Сравнение методов

	Векторные представления графов знаний (Knowledge Graph Embeddings, KGE)	Word2Vec
Область	Сетевой анализ	Интеллектуальный анализ текста
Семейство	Machine learning	Process / pipeline
Год появления	2013	2013
Автор метода	Bordes, Usunier, García-Durán, Weston & Yakhnenko	Tomas Mikolov et al.
Тип	Graph representation learning via low-dimensional vector embeddings	Neural word-embedding model
Основополагающий источник	Bordes, A., Usunier, N., García-Durán, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems, 26.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Другие названия	KG Embeddings, Knowledge Graph Representation Learning, Relational Embeddings, Bilgi Grafı Gömme	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Связанные	3	4
Сводка	Knowledge Graph Embeddings (KGE) are a family of methods that represent entities and relations in a knowledge graph as dense, low-dimensional vectors in a continuous space. The foundational model, TransE, was introduced by Bordes, Usunier, García-Durán, Weston, and Yakhnenko in 2013. TransE treats each relation as a translation in embedding space — the head entity vector plus the relation vector should approximate the tail entity vector for any true triple (h, r, t). This simple geometric principle enabled effective link prediction and knowledge base completion at scale.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.