Abstract
Abstract Due to the large-scale spread of COVID-19, which has a significant impact on human health and social economy, developing effective antiviral drugs for COVID-19 is vital to saving human lives. Various biomedical associations, e.g., drug-virus and viral protein-host protein interactions, can be used for building biomedical knowledge graphs. Based on these sources, large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and viruses. To utilize the various heterogeneous biomedical associations, we proposed a fusion strategy to integrate the results of two tensor decomposition-based models (i.e., CP-N3 and ComplEx-N3). Sufficient experiments indicated that our method obtained high performance (MRR=0.2328). Compared with CP-N3, the mean reciprocal rank (MRR) is increased by 3.3% and compared with ComplEx-N3, the MRR is increased by 3.5%. Meanwhile, we explored the relationship between the performance and relationship types, which indicated that there is a negative correlation (PCC=0.446, P-value=2.26e-194) between the performance of triples predicted by our method and edge betweenness.
Highlights
Knowledge Graph (KG) is a structured representation of real-world information
It is well known that even the most advanced KGs still suffer from incompleteness [3, 1, 4], such as FreeBase [5], WikiData [6], DBPedia [7] and Google KG [8]
The KG completion performance of current Canonical Tensor Decomposition (CP) on the standard benchmarks are behind their competitors
Summary
Knowledge Graph (KG) is a structured representation of real-world information. In KGs, nodes represent entities, such as people and places; edges are specific facts that connect two entities; labels are the types of edges [1, 2]. As a basis for knowledge engineering applications, KG plays an extremely important role for various artificial intelligence tasks (e.g.clinical decision making and question-answering systems). Link prediction (LP), which exploits the existing facts in a KG to infer missing ones, is one of the most prospective ways to address this problem [8]. At the time of the large-scale spread of COVID-19 [9], China Conference on Knowledge Graph and Semantic Computing (CCKS) 2020 set up a link prediction task for the knowledge graph of COVID-19
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