Representation Learning of Knowledge Graphs with Embedding Subspaces

Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result i...

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Veröffentlicht in:	Scientific programming 2020, Vol.2020 (2020), p.1-10
Hauptverfasser:	Cui, Zhiming, Ai, Xusheng, Xian, Xuefeng, Li, Chunhua
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Embedding Graphical representations Graphs Knowledge bases (artificial intelligence) Knowledge representation Natural language processing Semantics Subspaces Teaching methods Training Unstructured data
Online-Zugang:	Volltext
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