Knowledge Base Completion
65 papers with code • 0 benchmarks • 2 datasets
Knowledge base completion is the task which automatically infers missing facts by reasoning about the information already present in the knowledge base. A knowledge base is a collection of relational facts, often represented in the form of "subject", "relation", "object"-triples.
Benchmarks
These leaderboards are used to track progress in Knowledge Base Completion
Aristo-v4
ZeroKBC
Most implemented papers
Scene Graph Prediction with Limited Labels
All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each.
Path Ranking with Attention to Type Hierarchies
wliu88/AttentivePathRanking
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The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base.
Neural Consciousness Flow
Instead, inspired by the consciousness prior proposed by Yoshua Bengio, we explore reasoning with the notion of attentive awareness from a cognitive perspective, and formulate it in the form of attentive message passing on graphs, called neural consciousness flow (NeuCFlow).
Modeling Paths for Explainable Knowledge Base Completion
JosuaStadelmaier/CPM
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WS 2019
A common approach in knowledge base completion (KBC) is to learn representations for entities and relations in order to infer missing facts by generalizing existing ones.
Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning
netpaladinx/DPMPN
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ICLR 2020
We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning.
Commonsense Knowledge Base Completion with Structural and Semantic Context
allenai/commonsense-kg-completion
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Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1. 5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency.
OxKBC: Outcome Explanation for Factorization Based Knowledge Base Completion
State-of-the-art models for Knowledge Base Completion (KBC) are based on tensor factorization (TF), e. g, DistMult, ComplEx.
Knowledge Base Completion for Constructing Problem-Oriented Medical Records
Both electronic health records and personal health records are typically organized by data type, with medical problems, medications, procedures, and laboratory results chronologically sorted in separate areas of the chart.
Regex Queries over Incomplete Knowledge Bases
dair-iitd/kbi-regex
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AKBC 2021
In response, we develop RotatE-Box -- a novel combination of RotatE and box embeddings.
Knowledge Base Completion: Baseline strikes back (Again)
Most existing methods train with a small number of negative samples for each positive instance in these datasets to save computational costs.
