Knowledge Base Completion Using Matrix Factorization

2015 ◽  
pp. 256-267 ◽  
Author(s):  
Wenqiang He ◽  
Yansong Feng ◽  
Lei Zou ◽  
Dongyan Zhao
Keyword(s):  
Knowledge Base ◽  
Matrix Factorization

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 Lexical Sememe Prediction via Word Embeddings and Matrix Factorization

2017 ◽  
Author(s):  
Ruobing Xie ◽  
Xingchi Yuan ◽  
Zhiyuan Liu ◽  
Maosong Sun
Keyword(s):  
Knowledge Base ◽  
Real World ◽  
Matrix Factorization ◽  
Knowledge Bases ◽  
Semantic Relations ◽  
Linguistic Knowledge ◽  
Word Embeddings ◽  
New Words ◽  
First Time

Sememes are defined as the minimum semantic units of human languages. People have manually annotated lexical sememes for words and form linguistic knowledge bases. However, manual construction is time-consuming and labor-intensive, with significant annotation inconsistency and noise. In this paper, we for the first time explore to automatically predict lexical sememes based on semantic meanings of words encoded by word embeddings. Moreover, we apply matrix factorization to learn semantic relations between sememes and words. In experiments, we take a real-world sememe knowledge base HowNet for training and evaluation, and the results reveal the effectiveness of our method for lexical sememe prediction. Our method will be of great use for annotation verification of existing noisy sememe knowledge bases and annotation suggestion of new words and phrases.

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
Sign in / Sign up

Export Citation Format

Share Document