Update README.md
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README.md
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@@ -27,256 +27,21 @@ The number of triples in each split is summarized in the table below.
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- Number of instances (`filter_unified.min_entity_4_max_predicate_10`)
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|:--------------------------------|--------:|-------------:|-------:|
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| number of pairs | 603 | 68 | 122 |
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| number of unique relation types | 157 | 52 | 34 |
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- Number of pairs in each relation type (`filter_unified.min_entity_4_max_predicate_10`)
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| | number of pairs (train) | number of pairs (validation) | number of pairs (test) |
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|:----------------------------------------------------------|--------------------------:|-------------------------------:|-------------------------:|
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| [Academic Subject] studies [Topic] | 3 | 0 | 0 |
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| 40 |
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| [Airline] is in [Airline Alliance] | 3 | 2 | 0 |
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| 41 |
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| [Army] has [Fleet] | 9 | 1 | 0 |
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| 42 |
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| [Art Work] follows after [Art Work] | 2 | 1 | 0 |
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| [Art Work] is a translation of [Art Work] | 2 | 1 | 0 |
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| [Art Work] is painted by [Person] | 1 | 2 | 0 |
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| [Art Work] is sculpted by [Person] | 4 | 0 | 0 |
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| [Art Work] is written by [Person] | 1 | 0 | 0 |
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| [Artifact] has a shape of [Shape] | 1 | 0 | 0 |
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| [Artifact] is a patron saint of [Country] | 4 | 0 | 0 |
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| [Artifact] is a type of [Type] | 3 | 0 | 0 |
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| [Artifact] is built on [Date] | 5 | 0 | 0 |
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| [Artifact] is discovered by [Person] | 4 | 0 | 0 |
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| 52 |
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| [Artifact] is formation of [Army] | 1 | 1 | 0 |
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| 53 |
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| [Artifact] is formed from [Artifact] | 9 | 0 | 0 |
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| 54 |
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| [Artifact] is influenced by [Artifact] | 6 | 1 | 0 |
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| [Artifact] is maintained by [Company] | 6 | 2 | 0 |
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| [Artifact] is name of [Artifact] | 10 | 0 | 0 |
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| [Artifact] is named after [Person] | 5 | 0 | 0 |
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| [Artifact] is the OS of [Software] | 3 | 0 | 0 |
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| [Artifact] is the platform of [Game] | 4 | 0 | 0 |
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| [Artifact] is used for its namesake of [Artifact] | 3 | 0 | 0 |
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| 61 |
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| [Artists] leads [Movement] | 7 | 0 | 0 |
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| 62 |
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| [Award] is presented by [Company] | 1 | 0 | 0 |
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| 63 |
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| [Bank] is the central bank of [Country] | 1 | 0 | 0 |
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| 64 |
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| [Bridge] crosses [Artifact] | 3 | 1 | 0 |
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| [Bridge] crosses [River] | 1 | 0 | 0 |
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| [Building] has an architectural style of [Person] | 5 | 0 | 0 |
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| [City] is a twin city of [City] | 6 | 2 | 0 |
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| [City] is in [Country] | 1 | 0 | 0 |
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| [City] is the capital of [Country] | 2 | 0 | 0 |
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| [Company] is a subsidiary of [Company] | 3 | 1 | 0 |
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| [Company] is in a sector of [Sector] | 2 | 0 | 0 |
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| [Company] operates [Vehicle] | 1 | 0 | 0 |
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| [Company] owns [Product] | 3 | 1 | 0 |
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| [Company] publishes [Art Work] | 7 | 0 | 0 |
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| [Competition] is a league of [Sport] | 2 | 2 | 0 |
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| [Council] is the council of [Country] | 6 | 0 | 0 |
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| [Country] has [History] | 2 | 0 | 0 |
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| [Country] is [Political Party] assembly | 4 | 1 | 0 |
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| [Country] is enclaved by [Country] | 4 | 0 | 0 |
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| [Country] is in [Continent] | 2 | 0 | 0 |
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| [Country] joins [War] | 2 | 1 | 0 |
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| [Country]'s county seat is [Location] | 6 | 0 | 0 |
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| [Country]'s flag is [Artifact] | 1 | 0 | 0 |
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| [Culture] is originated in [Country] | 2 | 0 | 0 |
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| [Currency] is used in [Country] | 3 | 2 | 0 |
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| [Disease] is caused by [Virus] | 4 | 0 | 0 |
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| [Event] is since [Date] | 3 | 0 | 0 |
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| [Event] takes place at [Location] | 4 | 0 | 0 |
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| [Fictional Character] is a mascot of [Sport Team] | 2 | 1 | 0 |
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| [Fictional Character] is from [Art Work] | 6 | 1 | 0 |
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| [Food] is made from [Ingredient] | 6 | 0 | 0 |
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| [Government] is the government of [Country] | 4 | 0 | 0 |
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| [Government] is the jurisdiction of [City] | 1 | 0 | 0 |
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| [Group] has a section of [Group] | 6 | 1 | 0 |
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| [Group] is a predecessor of [Group] | 5 | 0 | 0 |
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| [Group] is a religious order of [Group] | 1 | 0 | 0 |
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| [Group] is created on [Date] | 6 | 0 | 0 |
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| [Group] is founded at [Location] | 9 | 0 | 0 |
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| [Group] is founded by [Person] | 6 | 0 | 0 |
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| [Group] is founded on [Date] | 2 | 0 | 0 |
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| [Group] is legislature of [Country] | 2 | 0 | 0 |
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| [Group] is the parliament of [Country] | 2 | 0 | 0 |
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| [Group]'s leader is [Person] | 3 | 0 | 0 |
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| [Head of Government] is appointed by [Head of Government] | 1 | 0 | 0 |
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| [Island] is [Country] | 1 | 0 | 0 |
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| [Job] is the head of state in [Location] | 1 | 0 | 0 |
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| [Land] is [Country] | 1 | 1 | 0 |
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| [Language] consists of [Alphabet] | 4 | 0 | 0 |
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| [Language] is a dialect of [Language] | 5 | 0 | 0 |
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| [Location] is a sovereign state of [Location] | 7 | 1 | 0 |
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| [Location] is an Indian reservation in [Country] | 9 | 0 | 0 |
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| [Location] is an administrative center of [Location] | 6 | 0 | 0 |
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| [Location] is exclave of [Country] | 4 | 0 | 0 |
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| [Location] is in [Planet] | 1 | 0 | 0 |
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| [Location] is next to [Location] | 1 | 0 | 0 |
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| [Location] is on the coast of [Ocean] | 5 | 0 | 0 |
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| [Location] is split from [Location] | 2 | 0 | 0 |
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| [Location] is the highest peak in [Country] | 2 | 0 | 0 |
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| [Medication] is for [Disease] | 4 | 0 | 0 |
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| [Movie] is [Genre] | 9 | 1 | 0 |
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| [Movie] is a libretto by [Person] | 6 | 0 | 0 |
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| [Movie] is a spinoff of [Movie] | 1 | 0 | 0 |
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| [Movie] is in the universe of [Art Work] | 1 | 0 | 0 |
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| [Movie] is produced by [Company] | 6 | 1 | 0 |
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| [Music Artist] is [Genre] | 7 | 0 | 0 |
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| [Music] is made by [Artist] | 1 | 1 | 0 |
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| [Music] is released on [Date] | 4 | 0 | 0 |
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| [Organization]'s ideology is [Ideology] | 5 | 0 | 0 |
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| [PC]'s cpu is [CPU] | 8 | 1 | 0 |
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| [Person] and [Person] are married | 5 | 0 | 0 |
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| [Person] belongs to [Record Label] | 1 | 1 | 0 |
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| [Person] built [Artifact] | 5 | 0 | 0 |
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| [Person] causes [War] | 2 | 0 | 0 |
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| [Person] creates [Work] | 4 | 0 | 0 |
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| [Person] dies at [Location] | 5 | 2 | 0 |
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| [Person] dies on [Date] | 5 | 1 | 0 |
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| [Person] has a house in [Location] | 9 | 0 | 0 |
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| [Person] is [Occupation] | 3 | 0 | 0 |
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| [Person] is [Sex] | 2 | 1 | 0 |
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| [Person] is a candidate of [Election] | 3 | 1 | 0 |
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| [Person] is a chancellor of [Country] | 4 | 0 | 0 |
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| [Person] is a coach of [Sport Team] | 3 | 0 | 0 |
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| [Person] is a concubine of [Person] | 4 | 0 | 0 |
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| [Person] is a consort of [Person] | 3 | 1 | 0 |
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| [Person] is a husband of [Person] | 2 | 0 | 0 |
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| [Person] is a manager of [Sport Team] | 3 | 0 | 0 |
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| [Person] is a member of [Music Group] | 6 | 1 | 0 |
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| [Person] is a mistress of [Person] | 4 | 0 | 0 |
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| [Person] is a premier of [Group] | 3 | 0 | 0 |
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| [Person] is a presenter of [TV show] | 2 | 0 | 0 |
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| [Person] is a student of [Person] | 1 | 0 | 0 |
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| [Person] is active in [Location] | 2 | 1 | 0 |
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| [Person] is awarded by [Award] | 5 | 0 | 0 |
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| [Person] is born in [Country] | 5 | 1 | 0 |
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| [Person] is born in [Location] | 4 | 1 | 0 |
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| [Person] is born on [Date] | 5 | 2 | 0 |
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| [Person] is buried at [Tomb] | 5 | 0 | 0 |
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| [Person] is drafted by [Group] | 3 | 0 | 0 |
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| [Person] is from [Era] | 6 | 0 | 0 |
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| [Person] is in [Prison] | 3 | 0 | 0 |
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| [Person] is killed by [Person] | 2 | 1 | 0 |
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| [Person] is played at [Group] | 7 | 3 | 0 |
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| [Person] is the chair of [Group] | 1 | 0 | 0 |
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| [Person] is the emperor of [Country] | 6 | 0 | 0 |
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| [Person] is the leader of [Dynasty] | 4 | 1 | 0 |
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| [Person] is the mayor of [City] | 2 | 0 | 0 |
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| [Person] is the monarch of [Country] | 4 | 0 | 0 |
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| [Person] is the president of [Country] | 3 | 1 | 0 |
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| [Person] is the prime minister of [Country] | 3 | 1 | 0 |
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| [Person] is the queen of [Country] | 5 | 0 | 0 |
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| [Person] live in [Location] | 8 | 1 | 0 |
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| [Person] plays in [Movie] | 3 | 0 | 0 |
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| [Person] plays in [Sport Team] | 8 | 0 | 0 |
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| [Person] studies [Academic Subject] | 6 | 1 | 0 |
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| [Person] studies at [School] | 9 | 1 | 0 |
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| [Pet] is a pet of [Person] | 1 | 0 | 0 |
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| [Planet] is in the orbit of [Orbit] | 1 | 0 | 0 |
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| [Play] is performed by [Person] | 7 | 2 | 0 |
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| [Railway] is in [Location] | 4 | 0 | 0 |
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| [Religion] is a denomination by [Artifact] | 2 | 2 | 0 |
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| [River] drains [Location] | 5 | 0 | 0 |
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| [River] is a tributary of [River] | 4 | 0 | 0 |
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| [River] outflows to [Location] | 3 | 0 | 0 |
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| [Software] is under license of [License] | 5 | 3 | 0 |
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| [Software] is used for [Purpose] | 3 | 0 | 0 |
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| [Software] is written in [Programming Language] | 6 | 1 | 0 |
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| [Sport Team] is an affiliate of [Sport Team] | 2 | 1 | 0 |
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| [Sport Team] plays at [Competition] | 5 | 0 | 0 |
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| [Sport Team] plays in [Competition] | 8 | 0 | 0 |
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| [Sport Team] wins [Competition] | 5 | 0 | 0 |
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| [Sport Team]'s home field is [Location] | 4 | 0 | 0 |
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| [Star] is a [Constellation] | 1 | 0 | 0 |
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| [State] is a state of [Country] | 1 | 0 | 0 |
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| [System] is a system in [Artifact] | 7 | 2 | 0 |
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| [Town] is in [Location] | 1 | 0 | 0 |
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| [Art Work] is directed by [Person] | 0 | 1 | 0 |
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| [Planet] is a satellite of [Planet] | 0 | 2 | 0 |
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| [Act] is signed by [Person] | 0 | 0 | 2 |
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| [Airline] has a hub in [Location] | 0 | 0 | 7 |
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| [Artifact] is a coat of arms of [Group] | 0 | 0 | 5 |
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| [Artifact] is a result of [Artifact] | 0 | 0 | 1 |
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| [Artifact] is found in [Artifact] | 0 | 0 | 4 |
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| [Artifact] is in [Color] | 0 | 0 | 4 |
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| [Artifact] is manufactured by [Company] | 0 | 0 | 5 |
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| [Artist] is produced by [Person] | 0 | 0 | 1 |
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| [City] is a capital town of [Country] | 0 | 0 | 6 |
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| [Country] claims [City] | 0 | 0 | 1 |
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| [Country] is a member of [Group] | 0 | 0 | 4 |
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| [Event] starts on [Date] | 0 | 0 | 2 |
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| [Group] is [Religion] | 0 | 0 | 6 |
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| [Group] is legislative body of [Country] | 0 | 0 | 8 |
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| [Group] is merged into [Group] | 0 | 0 | 4 |
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| [Group] speaks [Language] | 0 | 0 | 5 |
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| [License] is approved by [Organization] | 0 | 0 | 3 |
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| 215 |
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| [Location] is a ballpark of [Sport Team] | 0 | 0 | 3 |
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| [Location] is a river mouth of [Bay] | 0 | 0 | 1 |
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| [Movie] is screenplayed by [Person] | 0 | 0 | 5 |
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| [Movie] stars [Actor] | 0 | 0 | 2 |
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| 219 |
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| [Music] is an anthem of [Country] | 0 | 0 | 1 |
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| [Occupation] lives in [Location] | 0 | 0 | 1 |
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| [Person] belongs to [Political Party] | 0 | 0 | 10 |
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| [Person] is a chief executive of [Company] | 0 | 0 | 1 |
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| [Person] is a child of [Person] | 0 | 0 | 2 |
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| [Person] is the king of [Country] | 0 | 0 | 4 |
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| [Person] plays [Instrument] | 0 | 0 | 6 |
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| [Person] speaks [Language] | 0 | 0 | 1 |
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| [Radio Program] is broadcasted on [Radio Channel] | 0 | 0 | 3 |
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| [Software] is developed by [Company] | 0 | 0 | 1 |
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| [Station] is the terminus of [Railway] | 0 | 0 | 3 |
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| [TV Series] is broadcasted on [TV Channel] | 0 | 0 | 1 |
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| [Timezone] is a timezon in [Country] | 0 | 0 | 9 |
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### Other Statistics
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| | number of pairs | number of unique relation types |
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|:--------------------------------------------|------------------:|----------------------------------:|
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| min_entity_1_max_predicate_100 (train) | 7075 | 212 |
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| min_entity_1_max_predicate_100 (validation) | 787 | 166 |
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| min_entity_1_max_predicate_50 (train) | 4131 | 212 |
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| min_entity_1_max_predicate_50 (validation) | 459 | 156 |
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| min_entity_1_max_predicate_25 (train) | 2358 | 212 |
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| min_entity_1_max_predicate_25 (validation) | 262 | 144 |
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| min_entity_1_max_predicate_10 (train) | 1134 | 210 |
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| 244 |
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| min_entity_1_max_predicate_10 (validation) | 127 | 94 |
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| 245 |
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| min_entity_2_max_predicate_100 (train) | 4873 | 195 |
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| min_entity_2_max_predicate_100 (validation) | 542 | 139 |
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| min_entity_2_max_predicate_50 (train) | 3002 | 193 |
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| min_entity_2_max_predicate_50 (validation) | 334 | 139 |
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| min_entity_2_max_predicate_25 (train) | 1711 | 195 |
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| min_entity_2_max_predicate_25 (validation) | 191 | 113 |
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| min_entity_2_max_predicate_10 (train) | 858 | 194 |
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| 252 |
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| min_entity_2_max_predicate_10 (validation) | 96 | 81 |
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| min_entity_3_max_predicate_100 (train) | 3659 | 173 |
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| min_entity_3_max_predicate_100 (validation) | 407 | 116 |
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| min_entity_3_max_predicate_50 (train) | 2336 | 174 |
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| min_entity_3_max_predicate_50 (validation) | 260 | 115 |
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| min_entity_3_max_predicate_25 (train) | 1390 | 173 |
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| min_entity_3_max_predicate_25 (validation) | 155 | 94 |
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| 259 |
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| min_entity_3_max_predicate_10 (train) | 689 | 171 |
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| min_entity_3_max_predicate_10 (validation) | 77 | 59 |
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| min_entity_4_max_predicate_100 (train) | 2995 | 158 |
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| min_entity_4_max_predicate_100 (validation) | 333 | 105 |
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| 263 |
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| min_entity_4_max_predicate_50 (train) | 1989 | 157 |
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| 264 |
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| min_entity_4_max_predicate_50 (validation) | 222 | 102 |
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| min_entity_4_max_predicate_25 (train) | 1221 | 158 |
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| min_entity_4_max_predicate_25 (validation) | 136 | 76 |
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| min_entity_4_max_predicate_10 (train) | 603 | 157 |
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| min_entity_4_max_predicate_10 (validation) | 68 | 52 |
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### Filtering to Remove Noise
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We apply filtering to keep triples with named-entities in either of head or tail (`named-entity filter`).
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Then, we remove predicates if they have less than three triples (`rare-predicate filter`).
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After the filtering, we manually remove too vague and noisy predicate, and unify same predicates with different names (see the annotation [here](https://huggingface.co/datasets/relbert/t_rex/raw/main/predicate_manual_check.csv)).
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| Dataset | `raw` | `named-entity filter` | `rare-predicate` |
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| Triples | 20,877,472 | 12,561,573 | 12,561,250 |
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| Predicate | 1,616 | 1,470 | 1,237 |
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### Filtering to Purify the Dataset
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We reduce the size of the dataset by applying filtering based on the number of predicates and entities in the triples.
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- Number of instances (`filter_unified.min_entity_4_max_predicate_10`)
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|:--------------------------------|--------:|-------------:|-------:|
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| number of pairs | 603 | 68 | 122 |
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| number of unique relation types | 157 | 52 | 34 |
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| 35 |
|
| 36 |
### Filtering to Remove Noise
|
| 37 |
We apply filtering to keep triples with named-entities in either of head or tail (`named-entity filter`).
|
| 38 |
Then, we remove predicates if they have less than three triples (`rare-predicate filter`).
|
| 39 |
After the filtering, we manually remove too vague and noisy predicate, and unify same predicates with different names (see the annotation [here](https://huggingface.co/datasets/relbert/t_rex/raw/main/predicate_manual_check.csv)).
|
| 40 |
|
| 41 |
+
| Dataset | `raw` | `named-entity filter` | `rare-predicate` | `unify-denoise-predicate` |
|
| 42 |
+
|:----------|-----------:|-----------------------:|-----------------:|--------------------------:|
|
| 43 |
+
| Triples | 20,877,472 | 12,561,573 | 12,561,250 | 432,781 |
|
| 44 |
+
| Predicate | 1,616 | 1,470 | 1,237 | 246 |
|
| 45 |
|
| 46 |
### Filtering to Purify the Dataset
|
| 47 |
We reduce the size of the dataset by applying filtering based on the number of predicates and entities in the triples.
|