Knowledge Base Completion

48 papers with code • 0 benchmarks • 1 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.

Datasets


Greatest papers with code

End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion

JD-AI-Research-Silicon-Valley/SACN 11 Nov 2018

The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure.

Knowledge Base Completion Knowledge Graph Embedding +2

Embedding Multimodal Relational Data for Knowledge Base Completion

pouyapez/mkbe EMNLP 2018

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.

Imputation Knowledge Base Completion +1

STransE: a novel embedding model of entities and relationships in knowledge bases

datquocnguyen/STransE NAACL 2016

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks.

Knowledge Base Completion Link Prediction +1

Scene Graph Prediction with Limited Labels

vincentschen/limited-label-scene-graphs ICCV 2019

All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each.

Knowledge Base Completion Question Answering +2

Tensor Decompositions for temporal knowledge base completion

facebookresearch/tkbc ICLR 2020

Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.

Knowledge Base Completion Link Prediction +2

Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

takuo-h/GNN-for-OOKB 18 Jun 2017

Knowledge base completion (KBC) aims to predict missing information in a knowledge base. In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC:how to answer queries concerning test entities not observed at training time.

Knowledge Base Completion Transfer Learning

Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study

rahuln/lm-bio-kgc 17 Jun 2021

Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes.

Knowledge Base Completion Knowledge Graphs +1

BoxE: A Box Embedding Model for Knowledge Base Completion

ralphabb/BoxE NeurIPS 2020

Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB).

Knowledge Base Completion Knowledge Graphs +1

Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision

dair-iitd/kbi ACL 2018

State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings.

Entity Typing Knowledge Base Completion +4

Joint Matrix-Tensor Factorization for Knowledge Base Inference

dair-iitd/kbi 2 Jun 2017

If not, what characteristics of a dataset determine the performance of MF and TF models?

Knowledge Base Completion Knowledge Base Population +3