# GREG: A Global Level Relation Extraction with Knowledge Graph Embedding
###### Abstract  
In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations--usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level 'local' relation's to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model's modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.  
relation extraction; knowledge graph; meta learning; text summarization; natural language processing; machine learning +
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relation extractions as well, since the model may misinterpret ambiguous relations or even omit them entirely if there are types of relations that only become coherent when viewed on a more global scale such as metaphors or sarcasm.  
In this article, we propose a relation extraction method that focuses on extracting global relations by utilizing knowledge graph construction and processing. The model largely consists of two parts: a context-aware long short-term memory(LSTM) [5] based relation extraction module and a knowledge graph constructor module that produces knowledge graphs from the given document. By constructing a knowledge graph from the input document, the constructed knowledge graph is used to extract the input document's global relations through graph embedding. To do this, both modules go through a synchronization process. We achieved this by training the two modules in an alternating fashion. This allows the proposed model to extract and classify local relations while concerning its possible global relations. For this research, we have constructed a new set of data consisted of 10,470 lines of documented Korean text of history [6] for this task. The dataset was constructed purely through human annotations and contains the information of both local relations per each sentence and knowledge graphs that represents each document.  
The following article will consist of the following: Section 2 to show previous or related methods as well as introduce what our improvements are. Section 3 will contain details of our proposed model's structure and process and how it was trained. In Section 4, we shall introduce our newly constructed dataset as well as comparisons from other models and analyze the results in detail, proving the viability of our proposed model. Finally, we shall summarize this article and speculate future works in Section 5.