Semantic Enrichment of an ECEC Platform Knowledge Base

2012 ◽  
Author(s):  
Victor A. Roris ◽  
Ruben Miguez ◽  
Juan M. Santos
Keyword(s):  
Knowledge Base ◽  
Semantic Enrichment

scholarly journals Patient and Graph Embeddings for Predictive Diagnosis of Drug Iatrogenesis

2021 ◽  
Author(s):  
Lina F. Soualmia ◽  
Vincent Lafon ◽  
Stéfan J. Darmoni
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Knowledge Base ◽  
Learning Algorithms ◽  
Semantic Networks ◽  
Graph Embeddings ◽  
Predictive Diagnosis ◽  
Semantic Enrichment ◽  
Formal Ontologies

In the context of the IA.TROMED project we intend to develop and evaluate original algorithmic methods that will rely on semantic enrichment of embeddings by combining new deep learning algorithms, such as models founded on transformers, and symbolic artificial intelligence. The documents’ embeddings, the graphs’ embeddings of biomedical concepts, and patients’ embeddings, all of them semantically enriched with aligned formal ontologies and semantic networks, will constitute a layer that will play the role of a queryable and searchable knowledge base that will supply the IA.TROMED’s clinical, predictive, and iatrogenic diagnosis support module.

WinFriends: Building your English Knowledge-Base (Version 5.02)

2017 ◽  
Vol 20 (1) ◽  
pp. 208-220
Author(s):  
J. F. Coll
Keyword(s):  
Knowledge Base

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.

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