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Knowledge base population is the task of filling the incomplete elements of a given knowledge base by automatically processing a large corpus of text.

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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.

KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPHS RELATION EXTRACTION SLOT FILLING

KnowledgeNet: A Benchmark Dataset for Knowledge Base Population

IJCNLP 2019 diffbot/knowledge-net

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

TACL 2015 wikilinks/nel

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

SEMEVAL 2017 LIAAD/KeywordExtractor-Datasets

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.

KNOWLEDGE BASE POPULATION

Discovering Implicit Knowledge with Unary Relations

ACL 2018 IBM/cc-dbp

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

COLING 2018 onurgu/joint-ner-and-md-tagger

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

ACL 2018 dair-iitd/kbi

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