# GREG: A Global Level Relation Extraction with Knowledge Graph Embedding
## 2 Related Works  
### Relation Extraction  
Relation Extraction can largely be divided into two types: global level relation extraction and mention-level relation extraction [7], while the former deals with 'global' level relations that span across multiple relations and large bodies of text, and mention-level 'local' relations are smaller scale relations expressed directly within short sentences.  
The best example that shows the difference between the two can be shown by syllogism. Given two sentences \"All cars have wheels.' and \"A truck is a car\", a local relation extraction will only be able to extract the information \((Car,possess,Wheel)\) and \((Truck,is,Car)\). Thus, it will never be able to extract and represent what the two sentences combined mean: \"A truck has wheels\". On the other hand, global relation extraction would consider these global level relations and would be able to extract the information \((Truck,Possess,Wheel)\).  
Currently, most relation extraction modules that yield high performances usually only deal with 'local relations'. However, when representing a large amount of text such as a document, representations only through local relations are thoroughly limited. Considering the fact that the most valuable information within documents is those that have been described in detail and are spanned across the document, the importance of global level relation extraction is not something to be looked over so lightly.  
However, the task itself is nowhere near simple. The main cause that prevents current state-of-the-art local relation extraction methods to properly perform global relation extraction is due to the bottleneck problem [8]. With so much information to process for a singular relation, the loss of information is inevitable--thus hindering the model's capability to properly judge the relation or capture it at all. On the other hand, if the model is focused only on extracting global relations, then, in an attempt to avoid the bottleneck problem [8], its performance against local relation extraction drops due to the sparsity of information [9]. Previous attempts on global relation extraction have tried several different methods to tackle this problem. For instance, [PERSON] et al. [10] attempted this by simply using co-references to gain access to other sentences without actually having to model them or [PERSON] et al. [11] who applied distant supervision to general global relation extraction as well.  
One of the more recent approaches includes [PERSON] et al. [3] attempting global relation extraction by continuously retaining the entity representations learned from the entire text as input, allowing the extracted information from the whole text to be easily extended to other sentences as well.  
In this research, we propose a divide and conquer approach to the task. By using two separate modules to perform local relation extraction and global relation extraction separately, we entirely avoid the stated bottleneck problem [8] and the following dilemma. As global relations can largely be seen as an amalgamation of multiple local relations linked together, the proposed model aims to extract local relations through the first module and converts them into a knowledge graph. The constructed knowledge graph is then analyzed to extract global relations.