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Multi-Modal Entity Alignment Using Uncertainty Quantification for Modality Importance
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AI
DOI：10.1109/ACCESS.2023.3259987.png)
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Abstract
En 中文
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.
Keywords:
Uncertainty
Information retrieval
Visualization
Task analysis
Probability distribution
Numerical models
Knowledge graphs
Artificial intelligence
knowledge-based systems
knowledge representation
entity alignment
multi-modal learning
information retrieval