Linganisha mbinu

	Knowledge Graph Embeddings	Umuhimu wa Ukurasa (PageRank Centrality)
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2013	1999
Mwanzilishi	Bordes, Usunier, García-Durán, Weston & Yakhnenko	Page, Brin, Motwani & Winograd
Aina	Graph representation learning via low-dimensional vector embeddings	Iterative link-based centrality algorithm
Chanzo asilia	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.	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.
Majina mbadala	KG Embeddings, Knowledge Graph Representation Learning, Relational Embeddings, Bilgi Grafı Gömme	Google PageRank, Random Surfer Model, Link-Based Ranking, PageRank Merkeziliği
Zinazohusiana	3	2
Muhtasari	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.	PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Google search engine and remains one of the most influential algorithms in network science and information retrieval.