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<p>Causality stands as an essential relation for elucidating the reasoning behind given contents. However, current causality knowledge graphs fall short in effectively illustrating the inner logic in a specific domain, i.e. finance. To generate such a functional knowledge graph, we propose the multi-faceted approach encompassing causality detection module, entity linking module, and causality alignment module to automatically construct FinCaKG-Onto with the guidance of expert financial ontology - FIBO. In this paper, we outline the resources and methodology employed for FinCaKG-Onto construction, present the schema of FinCaKG-Onto, and share the final knowledge graph FinCaKG-Onto. Through various user scenarios, we demonstrate that FinCaKG-Onto not only captures nuanced domain expertise but also explicitly unveils the causal logic for any anchor terms. To facilitate your convenience of future use, a check table is conducted as well to showcase the quality of FinCaKG-Onto. The related resources are available in the webpage&lt;<a href="https://www.ai.iee.e.titech.ac.jp/FinCaKG-Onto/">https://www.ai.iee.e.titech.ac.jp/FinCaKG-Onto/</a>&gt;.</p>

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FinCaKG-Onto: the financial expertise depiction via causality knowledge graph and domain ontology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月19日

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  • Ziwei Xu,
  • Ryutaro Ichise

摘要

Causality stands as an essential relation for elucidating the reasoning behind given contents. However, current causality knowledge graphs fall short in effectively illustrating the inner logic in a specific domain, i.e. finance. To generate such a functional knowledge graph, we propose the multi-faceted approach encompassing causality detection module, entity linking module, and causality alignment module to automatically construct FinCaKG-Onto with the guidance of expert financial ontology - FIBO. In this paper, we outline the resources and methodology employed for FinCaKG-Onto construction, present the schema of FinCaKG-Onto, and share the final knowledge graph FinCaKG-Onto. Through various user scenarios, we demonstrate that FinCaKG-Onto not only captures nuanced domain expertise but also explicitly unveils the causal logic for any anchor terms. To facilitate your convenience of future use, a check table is conducted as well to showcase the quality of FinCaKG-Onto. The related resources are available in the webpage<https://www.ai.iee.e.titech.ac.jp/FinCaKG-Onto/>.