RETRA: Recurrent Transformers for Learning Temporally Contextualized Knowledge Graph Embeddings

Autor:	Achim Rettinger, Lavdim Halilaj, Simon Werner, Jürgen Lüttin
Rok vydání:	2021
Předmět:	Theoretical computer science Relation (database) Knowledge graph Computer science Context (language use) Distributional semantics Type (model theory) Representation (mathematics) Transformer (machine learning model)
Zdroj:	The Semantic Web ISBN: 9783030773847 ESWC
Popis:	Knowledge graph embeddings (KGE) are vector representations that capture the global distributional semantics of each entity instance and relation type in a static Knowledge Graph (KG). While KGEs have the capability to embed information related to an entity into a single representation, they are not customizable to a specific context.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d24de6200f77ccf38ec75ef23ada9680 https://doi.org/10.1007/978-3-030-77385-4_25 Zobrazit plný text záznamu