TY - GEN
T1 - Enhancing CNN Based Knowledge Graph Embedding Algorithms Using Auxiliary Vectors
T2 - 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020
AU - Pornprasit, Chanathip
AU - Kiattipadungkul, Pattararat
AU - Duangkaew, Peeranut
AU - Tuarob, Suppawong
AU - Noraset, Thanapon
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Knowledge graphs (KGs) have been utilized by various business fields. One example is Google that stores data in knowledge graphs for searching and retrieval tasks. Even though these graphs have reached an impressive size, they are far from completeness. Missing relations in knowledge graphs is a severe problem for algorithms that operate over knowledge graphs. There are many researchers trying to develop knowledge graph embedding methods so that they can handle different types of relations. ConvKB is one of the knowledge graph embedding methods that utilize convolution neural networks (CNN). However, this method lacks the ability to handle symmetric relations. Being inspired by this limitation, we would like to enhance this method by proposing ConvKB+, which is obtained by modifying ConvKB's CNN structure and introducing an additional relation vector. Our experiment results show that our method outperforms ConvKB by achieving higher MRR on some symmetric relations of the WN18RR dataset.
AB - Knowledge graphs (KGs) have been utilized by various business fields. One example is Google that stores data in knowledge graphs for searching and retrieval tasks. Even though these graphs have reached an impressive size, they are far from completeness. Missing relations in knowledge graphs is a severe problem for algorithms that operate over knowledge graphs. There are many researchers trying to develop knowledge graph embedding methods so that they can handle different types of relations. ConvKB is one of the knowledge graph embedding methods that utilize convolution neural networks (CNN). However, this method lacks the ability to handle symmetric relations. Being inspired by this limitation, we would like to enhance this method by proposing ConvKB+, which is obtained by modifying ConvKB's CNN structure and introducing an additional relation vector. Our experiment results show that our method outperforms ConvKB by achieving higher MRR on some symmetric relations of the WN18RR dataset.
KW - Convolution Neural Networks
KW - Knowledge Graph Embedding
KW - Wordnet
UR - https://www.scopus.com/pages/publications/85091888072
U2 - 10.1109/ECTI-CON49241.2020.9158288
DO - 10.1109/ECTI-CON49241.2020.9158288
M3 - Conference contribution
AN - SCOPUS:85091888072
T3 - 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020
SP - 763
EP - 766
BT - 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 June 2020 through 27 June 2020
ER -