Multi-Modal Entity Alignment Using Uncertainty Quantification for Modality Importance
Multi-Modal Entity Alignment Using Uncertainty Quantification for Modality Importance
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DOI：
10.1109/access.2023.3259987
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Kenta Hama;Takashi Matsubara
中科院分区：
文献类型：
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作者：
Kenta Hama;Takashi Matsubara
知识图是用于信息检索目的的结构化数据。利用多模态补充信息的实体对齐在知识图集成中起着重要作用。但是，如果补充信息缺失或不正确，则可能对信息检索产生负面影响。如果我们能够将信息的有用性量化为重要性程度，则可以减少不重要的补充信息的影响。在这项研究中，我们提出了一种方法，通过使用概率分布来量化每条信息的重要性。我们提出的方法在两个数据集（FB 15 K-DB 15 K，FB 15 K-YAGO 15 K）上的H@1中将现有方法提高了7.7%和7.3%。定性实验还表明，信息的重要性量化的不确定性成功地捕获数据，是没有用的信息检索。我们的定性实验还表明，信息的重要性，量化的不确定性，有效地捕捉数据，是不利于信息检索。
The knowledge graphs are structured data utilized for information retrieval purposes. Entity alignment using multi-modal supplementary information plays an important role in knowledge graph integration. However, if the supplementary information is missing or incorrect, it can negatively impact the retrieval of information. If we can quantify the usefulness of the information for retrieval as a degree of importance, the influence of unimportant supplementary information can be reduced. In this study, we proposed a method that quantifies the importance of each piece of information by using a probability distribution. Our proposed method improves an existing method by 7.7% and 7.3% in H@1 on two datasets (FB15K-DB15K, FB15K-YAGO15K). Qualitative experiments also showed that the importance of information quantified by uncertainty successfully captured data that was not useful for information retrieval. Our qualitative experiments also show that the importance of information, quantified by uncertainty, effectively captures data that is not beneficial for information retrieval.