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.
Benchmarks
These leaderboards are used to track progress in Knowledge Base Population
Most implemented papers
Joint Matrix-Tensor Factorization for Knowledge Base Inference
If not, what characteristics of a dataset determine the performance of MF and TF models?
Position-aware Attention and Supervised Data Improve Slot Filling
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
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
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
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
In this paper, we present TIMEN, a community-driven tool for temporal expression normalisation.
Entity Disambiguation with Web Links
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
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.