Relationship Types
RelationshipType | Entity1 Type | Entity2 Type | Relationships Covered |
---|---|---|---|
AffiliatedWith | Person | Organization | Indicates any professional or academic relationship between the Person and the Organization
entities. The relationship can be any of these or other similar relationships:
Note: This is an indicative list of the relationships this type
covers.
For example, James has studied from the University of Toronto and works at ABC Corp. Here, two relationships can be parsed:Entity1 = James, RelationshipType = AffiliatedWith, Entity2 = University of Toronto Entity1 = James, RelationshipType = AffiliatedWith, Entity2 = ABC Corp |
LivesIn | Person | Location | Indicates a relationship between the Person and the
Location entities. The relationship can be any of these:
Note: This is an indicative list of the relationships this type
covers.
For example, John Jamison, a National Weather Service meteorologist in Galveston, reported that a massive hurricane was about to hit the East Coast the next day. Entity1 = John Jamison, RelationshipType = LivesIn, Entity2 = Galveston |
OrgBasedIn | Organization | Location | Indicates that the Organization has at least one of its offices in
the Location. The Location can be a branch office, development office, headquarters, and the like. For example, HSBC Holdings Plc. is headquartered in London, United Kingdom. Here, two relationships can be parsed: Entity1 = HSBC Holdings Plc., RelationshipType = OrgBasedIn, Entity2 = London Entity1 = HSBC Holdings Plc., RelationshipType = OrgBasedIn, Entity2 = United States of America |
LocatedIn | Location | Location | Indicates the relationship between two different locations, where one of the
entities is geographically contained within the other entity.
|
Negative | Person Organization Location |
Organization Location |
Indicates that none of the above relationship types could be parsed between the two corresponding entities. For example, New Delhi and New York are good places to live in. On parsing this input text, none of the supported relationship types are parsed between any pair of identified entities. Hence it can be broken down into Negative relationship types between the identified entities: Entity1 = New Delhi, RelationshipType = Negative, Entity2 = New York |
Example
In case of a complex input text, multiple possible relationship type combinations might be parsed for the same sentence.
For example,
James McCarthy has settled in New York, United States as director of ABC Technologies.When the Relationship Extractor stage parses this input text using the relationship types selected in the stage options, the relationships found are:
- Relationship 1
- Entity1 = James McCarthy, Entity1 Type = Person, RelationshipType = LivesIn, Entity2 = New York, Entity2 Type = Location
- Relationship 2
- Entity1 = James McCarthy, Entity1 Type = Person, RelationshipType = AffiliatedWith, Entity2 = ABC Technologies, Entity2 Type = Organization
- Relationship 3
- Entity1 = ABC Technologies, Entity1 Type = Organization, RelationshipType = OrgBasedIn, Entity2 = United States, Entity2 Type = Location
- Relationship 4
- Entity1 = ABC Technologies, Entity1 Type = Organization, RelationshipType = OrgBasedIn, Entity2 = New York, Entity2 Type = Location
- Relationship 5
- Entity1 = James McCarthy, Entity1 Type = Person, RelationshipType = LivesIn, Entity2 = United States, Entity2 Type = Location
- Relationship 6
- Entity1 = New York, Entity1 Type = Location, RelationshipType = LocatedIn, Entity2 = United States, Entity2 Type = Location