Meta Knowledge Graph

The Meta Knowledge Graph operation takes an instance of kgx.graph.base_graph.BaseGraph and generates Translator API (TRAPI) Release 1.1 compatible knowledge map for the entire graph.

This operation generates graph summary as a JSON (or YAML) in a format that is compatible with the content metadata standards of the Knowledge Graph Exchange (KGE) Archive.

The main entry point is the kgx.graph_operations.meta_knowledge_graph.generate_meta_knowledge_graph method.

The tool does detect and logs anomalies in the graph (defaults reporting to stderr, but may be reset to a file using the error_log parameter)

Note: To generate a summary statistics YAML that is compatible with Knowledge Graph Hub dashboard, refer to Summarize Graph operation.

Streaming Data Processing Mode

For very large graphs, the Meta Knowledge Graph operation now successfully processes graph data using data streaming (command flag --stream=True) which significantly minimizes the memory footprint required to process such graphs.

Provenance Statistics

The Meta Knowledge Graph operation can count numbers of nodes and edges by Biolink 2.0 biolink:knowledge_source provenance (and related is_a descendant slot terms). The node_facet_properties and edge_facet_properties CLI (and code method) arguments need to be explicitly set to specify which provenance slot names are to be counted in a given graph (by default, provided_by slots used for nodes and knowledge_source slots used for edges).

kgx.graph_operations.meta_knowledge_graph

class kgx.graph_operations.meta_knowledge_graph.MetaKnowledgeGraph(name='', node_facet_properties: Optional[List] = None, edge_facet_properties: Optional[List] = None, progress_monitor: Optional[Callable[[kgx.utils.kgx_utils.GraphEntityType, List], None]] = None, error_log=None, **kwargs)[source]

Bases: object

Class for generating a TRAPI 1.1 style of “meta knowledge graph” summary.

The optional ‘progress_monitor’ for the validator should be a lightweight Callable which is injected into the class ‘inspector’ Callable, designed to intercepts node and edge records streaming through the Validator (inside a Transformer.process() call. The first (GraphEntityType) argument of the Callable tags the record as a NODE or an EDGE. The second argument given to the Callable is the current record itself. This Callable is strictly meant to be procedural and should not mutate the record. The intent of this Callable is to provide a hook to KGX applications wanting the namesake function of passively monitoring the graph data stream. As such, the Callable could simply tally up the number of times it is called with a NODE or an EDGE, then provide a suitable (quick!) report of that count back to the KGX application. The Callable (function/callable class) should not modify the record and should be of low complexity, so as not to introduce a large computational overhead to validation!

class Category(category_curie: str, mkg)[source]

Bases: object

Internal class for compiling statistics about a distinct category.

__init__(category_curie: str, mkg)[source]

MetaKnowledgeGraph.Category constructor.

category_curie: str

Biolink Model category CURIE identifier.

analyse_node_category(n, data) → None[source]

Analyse metadata of a given graph node record of this category.

Parameters

  • n (str) – Curie identifier of the node record (not used here).

  • data (Dict) – Complete data dictionary of node record fields.

classmethod get_category_curie_from_index(cid: int) → str[source]
Parameters

cid (int) – Internal MetaKnowledgeGraph index id for tracking a Category.

Returns

Curie identifier of the Category.

Return type

str

get_cid()[source]
Returns

Internal MetaKnowledgeGraph index id for tracking a Category.

Return type

int

get_count() → int[source]
Returns

Count of nodes which have this category.

Return type

int

get_count_by_source(facet: str = 'provided_by', source: str = None) → Dict[str, Any][source]
Parameters

  • facet (str) – Facet tag (default, ‘provided_by’) from which the count should be returned

  • source (str) – Source name about which the count is desired.

Returns

Count of nodes, by node ‘provided_by’ knowledge source, for a given category. Returns dictionary of all source counts, if input ‘source’ argument is not specified.

Return type

Dict

get_id_prefixes() → Set[str][source]
Returns

Set of identifier prefix (strings) used by nodes of this Category.

Return type

Set[str]

get_name() → str[source]
Returns

CURIE name of the category.

Return type

str

json_object()[source]
Returns

Returns JSON friendly metadata for this category.,

Return type

Dict[str, Any]

__call__(entity_type: kgx.utils.kgx_utils.GraphEntityType, rec: List)[source]

Transformer ‘inspector’ Callable, for analysing a stream of graph data.

Parameters

  • entity_type (GraphEntityType) – indicates what kind of record being passed to the function for analysis.

  • rec (Dict) – Complete data dictionary of the given record.

__init__(name='', node_facet_properties: Optional[List] = None, edge_facet_properties: Optional[List] = None, progress_monitor: Optional[Callable[[kgx.utils.kgx_utils.GraphEntityType, List], None]] = None, error_log=None, **kwargs)[source]

MetaKnowledgeGraph constructor.

Parameters

  • name (str) – (Graph) name assigned to the summary.

  • node_facet_properties (Optional[List]) – A list of node properties (e.g. knowledge_source tags) to facet on. For example, ['provided_by']

  • edge_facet_properties (Optional[List]) – A list of edge properties (e.g. knowledge_source tags) to facet on. For example, ['original_knowledge_source', 'aggregator_knowledge_source']

  • progress_monitor (Optional[Callable[[GraphEntityType, List], None]]) – Function given a peek at the current record being stream processed by the class wrapped Callable.

  • error_log – Where to write any graph processing error message (stderr, by default).

analyse_edge(u, v, k, data) → None[source]

Analyse metadata of one graph edge record. :param u: Subject node curie identifier of the edge. :type u: str :param v: Subject node curie identifier of the edge. :type v: str :param k: Key identifier of the edge record (not used here). :type k: str :param data: Complete data dictionary of edge record fields. :type data: Dict

analyse_node(n: str, data: Dict) → None[source]

Analyse metadata of one graph node record.

Parameters

  • n (str) – Curie identifier of the node record (not used here).

  • data (Dict) – Complete data dictionary of node record fields.

get_category(category_curie: str) → kgx.graph_operations.meta_knowledge_graph.MetaKnowledgeGraph.Category[source]

Counts the number of distinct (Biolink) categories encountered in the knowledge graph (not including those of ‘unknown’ category)

Parameters

category_curie (str) – Curie identifier for the (Biolink) category.

Returns

MetaKnowledgeGraph.Category object for a given Biolink category.

Return type

Category

get_edge_count_by_predicate(predicate_curie: str) → int[source]

Counts the number of edges in the graph with the specified predicate.

Parameters

predicate_curie (str) – (Biolink) curie identifier for the predicate.

Returns

Number of edges for the given predicate.

Return type

int

Raises

RuntimeError – Error if predicate identifier is empty string or None.

get_edge_count_by_source(subject_category: str, predicate: str, object_category: str, facet: str = 'knowledge_source', source: Optional[str] = None) → Dict[str, Any][source]

Returns count by source for one S-P-O triple (S, O being Biolink categories; P, a Biolink predicate)

get_edge_mapping_count() → int[source]

Counts the number of distinct edge Subject (category) - P (predicate) -> Object (category) mappings in the knowledge graph.

Returns

Count of subject(category) - predicate -> object(category) mappings in the graph.

Return type

int

get_edge_stats() → List[Dict[str, Any]][source]
Returns

Knowledge map for the list of edges in the graph.

Return type

List[Dict[str, Any]]

get_graph_summary(name: str = None, **kwargs) → Dict[source]

Similar to summarize_graph except that the node and edge statistics are already captured in the MetaKnowledgeGraph class instance (perhaps by Transformer.process() stream inspection) and therefore, the data structure simply needs to be ‘finalized’ for saving or similar use.

Parameters

  • name (Optional[str]) – Name for the graph (if being renamed)

  • kwargs (Dict) – Any additional arguments (ignored in this method at present)

Returns

A TRAPI 1.1 compliant meta knowledge graph of the knowledge graph returned as a dictionary.

Return type

Dict

get_name() → str[source]
Returns

Currently assigned knowledge graph name.

Return type

str

get_node_count_by_category(category_curie: str) → int[source]

Counts the number of edges in the graph with the specified (Biolink) category curie.

Parameters

category_curie (str) – Curie identifier for the (Biolink) category.

Returns

Number of nodes for the given category.

Return type

int

Raises

RuntimeError – Error if category identifier is empty string or None.

get_node_stats() → Dict[str, kgx.graph_operations.meta_knowledge_graph.MetaKnowledgeGraph.Category][source]
Returns

Statistics for the nodes in the graph.

Return type

Dict[str, Category]

get_number_of_categories() → int[source]

Counts the number of distinct (Biolink) categories encountered in the knowledge graph (not including those of ‘unknown’ category)

Returns

Number of distinct (Biolink) categories found in the graph (excluding nodes with ‘unknown’ category)

Return type

int

get_predicate_count() → int[source]

Counts the number of distinct edge predicates in the knowledge graph.

Returns

Number of distinct (Biolink) predicates in the graph.

Return type

int

get_total_edge_counts_across_mappings() → int[source]

Aggregate count of the edges in the graph for every mapping. Edges with subject and object nodes with multiple assigned categories will have their count replicated under each distinct mapping of its categories.

Returns

Number of the edges counted across all mappings.

Return type

int

get_total_edges_count() → int[source]

Gets the total number of ‘valid’ edges in the data set (ignoring those with ‘unknown’ subject or predicate category mappings)

Returns

Total count of edges in the graph.

Return type

int

get_total_node_counts_across_categories() → int[source]

The aggregate count of all node to category mappings for every category. Note that nodes with multiple categories will have their count replicated under each of its categories.

Returns

Total count of node to category mappings for the graph.

Return type

int

get_total_nodes_count() → int[source]

Counts the total number of distinct nodes in the knowledge graph (not including those ignored due to being of ‘unknown’ category)

Returns

Number of distinct nodes in the knowledge.

Return type

int

save(file, name: str = None, file_format: str = 'json') → None[source]

Save the current MetaKnowledgeGraph to a specified (open) file (device).

Parameters

  • file (File) – Text file handler open for writing.

  • name (str) – Optional string to which to (re-)name the graph.

  • file_format (str) – Text output format (‘json’ or ‘yaml’) for the saved meta knowledge graph (default: ‘json’)

Returns

Return type

None

summarize_graph(graph: kgx.graph.base_graph.BaseGraph, name: str = None, **kwargs) → Dict[source]

Generate a meta knowledge graph that describes the composition of the graph.

Parameters

  • graph (kgx.graph.base_graph.BaseGraph) – The graph

  • name (Optional[str]) – Name for the graph

  • kwargs (Dict) – Any additional arguments (ignored in this method at present)

Returns

A TRAPI 1.1 compliant meta knowledge graph of the knowledge graph returned as a dictionary.

Return type

Dict

summarize_graph_edges(graph: kgx.graph.base_graph.BaseGraph) → List[Dict][source]

Summarize the edges in a graph.

Parameters

graph (kgx.graph.base_graph.BaseGraph) – The graph

Returns

The edge stats

Return type

List[Dict]

summarize_graph_nodes(graph: kgx.graph.base_graph.BaseGraph) → Dict[source]

Summarize the nodes in a graph.

Parameters

graph (kgx.graph.base_graph.BaseGraph) – The graph

Returns

The node stats

Return type

Dict

kgx.graph_operations.meta_knowledge_graph.generate_meta_knowledge_graph(graph: kgx.graph.base_graph.BaseGraph, name: str, filename: str) → None[source]

Generate a knowledge map that describes the composition of the graph and write to filename.

Parameters

  • graph (kgx.graph.base_graph.BaseGraph) – The graph

  • name (Optional[str]) – Name for the graph

  • filename (str) – The file to write the knowledge map to

kgx.graph_operations.meta_knowledge_graph.mkg_default(o)[source]

JSONEncoder ‘default’ function override to properly serialize ‘Set’ objects (into ‘List’)

kgx.graph_operations.meta_knowledge_graph.summarize_graph(graph: kgx.graph.base_graph.BaseGraph, name: str = None, **kwargs) → Dict[source]

Generate a meta knowledge graph that describes the composition of the graph.

Parameters
Returns

A TRAPI 1.1 compliant meta knowledge graph of the knowledge graph returned as a dictionary.

Return type

Dict