The amount of scientific literature has been increasing exponentially year over year. This growth has made it increasingly difficult to manage scientific literature effectively. Knowledge graphs can be used to manage scientific literature efficiently and effectively.
Knowledge graphs are graphs where vertices represent documents and edges represent relationships between documents. They can be used to manage data in an efficient and effective manner. Some advantages of using knowledge graphs include:
They are easy to build using graph databases like Neo4j.
They are easy to maintain.
They can be queried effectively using query languages like SPARQL and cypher query.
Scientific researchers need to store scientific abstracts in a database and update the database periodically. Knowledge graphs can play a vital role in this management. For example, a toy knowledge graph can be made out of 10 scientific abstracts. A scientific knowledge graph can be automatically generated using various information extraction techniques. These techniques can be divided into two categories:
Supervised information extraction techniques: These techniques are used to extract useful information from unstructured text given a set of training data. In the context of knowledge graphs, this can be done to extract entities and relations from a dataset. The main benefit of using supervised information extraction techniques is that they can achieve high accuracy.
Unsupervised information extraction techniques: These techniques can be used to extract entities and relations from unlabelled unstructured text. This makes them a valuable tool for building knowledge graphs. There are a variety of information extraction techniques that can be used, such as pattern-based techniques, statistical techniques, and machine learning techniques.