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
Latest papers with no code
Progressive Knowledge Graph Completion
In this paper, we investigate three crucial processes relevant to real-world construction scenarios: (a) the verification process, which arises from the necessity and limitations of human verifiers; (b) the mining process, which identifies the most promising candidates for verification; and (c) the training process, which harnesses verified data for subsequent utilization; in order to achieve a transition toward more realistic challenges.
Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
Extensional knowledge provides information about the concrete instances that belong to specific concepts in the ontology, while intensional knowledge details inherent properties, characteristics, and semantic associations among concepts.
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
In this work, we evaluate a performance prediction model built on top of the extension of the recently proposed OPTION ontology.
OneRel:Joint Entity and Relation Extraction with One Module in One Step
Joint entity and relation extraction is an essential task in natural language processing and knowledge graph construction.
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training
Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs.
Triple Classification for Scholarly Knowledge Graph Completion
Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications.
Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation
In this paper, we propose a heterogeneous knowledge language model (\textbf{HKLM}), a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text.
Membership Inference Attacks on Knowledge Graphs
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework
A generative network (GN) takes two elements of a (subject, predicate, object) triple as input and generates the vector representation of the missing element.
Revisiting Evaluation of Knowledge Base Completion Models
To address these issues, we gather a semi-complete KG referred as YAGO3-TC, using a random subgraph from the test and validation data of YAGO3-10, which enables us to compute accurate triple classification accuracy on this data.
