Knowledge Graph Embeddings
109 papers with code • 0 benchmarks • 4 datasets
Benchmarks
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Libraries
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MutualFriends
OLPBENCH
Most implemented papers
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
We study the problem of learning to reason in large scale knowledge graphs (KGs).
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images.
Recommendation Through Mixtures of Heterogeneous Item Relationships
kang205/MoHR
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Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data.
Binarized Knowledge Graph Embeddings
This limitation is expected to become more stringent as existing knowledge graphs, which are already huge, keep steadily growing in scale.
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
deepakn97/relationPrediction
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ACL 2019
The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings
uma-pi1/kge
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ICLR 2020
A vast number of KGE techniques for multi-relational link prediction have been proposed in the recent literature, often with state-of-the-art performance.
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
pykeen/pykeen
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Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
zjunlp/relphormer
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EMNLP 2021
Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block.
Inductive Entity Representations from Text via Link Prediction
dfdazac/blp
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However, the extent to which these representations learned for link prediction generalize to other tasks is unclear.
