Knowledge Graph Completion using Hyper-class Information and Pre-trained Language Model

2021 ◽  
Vol 48 (11) ◽  
pp. 1228-1234
Author(s):  
Daesik Jang ◽  
Youngjoong Ko
Keyword(s):  
Language Model ◽  
Knowledge Graph ◽  
Class Information

scholarly journals Enhanced Language Model with Hybrid Knowledge Graph for Mathematical Topic Prediction

2021 ◽  
Author(s):  
Minghui Wu ◽  
Canghong Jin ◽  
Wenkang Hu ◽  
Yabo Chen
Keyword(s):  
Language Model ◽  
Real Data ◽  
Mathematical Concept ◽  
Fine Tuning ◽  
Knowledge Graph ◽  
Mathematics Knowledge ◽  
Hybrid Knowledge ◽  
Model Set ◽  
Set Up ◽  
Mathematical Topic

Understanding mathematical topics is important for both educators and students to capture latent concepts of questions, evaluate study performance, and recommend content in online learning systems. Compared to traditional text classification, mathematical topic classification has several main challenges: (1) the length of mathematical questions is relatively short; (2) there are various representations of the same mathematical concept(i.e., calculations and application); (3) the content of question is complex including algebra, geometry, and calculus. In order to overcome these problems, we propose a framework that combines content tokens and mathematical knowledge concepts in whole procedures. We embed entities from mathematics knowledge graphs, integrate entities into tokens in a masked language model, set up semantic similarity-based tasks for next-sentence prediction, and fuse knowledge vectors and token vectors during the fine-tuning procedure. We also build a Chinese mathematical topic prediction dataset consisting of more than 70,000 mathematical questions with topics. Our experiments using real data demonstrate that our knowledge graph-based mathematical topic prediction model outperforms other state-of-the-art methods.

scholarly journals DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT

2020 ◽  
Vol 10 (18) ◽  
pp. 6429
Author(s):  
SungMin Yang ◽  
SoYeop Yoo ◽  
OkRan Jeong
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Language Model ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Named Entity ◽  
Artificial Intelligence Technology

Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language. For computers to learn on their own, the skill of understanding natural language is very important. There are a wide variety of tasks involved in the field of natural language processing, but we would like to focus on the named entity registration and relation extraction task, which is considered to be the most important in understanding sentences. We propose DeNERT-KG, a model that can extract subject, object, and relationships, to grasp the meaning inherent in a sentence. Based on the BERT language model and Deep Q-Network, the named entity recognition (NER) model for extracting subject and object is established, and a knowledge graph is applied for relation extraction. Using the DeNERT-KG model, it is possible to extract the subject, type of subject, object, type of object, and relationship from a sentence, and verify this model through experiments.

scholarly journals Deep Learning-Based Knowledge Graph Generation for COVID-19

2021 ◽  
Vol 13 (4) ◽  
pp. 2276
Author(s):  
Taejin Kim ◽  
Yeoil Yun ◽  
Namgyu Kim
Keyword(s):  
Knowledge Base ◽  
Language Model ◽  
Fine Tuning ◽  
Knowledge Graph ◽  
Specific Information ◽  
Specific Knowledge ◽  
Text Documents ◽  
Domain Specific ◽  
Open Information Extraction ◽  
Domain Specific Knowledge

Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional “dictionary-based” or “supervised” knowledge graph building methods rely on predefined human-annotated resources of entities and their relationships. The cost of creating human-annotated resources is high in terms of both time and effort. This means that relying on human-annotated resources will not allow rapid adaptability in describing new knowledge when domain-specific information is added or updated very frequently, such as with the recent coronavirus disease-19 (COVID-19) pandemic situation. Therefore, in this study, we propose an Open Information Extraction (OpenIE) system based on unsupervised learning without a pre-built dataset. The proposed method obtains knowledge from a vast amount of text documents about COVID-19 rather than a general knowledge base and add this to the existing knowledge graph. First, we constructed a COVID-19 entity dictionary, and then we scraped a large text dataset related to COVID-19. Next, we constructed a COVID-19 perspective language model by fine-tuning the bidirectional encoder representations from transformer (BERT) pre-trained language model. Finally, we defined a new COVID-19-specific knowledge base by extracting connecting words between COVID-19 entities using the BERT self-attention weight from COVID-19 sentences. Experimental results demonstrated that the proposed Co-BERT model outperforms the original BERT in terms of mask prediction accuracy and metric for evaluation of translation with explicit ordering (METEOR) score.

scholarly journals Chinese Named Entity Recognition Method in History and Culture Field Based on BERT

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Shuang Liu ◽  
Hui Yang ◽  
Jiayi Li ◽  
Simon Kolmanič
Keyword(s):  
Short Term Memory ◽  
Conditional Random Field ◽  
Language Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Recognition Method ◽  
Short Term ◽  
Named Entity ◽  
Long Short Term Memory

AbstractWith rapid development of the Internet, people have undergone tremendous changes in the way they obtain information. In recent years, knowledge graph is becoming a popular tool for the public to acquire knowledge. For knowledge graph of Chinese history and culture, most researchers adopted traditional named entity recognition methods to extract entity information from unstructured historical text data. However, the traditional named entity recognition method has certain defects, and it is easy to ignore the association between entities. To extract entities from a large amount of historical and cultural information more accurately and efficiently, this paper proposes one named entity recognition model combining Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF). First, a BERT pre-trained language model is used to encode a single character to obtain a vector representation corresponding to each character. Then one Bidirectional Long Short-Term Memory (BiLSTM) layer is applied to semantically encode the input text. Finally, the label with the highest probability is output through the Conditional Random Field (CRF) layer to obtain each character’s category. This model uses the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model to replace the static word vectors trained in the traditional way. In comparison, the BERT pre-trained language model can dynamically generate semantic vectors according to the context of words, which improves the representation ability of word vectors. The experimental results prove that the model proposed in this paper has achieved excellent results in the task of named entity recognition in the field of historical culture. Compared with the existing named entity identification methods, the precision rate, recall rate, and $$F_1$$ F 1 value have been significantly improved.

scholarly journals Relation Extraction using Language Model Based on Knowledge Graph

2020 ◽  
Vol 1624 ◽  
pp. 022037
Author(s):  
Chengli Xing ◽  
Xueyang Liu ◽  
Dongdong Du ◽  
Wenhui Hu ◽  
Minghui Zhang
Keyword(s):  
Language Model ◽  
Relation Extraction ◽  
Knowledge Graph ◽  
Model Based

scholarly journals Representation Learning of Knowledge Graphs with Embedding Subspaces

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Chunhua Li ◽  
Xuefeng Xian ◽  
Xusheng Ai ◽  
Zhiming Cui
Keyword(s):  
Language Model ◽  
Representation Learning ◽  
Training Data ◽  
Knowledge Graph ◽  
Two Phase ◽  
Long Tail ◽  
Relational Knowledge ◽  
Supervised Methods ◽  
Knowledge Graphs ◽  
The Cost

Most of the existing knowledge graph embedding models are supervised methods and largely relying on the quality and quantity of obtainable labelled training data. The cost of obtaining high quality triples is high and the data sources are facing a serious problem of data sparsity, which may result in insufficient training of long-tail entities. However, unstructured text encoding entities and relational knowledge can be obtained anywhere in large quantities. Word vectors of entity names estimated from the unlabelled raw text using natural language model encode syntax and semantic properties of entities. Yet since these feature vectors are estimated through minimizing prediction error on unsupervised entity names, they may not be the best for knowledge graphs. We propose a two-phase approach to adapt unsupervised entity name embeddings to a knowledge graph subspace and jointly learn the adaptive matrix and knowledge representation. Experiments on Freebase show that our method can rely less on the labelled data and outperforms the baselines when the labelled data is relatively less. Especially, it is applicable to zero-shot scenario.

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