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
Latest papers with no code
Projected Canonical Decomposition for Knowledge Base Completion
However, as we show in this paper through experiments on standard benchmarks of link prediction in knowledge bases, ComplEx, a variant of CP, achieves similar performances to recent approaches based on Tucker decomposition on all operating points in terms of number of parameters.
Text-Based Joint Prediction of Numeric and Categorical Attributes of Entities in Knowledge Bases
Our analysis indicates that this is the case because categorical attributes, many of which describe membership in various classes, provide useful {`}background knowledge{'} for numeric prediction, while this is true to a lesser degree in the inverse direction.
Populating Web Scale Knowledge Graphs using Distantly Supervised Relation Extraction and Validation
In addition to that, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations.
Distributional Negative Sampling for Knowledge Base Completion
State-of-the-art approaches for Knowledge Base Completion (KBC) exploit deep neural networks trained with both false and true assertions: positive assertions are explicitly taken from the knowledge base, whereas negative ones are generated by random sampling of entities.
HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion
Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in data.
Meta Reasoning over Knowledge Graphs
The ability to reason over learned knowledge is an innate ability for humans and humans can easily master new reasoning rules with only a few demonstrations.
Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns
In this paper, we present a simple and effective method of extracting new facts with a pre-specified binary relationship type from the biomedical literature, without requiring any training data or hand-crafted rules.
Neural Markov Logic Networks
We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic.
Riemannian TransE: Multi-relational Graph Embedding in Non-Euclidean Space
Multi-relational graph embedding which aims at achieving effective representations with reduced low-dimensional parameters, has been widely used in knowledge base completion.
