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#

Translator Reasoner API ‘meta-knowledge-graph’ endpoint analogous graph summary module.

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[[GraphEntityType, List], None]] = None, error_log=None, **kwargs)[source]#

Bases: ErrorDetecting

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.

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: Optional[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]

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.

clear_errors()#

Clears the current error log list

get_category(category_curie: str) → 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_errors(level: Optional[str] = None) → Dict#

Get the index list of distinct error messages.

Parameters:

level (str) – Optional filter (case insensitive) name of error message level (generally either “Error” or “Warning”)

Returns:

A raw dictionary of entities indexed by [message_level][error_type][message] or only just [error_type][message] specific to a given message level if the optional level filter is given

Return type:

Dict

static get_facet_counts(facets: Optional[List], counts_by_source: Dict, data: Dict)[source]#

Get node or edge facet counts

get_graph_summary(name: Optional[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, Dict][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

log_error(entity: str, error_type: ErrorType, message: str, message_level: MessageLevel = MessageLevel.ERROR)#

Log an error to the list of such errors.

Parameters:

  • entity – source of parse error

  • error_type – ValidationError ErrorType,

  • message – message string describing the error

  • message_level – ValidationError MessageLevel

save(file, name: Optional[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’)

Return type:

None

summarize_graph(graph: BaseGraph, name: Optional[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: 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: 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

write_report(outstream: Optional[TextIO] = None, level: Optional[str] = None) → None#

Write error get_errors to a file

Parameters:

  • outstream (TextIO) – The stream to which to write

  • level (str) – Optional filter (case insensitive) name of error message level (generally either “Error” or “Warning”)

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

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

Parameters:

Deprecated since version 1.5.8: Default is the use streaming graph_summary with inspector

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: BaseGraph, name: Optional[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