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Holographic Embeddings of Knowledge Graphs
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs.
Maximilian Nickel +2 more
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Knowledge Graph Embedding Compression [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 Knowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications. With a large KG, the embeddings consume a large amount of storage and memory. This is problematic and prohibits the deployment of these techniques in many real world settings. Thus, we
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Knowledge Association with Hyperbolic Knowledge Graph Embeddings [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 EMNLP ...
Zequn Sun 0001 +5 more
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Enriching Translation-Based Knowledge Graph Embeddings Through Continual Learning
IEEE Access, 2018 This paper addresses an enrichment of translation-based knowledge graph embeddings. When new knowledge triples become available after a knowledge graph is embedded onto a vector space, the embedding should be enriched with the new triples, but without ...
Hyun-Je Song, Seong-Bae Park
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Recommender Systems Based on Graph Embedding Techniques: A Review
IEEE Access, 2022 As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user’s preferred items from millions of candidates by analyzing observed user-item relations.
Yue Deng
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Quaternion Knowledge Graph Embeddings
CoRR, 2019 Accepted by NeurIPS ...
Shuai Zhang 0007 +3 more
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Binarized Knowledge Graph Embeddings [PDF]
, 2019 Tensor factorization has become an increasingly popular approach to knowledge graph completion(KGC), which is the task of automatically predicting missing facts in a knowledge graph. However, even with a simple model like CANDECOMP/PARAFAC(CP) tensor decomposition, KGC on existing knowledge graphs is impractical in resource-limited environments, as a ...
Koki Kishimoto +4 more
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Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet.
PLoS ONE, 2021 Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work.
YueQun Wang +3 more
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Embedding models for episodic knowledge graphs [PDF]
Journal of Web Semantics, 2019 26 ...
Yunpu Ma +2 more
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Knowledge Graph Embedding [PDF]
, 2017 A knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. The construction of knowledge graphs in the past decades facilitates many applications, such as link prediction, web search analysis ...
Hailun Lin +4 more
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