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 |
|---|---|---|---|---|---|
|
YAGO
CoDEx Medium
CoDEx Small
CoDEx Large
Most implemented papers
TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces
SoyeonTiffanyMin/TransINT
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AKBC 2020
We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space.
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.
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.
GreenKGC: A Lightweight Knowledge Graph Completion Method
Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs).
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.
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.
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.
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.
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.
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.
