We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge ...
This work proposes a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and ...
Aug 25, 2020 · We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge ...
Abstract: Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable ...
Jan 1, 2020 · We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix ...
Nov 7, 2022 · In this paper, we provide a systematic review of existing KGE techniques based on representation spaces.
We propose a correlation-based knowledge representation learning (CKRL) method. We utilize three semantic spaces, namely entity, relation, and canonical spaces.
Representation Learning of Knowledge Graphs with Embedding Subspaces · AutoTGRL: an automatic text-graph representation learning framework.
Feb 3, 2023 · We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge ...
Missing: Subspaces. | Show results with:Subspaces.
Embedding of a knowledge graph. The vector representation of the entities and relations can be used for different machine learning applications.
Missing: Subspaces. | Show results with:Subspaces.