Abstract：
To easier manipulate Knowledge Graphs (KGs), knowledge graph embedding (KGE) is proposed and wildly used. However, the relations between entities are usually incomplete due to the performance problems of knowledge extraction methods, which also leads to the sparsity of KGs and make it difficult for KGE methods to obtain reliable representations. Related research has not paid much attention to this challenge in the biomedicine field and has not sufficiently integrated the domain knowledge into KGE methods. To alleviate this problem, we try to incorporate the molecular structure information of the entity into KGE. Specifically, we adopt two strategies to obtain the vector representations of the entities: text-structure-based and graph-structure-based. Then, we spliced the two together as the input of the KGE models. To validate our model, we construct a KCCR knowledge graph and validate the model\'s superiority in entity prediction, relation prediction, and drug-drug interaction prediction tasks. To the best of our knowledge, this is the first time that molecular structure information has been integrated into KGE methods. It is worth noting that researchers can try to improve the work based on KGE by fusing other feature annotations such as Gene Ontology and protein structure.
摘要：
为了更容易操作知识图谱(KGs)，知识图嵌入(KGE)被提出并得到了广泛的应用。然而,由于知识提取方法的性能问题，实体之间的关系通常是不完整的，这也导致了KGs的稀疏性，使得KGE方法难以获得可靠的表示。相关研究并没有对生物医学领域的这一挑战给予足够的关注，也没有将领域知识充分整合到KGE方法中。为了缓解这个问题,我们尝试将实体的分子结构信息整合到KGE中。具体来说,我们采用两种策略来获得实体的向量表示：基于文本结构和基于图形结构。然后，我们将两者拼接在一起，作为KGE模型的输入。为了验证我们的模型，构建了KCCR知识图，验证了模型在实体预测中的优越性，关系预测，和药物相互作用预测任务。据我们所知,这是第一次将分子结构信息集成到KGE方法中。值得注意的是，研究人员可以尝试通过融合基因本体和蛋白质结构等其他特征注释来改进基于KGE的工作。
