KGC adds new triples to the existing knowledge graph and also accomplishes subtasks like link prediction, entity prediction and relation prediction. KGC also learns low dimensional embedding for triple prediction with recent advancement on capturing multi step relationship paths. KGC also do triple classification which evaluates the correctness of factual triple.

 - Embedding-based Models:
 - Relation Path Reasoning
 - RL-based Path Finding
 - Rule-based Reasoning
 - Meta Relational Learning:
 - Triple Classification


Knowledge Graph Completion 

Learning of entity related knowledge from text under various settings. Entity-based knowledge acquisition can be categorized into 

 - Entity recognition
 - Entity disambiguation 
 - Entity typing
 - Entity alignment


Entity Discovery

Unknown relations are extracted from text  and added to the knowledge graph.The various methods used for relation extraction are 

 - Neural Relation Extraction
 - Attention Mechanism
 - Graph Convolutional Networks 
 - Adversarial Training
 - Reinforcement Learning
 - Other Advances
 - Joint Entity and Relation Extraction


Relation Extraction

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.


San Jose State University

Research on knowledge graph are categorized into

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


Research on Knowledge Graph in NLP

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

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

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


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