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MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering
[article]
2022
arXiv
pre-print
Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used in NLP tasks requiring implicit information about the real world. Furthermore, the size of KGs that may be useful in actual NLP assignments is enormous, and creating embedding over it has memory cost issues. We represent KG as a 3rd-order binary tensor and move beyond the standard CP decomposition by using a data-specific generalized version of it. The generalization of the standard
arXiv:2204.10629v2
fatcat:vbijfw6nkze3ne2iawfez2scd4