Results 41 to 50 of about 30,602 (303)
Knowledge Graph Embedding via Graph Attenuated Attention Networks
IEEE Access, 2020 Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are based on graph convolutional neural
Rui Wang +4 more
doaj +1 more source
Relation path embedding in knowledge graphs [PDF]
Neural Computing and Applications, 2018 Large-scale knowledge graphs have currently reached impressive sizes; however, they are still far from complete. In addition, most existing methods for knowledge graph completion only consider the direct links between entities, ignoring the vital impact of the semantics of relation paths.
Xixun Lin +4 more
openaire +3 more sources
Efficiently embedding dynamic knowledge graphs
Knowledge-Based Systems, 2022 46 ...
Tianxing Wu 0001 +4 more
openaire +3 more sources
Triple Context-Based Knowledge Graph Embedding
IEEE Access, 2018 Knowledge graph embedding aims to represent entities and relations of a knowledge graph in continuous vector spaces. It has increasingly drawn attention for its ability to encode semantics in low dimensional vectors as well as its outstanding performance
Huan Gao, Jun Shi, Guilin Qi, Meng Wang
doaj +1 more source
TransET: Knowledge Graph Embedding with Entity Types [PDF]
, 2021 Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs.
Peng Wang +3 more
core +1 more source
Knowledge Graph Embedding Based Collaborative Filtering
IEEE Access, 2020 Along with the rapidly increasing massive online data, recommender systems have been used as an effective approach for filtering useful information, which have been widely adopted in many web applications.
Yuhang Zhang, Jun Wang, Jie Luo
doaj +1 more source
KGvec2go -- Knowledge Graph Embeddings as a Service
CoRR, 2020 In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple ...
Portisch, Jan +2 more
openaire +4 more sources
BiQUE: Biquaternionic Embeddings of Knowledge Graphs [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs). Existing KGE models rely on geometric operations to model relational patterns. Euclidean (circular) rotation is useful for modeling patterns such as symmetry, but cannot represent hierarchical semantics.
Jia Guo, Stanley Kok
openaire +2 more sources
Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding [PDF]
, 2023 Knowledge graph embedding involves learning representations of entities -- the vertices of the graph -- and relations -- the edges of the graph -- such that the resulting representations encode the known factual information represented by the knowledge ...
Schrater, Paul +2 more
core +1 more source
STKE: Temporal Knowledge Graph Embedding in the Spherical Coordinate System [PDF]
, 2022 Knowledge graph embedding (KGE) aims to learn the representation of entities and predicates in low-dimensional vector spaces which can complete the missing parts of the Knowledge Graphs (KGs).
Shen, Linshan +3 more
core +2 more sources