Triple Classification
21 papers with code • 1 benchmarks • 4 datasets
Triple classification aims to judge whether a given triple (h, r, t) is correct or not with respect to the knowledge graph.
Benchmarks
These leaderboards are used to track progress in Triple Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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YAGO
CoDEx Medium
CoDEx Small
CoDEx Large
Latest papers
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
Graph data structures are widely used to store relational information between several entities.
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs
Most knowledge graph completion (KGC) methods learn latent representations of entities and relations of a given graph by mapping them into a vector space.
Exploring Large Language Models for Knowledge Graph Completion
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness.
Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
wzh-nlp/ookg
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Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs.
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks.
Knowledge Graph Refinement based on Triplet BERT-Networks
This paper adopts a transformer-based triplet network creating an embedding space that clusters the information about an entity or relation in the KG.
Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision
yur7nd/ptss
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The majority of knowledge graph embedding techniques treat entities and predicates as separate embedding matrices, using aggregation functions to build a representation of the input triple.
GreenKGC: A Lightweight Knowledge Graph Completion Method
Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs).
Language Models as Knowledge Embeddings
neph0s/lmke
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In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods.
Differentially Private Federated Knowledge Graphs Embedding
However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make full use of the data from different knowledge domains while preserving the privacy of exchanged data.
