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
KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Knowledge graph completion aims to perform link prediction between entities.
Knowledge Representation Learning: A Quantitative Review
thunlp/OpenKE
•
•
Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks.
Reasoning on Knowledge Graphs with Debate Dynamics
The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively.
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty.
Image-embodied Knowledge Representation Learning
More specifically, we first construct representations for all images of an entity with a neural image encoder.
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence
Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.
Differentiating Concepts and Instances for Knowledge Graph Embedding
davidlvxin/TransC
•
•
EMNLP 2018
Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances.
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
daiquocnguyen/R-MeN
•
•
ACL 2020
Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems.
On the Role of Conceptualization in Commonsense Knowledge Graph Construction
mutiann/ccc
•
•
Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs as they consist of much larger number of nodes formed by loosely-structured text, which, though, enables them to handle highly diverse queries in natural language related to commonsense, leads to unique challenges for automatic KG construction methods.
