知识表示学习常用数据集
WN11
WN11所包含的11种关系
出处（SE）
Bordes A, Weston J, Collobert R, et al. Learning Structured Embeddings of Knowledge Bases[C]//AAAI. 2011, 6(1): 6. PDF
特点
As WordNet is composed of words with different meanings, here we term its entities as the concatenation of the word and an number indicating which sense it refers to i.e. auto_1 is the entityencoding the first meaning of the word “auto”.
举例：(_auto_1, _has_instance, _s_u_v_1)
WN18
WN18所包含的18种关系
出处（SME）
Bordes A, Glorot X, Weston J, et al. A semantic matching energy function for learning with multi-relational data[J]. Machine Learning, 2014, 94(2): 233-259. PDF
特点
entities (termed synsets) correspond to senses, and relation types define lexical relations between those senses.
As WordNet is composed of words with different meanings, we describe its entities by the concatenation of the word, its part-of-speech tag (‘NN’ for noun, ‘VB’ for verb, ‘JJ’ for adjective and ‘RB’ for adverb) and a digit indicating which sense it refers to i.e. _score_NN_1 is the entity encoding the first meaning of the noun “score”. This version of WordNet is different from that used in Bordes et al. (2011) because the original data has been preprocessed differently: this version contains less entities but more relation types.
举例： (_score_NN_1, _hypernym, _evaluation_NN_1)
FB13
FB13所包含的13种关系
出处（SE）
Bordes A, Weston J, Collobert R, et al. Learning Structured Embeddings of Knowledge Bases[C]//AAAI. 2011, 6(1): 6. PDF
特点
举例：(_marylin_monroe, _profession, _actress)
FB15K
出处（TransE）
Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data[C]//Advances in neural information processing systems. 2013: 2787-2795. PDF
特点
To make a small data set to experiment on we selected the subset of entities that are also present in the Wikilinks database and that also have at least 100 mentions in Freebase (for both entities and relationships). We also removed relationships like ’!/people/person/nationality’ which just reverses the head and tail compared to the relationship ’/people/person/nationality’.
FB1M
出处（TransE）
Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data[C]//Advances in neural information processing systems. 2013: 2787-2795. PDF
特点
We also wanted to have large-scale data in order to test TransE at scale. Hence, we created another data set from Freebase, by selecting the most frequently occurring 1 million entities.