Knowledge Base Population

23 papers with code • 0 benchmarks • 2 datasets

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

Most implemented papers

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?

Position-aware Attention and Supervised Data Improve Slot Filling

yuhaozhang/tacred-relation EMNLP 2017

The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.

Information Extraction of Clinical Trial Eligibility Criteria

facebookresearch/Clinical-Trial-Parser 12 Jun 2020

Clinical trials predicate subject eligibility on a diversity of criteria ranging from patient demographics to food allergies.

Zero-shot Slot Filling with DPR and RAG

IBM/retrieve-write-slot-filling 17 Apr 2021

Recently, there has been a promising direction in evaluating language models in the same way we would evaluate knowledge bases, and the task of slot filling is the most suitable to this intent.

DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

zjunlp/deepke 10 Jan 2022

We present a new open-source and extensible knowledge extraction toolkit, called DeepKE (Deep learning based Knowledge Extraction), supporting standard fully supervised, low-resource few-shot and document-level scenarios.

TIMEN: An Open Temporal Expression Normalisation Resource

leondz/timen LREC 2012

In this paper, we present TIMEN, a community-driven tool for temporal expression normalisation.

Entity Disambiguation with Web Links

wikilinks/nel TACL 2015

Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories.

SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

LIAAD/KeywordExtractor-Datasets SEMEVAL 2017

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.