Knowledge representation learning(KRL) maps entities and relations onto a low dimensional vector space by capturing its semantic meaning. KRL is also called multi-relation learning or statistical relational learning. The recent advancement in distributed representation learning can be categorized into four scopes

 - Representation Space 
 - Scoring Function 
 - Encoding Models 
 - Embedding with Auxiliary Information


Knowledge Representation Learning /Knowledge Graph Embedding

Knowledge acquisition aims to build new knowledge graphs from structured ,semi structured and unstructured text. It also helps to complete existing knowledge graphs, discover and recognize entities and relations. Three categories of knowledge acquisition techniques are 

 - knowledge  graph completion 
 - entity discovery
 - relation extraction.


Knowledge Acquisition

Temporal knowledge graph takes consideration of  temporal knowledge into KRL and KGC. The different areas where temporal information accounted are

 - Temporal Information Embedding
 - Entity Dynamics 
 - Temporal Relational Dependency
 - Temporal Logical Reasoning 


Temporal Knowledge Graph

The application of structured data in knowledge graphs are useful in applications like link prediction, entity prediction ,relation extraction ,question answering applications and recommendation systems. The various applications of knowledge graphs are  in 

 - Language Representation Learning
 - Question Answering 
 - Recommender Systems


Knowledge Aware Applications

Research on knowledge graph are categorized into

 - Knowledge representation learning
 - Knowledge acquisition
 - Temporal knowledge graph
 - Knowledge -aware applications


San Jose State University

University of Michigan - Ann Arbor

Knowledge graph utilizes NLP to construct a view of nodes and edges through the process of semantic enrichment.

Knowledge Graph in NLP

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Entities are real world objects or abstract concepts
Examples are person, books, movies, physics, theory of relativity etc

Entities are represented as graphs and relations are represented as edges.


Entities and Relations  

Knowledge graph is synonymous to knowledge base, where the latter have proper semantics connecting facts and their relations through semantic descriptions.

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