Survey on Embedding Models for Knowledge Graph and its Applications
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Abstract (arXiv)
Knowledge Graph (KG) is a graph based data structure to represent facts of the world where nodes represent real world entities or abstract concept and edges represent relation between the entities. Graph as representation for knowledge has several drawbacks like data sparsity, computational complexity and manual feature engineering. Knowledge Graph embedding tackles the drawback by representing entities and relation in low dimensional vector space by capturing the semantic relation between them. There are different KG embedding models. Here, we discuss translation based and neural network based embedding models which differ based on semantic property, scoring function and architecture they use. Further, we discuss application of KG in some domains that use deep learning models and leverage social media data.
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References · 11
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
- 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
- 2016Complex Embeddings for Simple Link Predictioncited by 902
- 2017A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Networkcited by 143
- 2017KBGAN: Adversarial Learning for Knowledge Graph Embeddingscited by 62
- 2013A Semantic Matching Energy Function for Learning with Multi-relational Datacited by 61
- 2016Probabilistic Reasoning via Deep Learning: Neural Association Modelscited by 20
- 2017Finding Streams in Knowledge Graphs to Support Fact Checkingcited by 17
- 2020Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Mediacited by 12
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