant:A├╫───────┤ X│;│drug:Y│║
╰────────────╯   ║╰─────────╯║       ╰┬─╯ ╰──────╯║
                 ╠═══════════╬════════╪═══════════╣
╭────────────╮   ║╭─────────╮║       ╭│ ╮ ╭──╮    ║
│      1     │   ║│    B    ├╫────────╯X ;│ Z│    ║
╰──────┬─────╯   ║╰────┬───┬╯║       ╰  ╯ ╰─┬╯    ║
       │         ╚═════╪═══╪═╩══════════════╪═════╝
       ╰───────────────╯   ╰────────────────╯

cat

The cat transformer concatenates the values cells of the defined columns and then inserts a single node. For example, the mapping below would result in the concatenation of cell values from the columns variant_id, and disease, to the node type variant. The values are concatenated in the order written in the columns section.

row:
   cat:
      columns: # List of columns whose cell values to be concatenated
        - variant_id
        - disease
      to_subject: variant # The ontology type to map to

cat_format

The user can also define the order and format of concatenation by creating a format_string field, which defines the format of the concatenation. For example:

row:
   cat_format:
      columns: # List of columns whose cell values to be concatenated
        - variant_id
        - disease
      to_subject: variant # The ontology type to map to
      format_string: "{disease}_____{variant_id}"

Although the examples above all define mapping of cell values to nodes, the transformers are also used to map cell values to properties of nodes and edges. For example:

    - map:
        columns:
            - version
        to_property:
            - version
        for_objects:
            - patient # Node type.
            - variant
            - patient_has_variant # Edge type.

Keywords Synonyms

Because several communities gathered around semantic knowledge graph, several terms can be used (more or less) interchangeably.

OntoWeaver thus allows to use your favorite vocabulary to write down the mapping configurations.

Here is the list of available synonyms:

• subject = row = entry = line = source
• columns = fields
• to_object = to_target = to_node
• from_subject = from_source
• via_relation = via_edge = via_predicate
• to_property = to_properties

How to Create User-defined Classes

Dynamic Node and Edge Types

OntoWeaver relies a lot on meta-programming, as it actually creates Python types while parsing the mapping configuration. By default, those classes are dynamically created into the ontoweaver.types module.

You may manually define your own types, derivating from ontoweaver.base.Node or ontoweaver.base.Edge.

The ontoweaver.types module automatically gathers the list of available types in the ontoweaver.types.all submodule. This allows accessing the list of node and edge types:

node_types  = types.all.nodes()
edge_types  = types.all.edges()

User-defined Adapters

You may manually define your own adapter class, inheriting from the OntoWeaver's class that manages tabular mappings.

For example:

class MYADAPTER(ontoweaver.tabular.PandasAdapter):

    def __init__(self,
        df: pd.DataFrame,
        config: dict,
        type_affix: Optional[ontoweaver.tabular.TypeAffixes] = ontoweaver.tabular.TypeAffixes.prefix,
        type_affix_sep: Optional[str] = "//",
    ):
        # Default mapping as a simple config.
        from . import types
        parser = ontoweaver.tabular.YamlParser(config, types)
        mapping = parser()

        super().__init__(
            df,
            *mapping,
        )
     

When manually defining adapter classes, be sure to define the affix type and separator you wish to use in the mapping. Unless otherwise defined, affix type defaults to suffix and separator defaults to :. In the example above, the affix type is defined as prefix and the separator is defined as //. If you wish to define affix as none, you should use type_affix: Optional[ontoweaver.tabular.TypeAffixes] = ontoweaver.tabular.TypeAffixes.none, and if you wish to define affix type as suffix, use type_affix: Optional[ontoweaver.tabular.TypeAffixes] = ontoweaver.tabular.TypeAffixes.suffix.

How to Extract Additional Edges

Edges can be extracted from the mapping configuration, by defining a from_subject and to_object in the mapping configuration, where the from_subject is the node type from which the edge will start, and the to_object is the node type to which the edge will end.

For example, consider the following mapping configuration for the sample dataset below:

id	patient	        sample
0	patient1	sample1
1	patient2	sample2
2	patient3	sample3
3	patient4	sample4

row:
    map:
        columns:
            - id
        to_subject: variant
transformers:
    - map:
          columns:
              - patient
          to_object: patient
          via_relation: patient_has_variant
    - map:
          columns:
              - sample
          to_object: sample
          via_relation: variant_in_sample

If the user would like to extract an additional edge from the node type patient to the node type sample, they would need to add the following section to the transformers in the mapping configuration:

    - map:
        columns:
          - patient
        from_subject: sample
        to_object: patient
        via_relation: sample_to_patient