Relation Extraction for Knowledge Base Completion: A Supervised Approach

2018 ◽  
pp. 52-66
Author(s):  
Héctor Cerezo-Costas ◽  
Manuela Martín-Vicente
Keyword(s):  
Knowledge Base ◽  
Relation Extraction

Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference

2018 ◽  
Author(s):  
Prachi Jain ◽  
Shikhar Murty ◽  
Mausam . ◽  
Soumen Chakrabarti
Keyword(s):  
Knowledge Base ◽  
Hybrid Model ◽  
Matrix Factorization ◽  
Relation Extraction ◽  
Tensor Factorization ◽  
Evaluation Protocol

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.

scholarly journals Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Li ◽  
Guimin Huang ◽  
Jianheng Chen ◽  
Yabing Wang
Keyword(s):  
Knowledge Base ◽  
Relation Extraction ◽  
Background Knowledge ◽  
Word Embedding ◽  
Entity Recognition ◽  
Instance Selection ◽  
Attention Model ◽  
Training Tool ◽  
Knowledge Based ◽  
Proposed Model

Relation extraction is the underlying critical task of textual understanding. However, the existing methods currently have defects in instance selection and lack background knowledge for entity recognition. In this paper, we propose a knowledge-based attention model, which can make full use of supervised information from a knowledge base, to select an entity. We also design a method of dual convolutional neural networks (CNNs) considering the word embedding of each word is restricted by using a single training tool. The proposed model combines a CNN with an attention mechanism. The model inserts the word embedding and supervised information from the knowledge base into the CNN, performs convolution and pooling, and combines the knowledge base and CNN in the full connection layer. Based on these processes, the model not only obtains better entity representations but also improves the performance of relation extraction with the help of rich background knowledge. The experimental results demonstrate that the proposed model achieves competitive performance.

scholarly journals Distantly supervised Web relation extraction for knowledge base population

Semantic Web ◽  
2016 ◽  
Vol 7 (4) ◽  
pp. 335-349 ◽  
Author(s):  
Isabelle Augenstein ◽  
Diana Maynard ◽  
Fabio Ciravegna
Keyword(s):  
Knowledge Base ◽  
Relation Extraction ◽  
Base Population ◽  
Knowledge Base Population

Chinese Open Relation Extraction and Knowledge Base Establishment

2018 ◽  
Vol 17 (3) ◽  
pp. 1-22 ◽  
Author(s):  
Shengbin Jia ◽  
Shijia E ◽  
Maozhen Li ◽  
Yang Xiang
Keyword(s):  
Knowledge Base ◽  
Relation Extraction

scholarly journals Deep learning models in detection of dietary supplement adverse event signals from Twitter

JAMIA Open ◽  
2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Yefeng Wang ◽  
Yunpeng Zhao ◽  
Dalton Schutte ◽  
Jiang Bian ◽  
Rui Zhang
Keyword(s):  
Adverse Event ◽  
Deep Learning ◽  
Knowledge Base ◽  
Dietary Supplement ◽  
Relation Extraction ◽  
Learning Models ◽  
Concept Extraction ◽  
Annotation Guideline ◽  
End To End ◽  
Ae Signals

Abstract Objective The objective of this study is to develop a deep learning pipeline to detect signals on dietary supplement-related adverse events (DS AEs) from Twitter. Materials and Methods We obtained 247 807 tweets ranging from 2012 to 2018 that mentioned both DS and AE. We designed a tailor-made annotation guideline for DS AEs and annotated biomedical entities and relations on 2000 tweets. For the concept extraction task, we fine-tuned and compared the performance of BioClinical-BERT, PubMedBERT, ELECTRA, RoBERTa, and DeBERTa models with a CRF classifier. For the relation extraction task, we fine-tuned and compared BERT models to BioClinical-BERT, PubMedBERT, RoBERTa, and DeBERTa models. We chose the best-performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (ie, iDISK). Results DeBERTa-CRF model outperformed other models in the concept extraction task, scoring a lenient microaveraged F1 score of 0.866. RoBERTa model outperformed other models in the relation extraction task, scoring a lenient microaveraged F1 score of 0.788. The end-to-end pipeline built on these 2 models was able to extract DS indication and DS AEs with a lenient microaveraged F1 score of 0.666. Conclusion We have developed a deep learning pipeline that can detect DS AE signals from Twitter. We have found DS AEs that were not recorded in an existing knowledge base (iDISK) and our proposed pipeline can as sist DS AE pharmacovigilance.

scholarly journals Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution

2021 ◽  
Author(s):  
Severine Verlinden ◽  
Klim Zaporojets ◽  
Johannes Deleu ◽  
Thomas Demeester ◽  
Chris Develder
Keyword(s):  
Knowledge Base ◽  
Relation Extraction ◽  
Coreference Resolution ◽  
End To End
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