Survey on Embedding Models for Knowledge Graph and its Applications
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909169379966976 |
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| author | Pote, Manita |
| author_facet | Pote, Manita |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09167 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Survey on Embedding Models for Knowledge Graph and its Applications Pote, Manita Social and Information Networks Artificial Intelligence 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. |
| title | Survey on Embedding Models for Knowledge Graph and its Applications |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2404.09167 |