Entity Linking for Short Text Using Structured Knowledge Graph via Multi-Grained Text Matching

2020 ◽  
Author(s):  
Binxuan Huang ◽  
Han Wang ◽  
Tong Wang ◽  
Yue Liu ◽  
Yang Liu
Keyword(s):  
Knowledge Graph ◽  
Entity Linking ◽  
Short Text ◽  
Structured Knowledge ◽  
Text Matching

scholarly journals News image-text matching with news knowledge graph

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Zhao Yumeng ◽  
Yun Jing ◽  
Gao Shuo ◽  
Liu Limin
Keyword(s):  
Knowledge Graph ◽  
Text Matching

scholarly journals S $$^3$$ 3 -TM: scalable streaming short text matching

2015 ◽  
Vol 24 (6) ◽  
pp. 849-866 ◽  
Author(s):  
Fuat Basık ◽  
Buğra Gedik ◽  
Hakan Ferhatosmanoğlu ◽  
Mert Emin Kalender
Keyword(s):  
Short Text ◽  
Scalable Streaming ◽  
Text Matching

scholarly journals Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

2021 ◽  
Vol 2083 (4) ◽  
pp. 042044
Author(s):  
Zuhua Dai ◽  
Yuanyuan Liu ◽  
Shilong Di ◽  
Qi Fan
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Structural Information ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Text Data ◽  
Short Text ◽  
Fine Grained ◽  
Syntactic Information ◽  
Text Information

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.

scholarly journals Deep Semantic Match Model for Entity Linking Using Knowledge Graph and Text

2018 ◽  
Vol 129 ◽  
pp. 110-114 ◽  
Author(s):  
Angen Luo ◽  
Sheng Gao ◽  
Yajing Xu
Keyword(s):  
Knowledge Graph ◽  
Entity Linking ◽  
Semantic Match ◽  
Match Model

A domain knowledge graph construction method based on Wikipedia

2020 ◽  
pp. 016555152093251
Author(s):  
Haoze Yu ◽  
Haisheng Li ◽  
Dianhui Mao ◽  
Qiang Cai
Keyword(s):  
Domain Knowledge ◽  
Multiple Scale ◽  
Construction Method ◽  
Knowledge Graph ◽  
Relationship Extraction ◽  
Proposed Model ◽  
Extraction Algorithm ◽  
Structured Knowledge ◽  
Extraction Model ◽  
Domain Independent

In order to achieve real-time updating of the domain knowledge graph and improve the relationship extraction ability in the construction process, a domain knowledge graph construction method is proposed. Based on the structured knowledge in Wikipedia’s classification system, we acquire concepts and instances contained in subject areas. A relationship extraction algorithm based on co-word analysis is intended to extract the classification relationships in semi-structured open labels. A Bi-GRU remote supervised relationship extraction model based on a multiple-scale attention mechanism and an improved cross-entropy loss function is proposed to obtain the non-classification relationships of concepts in unstructured texts. Experiments show that the proposed model performs better than the existing methods. Based on the obtained concepts, instances and relationships, a domain knowledge graph is constructed and the domain-independent nodes and relationships contained in them are removed through a vector variance algorithm. The effectiveness of the proposed method is verified by constructing a food domain knowledge graph based on Wikipedia.

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