Relation Prediction
84 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find Relation Prediction models and implementationsMost implemented papers
Inductive Relation Prediction by Subgraph Reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.
Relational Message Passing for Knowledge Graph Completion
hwwang55/PathCon
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Specifically, two kinds of neighborhood topology are modeled for a given entity pair under the relational message passing framework: (1) Relational context, which captures the relation types of edges adjacent to the given entity pair; (2) Relational paths, which characterize the relative position between the given two entities in the knowledge graph.
Structured Sparse R-CNN for Direct Scene Graph Generation
mcg-nju/structured-sparse-rcnn
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CVPR 2022
The key to our method is a set of learnable triplet queries and a structured triplet detector which could be jointly optimized from the training set in an end-to-end manner.
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
migalkin/NodePiece
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ICLR 2022
To this end, we propose NodePiece, an anchor-based approach to learn a fixed-size entity vocabulary.
DisSent: Sentence Representation Learning from Explicit Discourse Relations
facebookresearch/InferSent
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Learning effective representations of sentences is one of the core missions of natural language understanding.
KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
Deep Weakly-supervised Anomaly Detection
mala-lab/prenet
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To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two randomly sampled training instances, in which the pairwise relation can be anomaly-anomaly, anomaly-unlabeled, or unlabeled-unlabeled.
VLPrompt: Vision-Language Prompting for Panoptic Scene Graph Generation
Leveraging the recent progress in Large Language Models (LLMs), we propose to use language information to assist relation prediction, particularly for rare relations.
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).
EntEval: A Holistic Evaluation Benchmark for Entity Representations
ZeweiChu/EntEval
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IJCNLP 2019
Rich entity representations are useful for a wide class of problems involving entities.
