A review of knowledge graph embedding methods of TransE, TransH and TransR for missing links
Knowledge representation and reasoning require knowledge graph embedding as it is crucial in the area. It involves mapping entities and relationships from a knowledge graph into vectors of lower dimensions that are continuous in nature. This encoding enables machine learning algorithms to effectivel...
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IEEE
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40733/1/A%20Review%20of%20Knowledge%20Graph%20Embedding%20Methods.pdf http://umpir.ump.edu.my/id/eprint/40733/2/A%20review%20of%20knowledge%20graph%20embedding%20methods%20of%20TransE.pdf http://umpir.ump.edu.my/id/eprint/40733/ https://doi.org/10.1109/ICSECS58457.2023.10256354 |
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| _version_ | 1831530164213776384 |
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| author | Salwana, Mohamad Noor Azida, Sahabudin Nor Syahidatul Nadiah, Ismail Ily Amalina, Ahmad Sabri |
| author_facet | Salwana, Mohamad Noor Azida, Sahabudin Nor Syahidatul Nadiah, Ismail Ily Amalina, Ahmad Sabri |
| author_sort | Salwana, Mohamad |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Knowledge representation and reasoning require knowledge graph embedding as it is crucial in the area. It involves mapping entities and relationships from a knowledge graph into vectors of lower dimensions that are continuous in nature. This encoding enables machine learning algorithms to effectively reason and make predictions on graph-structured data. This review article offers an overview and critical analysis specifically about the methods of knowledge graph embedding which are TransE, TransH, and TransR. The key concepts, methodologies, strengths, and limitations of these methods, along with examining their applications and experiments conducted by existing researchers have been studied. The motivation to conduct this study is to review the well-known and most applied knowledge embedding methods and compare the features of those methods so that a comprehensive resource for researchers and practitioners interested in delving into knowledge graph embedding techniques is delivered. |
| format | Conference or Workshop Item |
| id | my.ump.umpir.40733 |
| institution | Universiti Malaysia Pahang |
| language | en en |
| publishDate | 2023 |
| publisher | IEEE |
| record_format | eprints |
| spelling | my.ump.umpir.407332024-03-22T03:35:47Z http://umpir.ump.edu.my/id/eprint/40733/ A review of knowledge graph embedding methods of TransE, TransH and TransR for missing links Salwana, Mohamad Noor Azida, Sahabudin Nor Syahidatul Nadiah, Ismail Ily Amalina, Ahmad Sabri QA75 Electronic computers. Computer science Knowledge representation and reasoning require knowledge graph embedding as it is crucial in the area. It involves mapping entities and relationships from a knowledge graph into vectors of lower dimensions that are continuous in nature. This encoding enables machine learning algorithms to effectively reason and make predictions on graph-structured data. This review article offers an overview and critical analysis specifically about the methods of knowledge graph embedding which are TransE, TransH, and TransR. The key concepts, methodologies, strengths, and limitations of these methods, along with examining their applications and experiments conducted by existing researchers have been studied. The motivation to conduct this study is to review the well-known and most applied knowledge embedding methods and compare the features of those methods so that a comprehensive resource for researchers and practitioners interested in delving into knowledge graph embedding techniques is delivered. IEEE 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40733/1/A%20Review%20of%20Knowledge%20Graph%20Embedding%20Methods.pdf pdf en http://umpir.ump.edu.my/id/eprint/40733/2/A%20review%20of%20knowledge%20graph%20embedding%20methods%20of%20TransE.pdf Salwana, Mohamad and Noor Azida, Sahabudin and Nor Syahidatul Nadiah, Ismail and Ily Amalina, Ahmad Sabri (2023) A review of knowledge graph embedding methods of TransE, TransH and TransR for missing links. In: 8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25 - 27 August 2023 , Penang, Malaysia. 470 -475.. ISBN 979-835031093-1 (Published) https://doi.org/10.1109/ICSECS58457.2023.10256354 |
| spellingShingle | QA75 Electronic computers. Computer science Salwana, Mohamad Noor Azida, Sahabudin Nor Syahidatul Nadiah, Ismail Ily Amalina, Ahmad Sabri A review of knowledge graph embedding methods of TransE, TransH and TransR for missing links |
| title | A review of knowledge graph embedding methods of TransE, TransH and TransR for missing links |
| title_full | A review of knowledge graph embedding methods of TransE, TransH and TransR for missing links |
| title_fullStr | A review of knowledge graph embedding methods of TransE, TransH and TransR for missing links |
| title_full_unstemmed | A review of knowledge graph embedding methods of TransE, TransH and TransR for missing links |
| title_short | A review of knowledge graph embedding methods of TransE, TransH and TransR for missing links |
| title_sort | review of knowledge graph embedding methods of transe, transh and transr for missing links |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/40733/1/A%20Review%20of%20Knowledge%20Graph%20Embedding%20Methods.pdf http://umpir.ump.edu.my/id/eprint/40733/2/A%20review%20of%20knowledge%20graph%20embedding%20methods%20of%20TransE.pdf http://umpir.ump.edu.my/id/eprint/40733/ https://doi.org/10.1109/ICSECS58457.2023.10256354 |
| url_provider | http://umpir.ump.edu.my/ |