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• W4365794865 abstract "In recent years, due to their expertise and comprehensiveness in a specific domain, domain-specific knowledge bases (KBs) have attracted more and more attention from both academics and industries. Among these domain-specific KBs, financial KBs have become more and more popular and valuable due to their broad spectrum of downstream applications, such as quantitative investment analysis, financial risk analysis, and financial domain-based KBQA. However, due to their massive volume, high conflicts, and frequent volatile properties, it is pretty challenging to build an error-prone dynamic financial KB. To address these challenges, in this paper, we propose a dynamic financial KB construction pipeline that mainly consists of two fundamental modules, a Human-Interacted (HI) distant supervised evolved relation extraction module targets at obtaining the evolved knowledge with less manual annotations and high extraction accuracy, and a Temporal (T) duplication and conflict resolution module focus on applying a data fusion algorithm to the knowledge fusion task to select high-confidence knowledge without duplication and conflict by incorporating the temporal information. Through extensive experiments, we have demonstrated the effectiveness of HIT. Compared to state-of-the-art solutions, HIT can improve the accuracy by $$10.6%$$ on average for the relation extraction task and by $$6.9%$$ on average for the duplication and conflict resolution task, respectively." @default.
• W4365794865 created "2023-04-16" @default.
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• W4365794865 date "2023-01-01" @default.
• W4365794865 modified "2023-10-14" @default.
• W4365794865 title "HIT - An Effective Approach to Build a Dynamic Financial Knowledge Base" @default.
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• W4365794865 doi "https://doi.org/10.1007/978-3-031-30672-3_48" @default.
• W4365794865 hasPublicationYear "2023" @default.
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