Knowledge Base Population Edit Task
Natural Language Processing
About
Knowledge base population is the task of filling the incomplete elements of a given knowledge base by automatically processing a large corpus of text.
Benchmarks
Subtasks
Datasets
Greatest papers with code
Position-aware Attention and Supervised Data Improve Slot Filling
EMNLP 2017
• yuhaozhang/tacred-relation
• 
The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.
Ranked #3 on
Relation Extraction
on Re-TACRED
KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPHS RELATION EXTRACTION SLOT FILLING
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web.
ENTITY LINKING KNOWLEDGE BASE POPULATION RELATION EXTRACTION
Entity Disambiguation with Web Links
Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories.
ENTITY DISAMBIGUATION ENTITY LINKING KNOWLEDGE BASE POPULATION
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials.
Deep Neural Networks for Relation Extraction
• nusnlp/PtrNetDecoding4JERE
•
Relation extraction from text is an important task for automatic knowledge base population.
JOINT ENTITY AND RELATION EXTRACTION KNOWLEDGE BASE POPULATION
Discovering Implicit Knowledge with Unary Relations
State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence.
KNOWLEDGE BASE POPULATION NATURAL LANGUAGE INFERENCE RELATION EXTRACTION
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags
In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses.
ENTITY LINKING KNOWLEDGE BASE POPULATION MORPHOLOGICAL DISAMBIGUATION NAMED ENTITY RECOGNITION RELATION EXTRACTION WORD EMBEDDINGS
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision
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 KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPH COMPLETION LINK PREDICTION TYPE PREDICTION
Joint Matrix-Tensor Factorization for Knowledge Base Inference
• dair-iitd/kbi
•
If not, what characteristics of a dataset determine the performance of MF and TF models?
KNOWLEDGE BASE COMPLETION KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPH EMBEDDING MATRIX FACTORIZATION / DECOMPOSITION