nlp_architect.data.cdc_resources.relations package

Submodules

nlp_architect.data.cdc_resources.relations.computed_relation_extraction module

class nlp_architect.data.cdc_resources.relations.computed_relation_extraction.ComputedRelationExtraction[source]

Bases: nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction

Extract Relation between two mentions according to computation and rule based algorithms

extract_all_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]

Try to find if mentions has anyone or more of the relations this class support

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

One or more of: RelationType.EXACT_STRING, RelationType.FUZZY_FIT,

RelationType.FUZZY_HEAD_FIT, RelationType.SAME_HEAD_LEMMA, RelationType.SAME_HEAD_LEMMA_RELAX
Return type:

Set[RelationType]
static extract_exact_string(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has exact string relation

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

RelationType.EXACT_STRING or RelationType.NO_RELATION_FOUND
static extract_fuzzy_fit(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has fuzzy fit relation

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

RelationType.FUZZY_FIT or RelationType.NO_RELATION_FOUND
static extract_fuzzy_head_fit(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has fuzzy head fit relation

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

RelationType.FUZZY_HEAD_FIT or RelationType.NO_RELATION_FOUND
static extract_same_head_lemma(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has same head lemma relation

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

RelationType.SAME_HEAD_LEMMA or RelationType.NO_RELATION_FOUND
extract_sub_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has the given relation between them

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
  • relation – RelationType
Returns:

relation in case mentions has given relation or

RelationType.NO_RELATION_FOUND otherwise
Return type:

RelationType
static get_supported_relations() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]

Return all supported relations by this class

Returns:List[RelationType]

nlp_architect.data.cdc_resources.relations.referent_dict_relation_extraction module

class nlp_architect.data.cdc_resources.relations.referent_dict_relation_extraction.ReferentDictRelationExtraction(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.OnlineOROfflineMethod = <OnlineOROfflineMethod.ONLINE: 'online'>, ref_dict: str = None)[source]

Bases: nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction

extract_all_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]
extract_sub_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has the given relation between them

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
  • relation – RelationType
Returns:

relation in case mentions has given relation or

RelationType.NO_RELATION_FOUND otherwise
Return type:

RelationType
static get_supported_relations()[source]

Return all supported relations by this class

Returns:List[RelationType]
is_referent_dict(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → bool[source]

Check if input mentions has referent dictionary relation between them

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

bool
static load_reference_dict(dict_fname: str) → Dict[str, List[str]][source]

Method to load referent dictionary to memory

Returns:List[RelationType]

nlp_architect.data.cdc_resources.relations.relation_extraction module

class nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction[source]

Bases: object

extract_relation(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Base Class Check if Sub class support given relation before executing the sub class

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
  • relation – RelationType
Returns:

relation in case mentions has given relation and

RelationType.NO_RELATION_FOUND otherwise
Return type:

RelationType
extract_sub_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]
static get_supported_relations() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]

nlp_architect.data.cdc_resources.relations.relation_types_enums module

class nlp_architect.data.cdc_resources.relations.relation_types_enums.EmbeddingMethod[source]

Bases: enum.Enum

An enumeration.

ELMO = 'elmo'
ELMO_OFFLINE = 'elmo_offline'
GLOVE = 'glove'
GLOVE_OFFLINE = 'glove_offline'
class nlp_architect.data.cdc_resources.relations.relation_types_enums.OnlineOROfflineMethod[source]

Bases: enum.Enum

An enumeration.

OFFLINE = 'offline'
ONLINE = 'online'
class nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Bases: enum.Enum

An enumeration.

EXACT_STRING = 8
FUZZY_FIT = 9
FUZZY_HEAD_FIT = 10
NO_RELATION_FOUND = 0
OTHER = 21
REFERENT_DICT = 18
SAME_HEAD_LEMMA = 11
VERBOCEAN_MATCH = 13
WIKIPEDIA_ALIASES = 2
WIKIPEDIA_BE_COMP = 7
WIKIPEDIA_CATEGORY = 5
WIKIPEDIA_DISAMBIGUATION = 3
WIKIPEDIA_PART_OF_SAME_NAME = 4
WIKIPEDIA_TITLE_PARENTHESIS = 6
WITHIN_DOC_COREF = 20
WORDNET_DERIVATIONALLY = 14
WORDNET_PARTIAL_SYNSET_MATCH = 15
WORDNET_SAME_SYNSET = 17
WORD_EMBEDDING_MATCH = 19
class nlp_architect.data.cdc_resources.relations.relation_types_enums.WikipediaSearchMethod[source]

Bases: enum.Enum

An enumeration.

ELASTIC = 'elastic'
OFFLINE = 'offline'
ONLINE = 'online'

nlp_architect.data.cdc_resources.relations.verbocean_relation_extraction module

class nlp_architect.data.cdc_resources.relations.verbocean_relation_extraction.VerboceanRelationExtraction(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.OnlineOROfflineMethod = <OnlineOROfflineMethod.ONLINE: 'online'>, vo_file: str = None)[source]

Bases: nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction

extract_all_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]
extract_sub_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has the given relation between them

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
  • relation – RelationType
Returns:

relation in case mentions has given relation or

RelationType.NO_RELATION_FOUND otherwise
Return type:

RelationType
static get_supported_relations()[source]

Return all supported relations by this class

Returns:List[RelationType]
is_verbocean_relation(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → bool[source]

Check if input mentions has VerbOcean relation between them

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

bool
static load_verbocean_file(fname: str) → Dict[str, Dict[str, str]][source]

Method to load referent dictionary to memory

Returns:List[RelationType]

nlp_architect.data.cdc_resources.relations.wikipedia_relation_extraction module

class nlp_architect.data.cdc_resources.relations.wikipedia_relation_extraction.WikipediaRelationExtraction(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.WikipediaSearchMethod = <WikipediaSearchMethod.ONLINE: 'online'>, wiki_file: str = None, host: str = None, port: int = None, index: str = None, filter_pronouns: bool = True, filter_time_data: bool = True)[source]

Bases: nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction

static extract_aliases(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has aliases relation

Parameters:
  • pages1 – WikipediaPages
  • pages2 – WikipediaPage
  • titles1 – Set[str]
  • titles2 – Set[str]
Returns:

RelationType.WIKIPEDIA_ALIASES or RelationType.NO_RELATION_FOUND
extract_all_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]

Try to find if mentions has anyone or more of the relations this class support

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

One or more of: RelationType.WIKIPEDIA_BE_COMP,

RelationType.WIKIPEDIA_TITLE_PARENTHESIS, RelationType.WIKIPEDIA_DISAMBIGUATION, RelationType.WIKIPEDIA_CATEGORY, RelationType.WIKIPEDIA_REDIRECT_LINK, RelationType.WIKIPEDIA_ALIASES, RelationType.WIKIPEDIA_PART_OF_SAME_NAME
Return type:

Set[RelationType]
static extract_be_comp(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has be-comp/is-a relation

Parameters:
  • pages1 – WikipediaPages
  • pages2 – WikipediaPage
  • titles1 – Set[str]
  • titles2 – Set[str]
Returns:

RelationType.WIKIPEDIA_BE_COMP or RelationType.NO_RELATION_FOUND
static extract_category(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has category relation

Parameters:
  • pages1 – WikipediaPages
  • pages2 – WikipediaPage
  • titles1 – Set[str]
  • titles2 – Set[str]
Returns:

RelationType.WIKIPEDIA_CATEGORY or RelationType.NO_RELATION_FOUND
static extract_disambig(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has disambiguation relation

Parameters:
  • pages1 – WikipediaPages
  • pages2 – WikipediaPage
  • titles1 – Set[str]
  • titles2 – Set[str]
Returns:

RelationType.WIKIPEDIA_DISAMBIGUATION or RelationType.NO_RELATION_FOUND
static extract_parenthesis(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, titles1: Set[str], titles2: Set[str]) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has parenthesis relation

Parameters:
  • pages1 – WikipediaPages
  • pages2 – WikipediaPage
  • titles1 – Set[str]
  • titles2 – Set[str]
Returns:

RelationType.WIKIPEDIA_TITLE_PARENTHESIS or RelationType.NO_RELATION_FOUND
extract_sub_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has the given relation between them

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
  • relation – RelationType
Returns:

relation in case mentions has given relation or

RelationType.NO_RELATION_FOUND otherwise
Return type:

RelationType

Get all WikipediaPages pages related with this mention string

Parameters:mention_str – str
Returns:WikipediaPages
static get_supported_relations() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]

Return all supported relations by this class

Returns:List[RelationType]
static is_both_data_or_time(mention1: nlp_architect.common.cdc.mention_data.MentionDataLight, mention2: nlp_architect.common.cdc.mention_data.MentionDataLight) → bool[source]

check if both phrases refers to time or date

Returns:bool
static is_both_opposite_personal_pronouns(phrase1: str, phrase2: str) → bool[source]

check if both phrases refers to pronouns

Returns:bool
is_part_of_same_name(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages) → bool[source]

Check if input mentions has part of same name relation (eg: page1=John, page2=Smith)

Parameters:
  • pages1 – WikipediaPages
  • pages2 – WikipediaPage
Returns:

bool
static is_redirect_same(pages1: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages, pages2: nlp_architect.data.cdc_resources.data_types.wiki.wikipedia_pages.WikipediaPages) → bool[source]

Check if input mentions has same wikipedia redirect page

Parameters:
  • pages1 – WikipediaPages
  • pages2 – WikipediaPage
Returns:

bool

nlp_architect.data.cdc_resources.relations.within_doc_coref_extraction module

class nlp_architect.data.cdc_resources.relations.within_doc_coref_extraction.WithinDocCoref(wd_file: str)[source]

Bases: nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction

static arrange_resource(wd_mentions_json)[source]
static create_ment_id(mention_x: nlp_architect.common.cdc.mention_data.MentionData, mention_y: nlp_architect.common.cdc.mention_data.MentionData) → str[source]
extract_all_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionData, mention_y: nlp_architect.common.cdc.mention_data.MentionData) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]
extract_sub_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionData, mention_y: nlp_architect.common.cdc.mention_data.MentionData, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has the given relation between them

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
  • relation – RelationType
Returns:

relation in case mentions has given relation or

RelationType.NO_RELATION_FOUND otherwise
Return type:

RelationType
extract_within_coref(mention: nlp_architect.common.cdc.mention_data.MentionData) → List[str][source]
static get_supported_relations() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]

Return all supported relations by this class

Returns:List[RelationType]
get_within_doc_coref_chain()[source]

nlp_architect.data.cdc_resources.relations.word_embedding_relation_extraction module

class nlp_architect.data.cdc_resources.relations.word_embedding_relation_extraction.WordEmbeddingRelationExtraction(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.EmbeddingMethod = <EmbeddingMethod.GLOVE: 'glove'>, glove_file: str = None, elmo_file: str = None, cos_accepted_dist: float = 0.7)[source]

Bases: nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction

extract_all_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]
extract_sub_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has the given relation between them

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
  • relation – RelationType
Returns:

relation in case mentions has given relation or

RelationType.NO_RELATION_FOUND otherwise
Return type:

RelationType
static get_supported_relations() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]

Return all supported relations by this class

Returns:List[RelationType]
is_word_embed_match(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight)[source]

Check if input mentions Word Embedding cosine distance below above 0.65

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

bool

nlp_architect.data.cdc_resources.relations.wordnet_relation_extraction module

class nlp_architect.data.cdc_resources.relations.wordnet_relation_extraction.WordnetRelationExtraction(method: nlp_architect.data.cdc_resources.relations.relation_types_enums.OnlineOROfflineMethod = <OnlineOROfflineMethod.ONLINE: 'online'>, wn_file: str = None)[source]

Bases: nlp_architect.data.cdc_resources.relations.relation_extraction.RelationExtraction

extract_all_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight) → Set[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]

Try to find if mentions has anyone or more of the relations this class support

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
Returns:

One or more of: RelationType.WORDNET_SAME_SYNSET_ENTITY,

RelationType.WORDNET_SAME_SYNSET_EVENT, RelationType.WORDNET_PARTIAL_SYNSET_MATCH, RelationType.WORDNET_DERIVATIONALLY
Return type:

Set[RelationType]
static extract_derivation(page_x: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage, page_y: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has derivation relation

Parameters:
  • page_x – WordnetPage
  • page_y – WordnetPage
Returns:

RelationType.WORDNET_DERIVATIONALLY or RelationType.NO_RELATION_FOUND
static extract_partial_synset_match(page_x: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage, page_y: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has partial synset relation

Parameters:
  • page_x – WordnetPage
  • page_y – WordnetPage
Returns:

RelationType.WORDNET_PARTIAL_SYNSET_MATCH or RelationType.NO_RELATION_FOUND
static extract_same_synset_entity(page_x: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage, page_y: nlp_architect.data.cdc_resources.data_types.wn.wordnet_page.WordnetPage) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has same synset relation for entity mentions

Parameters:
  • page_x – WordnetPage
  • page_y – WordnetPage
Returns:

RelationType.WORDNET_SAME_SYNSET_ENTITY or RelationType.NO_RELATION_FOUND
extract_sub_relations(mention_x: nlp_architect.common.cdc.mention_data.MentionDataLight, mention_y: nlp_architect.common.cdc.mention_data.MentionDataLight, relation: nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType) → nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType[source]

Check if input mentions has the given relation between them

Parameters:
  • mention_x – MentionDataLight
  • mention_y – MentionDataLight
  • relation – RelationType
Returns:

relation in case mentions has given relation or

RelationType.NO_RELATION_FOUND otherwise
Return type:

RelationType
static get_supported_relations() → List[nlp_architect.data.cdc_resources.relations.relation_types_enums.RelationType][source]

Return all supported relations by this class

Returns:List[RelationType]

Module contents