MDistMult: A Multiple Scoring Functions Model for Link Prediction on Antiviral Drugs Knowledge Graph
Knowledge graphs (KGs) on COVID-19 have been constructed to accelerate the research process of COVID-19. However, KGs are always incomplete, especially the new constructed COVID-19 KGs. Link prediction task aims to predict missing entities for (e, r, t) or (h, r, e), where h and t are certain entities, e is an entity that needs to be predicted and r is a relation. This task also has the potential to solve COVID-19 related KGs' incomplete problem. Although various knowledge graph embedding (KGE) approaches have been proposed to the link prediction task, these existing methods suffer from the limitation of using a single scoring function, which fails to capture rich features of COVID-19 KGs. In this work, we propose the MDistMult model that leverages multiple scoring functions to extract more features from existing triples. We employ experiments on the CCKS2020 COVID-19 Antiviral Drugs Knowledge Graph (CADKG). The experimental results demonstrate that our MDistMult achieves state-of-the-art performance in link prediction task on the CADKG dataset.
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Preprint| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
arXiv.org - (2021) vom: 29. Nov. Zur Gesamtaufnahme - year:2021 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Weichuan [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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XAR03311241X |
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| 520 | |a Knowledge graphs (KGs) on COVID-19 have been constructed to accelerate the research process of COVID-19. However, KGs are always incomplete, especially the new constructed COVID-19 KGs. Link prediction task aims to predict missing entities for (e, r, t) or (h, r, e), where h and t are certain entities, e is an entity that needs to be predicted and r is a relation. This task also has the potential to solve COVID-19 related KGs' incomplete problem. Although various knowledge graph embedding (KGE) approaches have been proposed to the link prediction task, these existing methods suffer from the limitation of using a single scoring function, which fails to capture rich features of COVID-19 KGs. In this work, we propose the MDistMult model that leverages multiple scoring functions to extract more features from existing triples. We employ experiments on the CCKS2020 COVID-19 Antiviral Drugs Knowledge Graph (CADKG). The experimental results demonstrate that our MDistMult achieves state-of-the-art performance in link prediction task on the CADKG dataset | ||
| 700 | 1 | |a Xie, Zhiwen |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Jin |e verfasserin |4 aut | |
| 700 | 1 | |a Duan, Yucong |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Junsheng |e verfasserin |4 aut | |
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