Relational facts structure knowledge in the format of triplets. These facts represent an important role of human knowledge and are explicit or implicit in text. An example of an explicit representation is the sentence “Bill Gates co-founded Microsoft” which indicates the fact (Microsoft Inc., founded by, Bill Gates). An example of an implicit representation is that (USA, contains, New York) can be inferred from “Hamilton made its debut in New York, USA”. 

Relational facts

A typical RE setting is given a sentence marked by two entities, and models need to classify the sentence into one of the predefined relations. A complete relation extraction system consists of: 
- named entity recognizer to identify named entities  
- Entity linker to link entities to existing knowledge graphs 
- Relational classifier to determine relations between entities by given context
Identifying the relation is the most crucial and most difficult task because it requires models to understand the semantics of the context. Therefore, RE generally focuses on researching the relational classification side of the RE systems. 

Relation Extraction System

The following approaches have been explored for relation extraction, and these methods have bridged the gap between unstructured text and structured knowledge. 
- Pattern mining
- Statistical approaches 
- Deep learning

Pioneering Explorations of Relation Extraction

Relation extraction is an important task in IE. Relation extraction is a task which aims at extracting relational facts from plain text by classifying relations between entity mentions in text from their context after identifying them. Relation extraction can benefit down-stream applications such as knowledge graph completion, search engine, and questions answering.

Columbia University

Information extraction aims to extract structural information from unstructured text, which is a key field within natural language processing. 

Information Extraction (IE)

Han, X., Gao, T., Lin, Y., Peng, H., Yang, Y., Xiao, C., … Zhou, J. (2020). More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction. doi:10.48550/ARXIV.2004.03186


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