Knowledge Base Completion
64 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
Modeling Relational Data with Graph Convolutional Networks
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
daiquocnguyen/ConvKB
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NAACL 2018
This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps.
Canonical Tensor Decomposition for Knowledge Base Completion
facebookresearch/kbc
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ICML 2018
The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem.
Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases
This paper tackles the problem of endogenous link prediction for Knowledge Base completion.
Traversing Knowledge Graphs in Vector Space
Path queries on a knowledge graph can be used to answer compositional questions such as "What languages are spoken by people living in Lisbon?".
A survey of embedding models of entities and relationships for knowledge graph completion
Sujit-O/pykg2vec
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COLING (TextGraphs) 2020
Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.
Joint Matrix-Tensor Factorization for Knowledge Base Inference
dair-iitd/kbi
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If not, what characteristics of a dataset determine the performance of MF and TF models?
KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features.
Embedding Multimodal Relational Data for Knowledge Base Completion
In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data.
