Semantic Causality Knowledge Graph with Ontology Integration for Financial Analysis

In recent years, knowledge graphs have become vital for financial decision support by allowing structured representation and reasoning through complex textual data. However, traditional methods such as Financial Causality Knowledge Graph (FinCaKG) fail in inconsistent or semantically weak settings due to their dependance on raw text strings without ontology alignment. Hence, a Financial Causality Knowledge Graph with Ontology Integration (FinCaKG-Onto) framework was proposed to confirm accurate causality extraction and semantically consistent financial knowledge representation. Initially, financial reports (10-K filings) from FinCausal benchmark dataset were gathered. After that, a Bidirectional Encoder Representations from Transformers (BERT)-based causality detection module studies to recognize cause effect spans in financial reports by state observations from labeled datasets. Furthermore, the extracted spans were dynamically mapped to standardized financial concepts through entity linking with Financial Industry Business Ontology (FIBO). Then, causality bonding mechanism creates explicit cause–effect relations among normalized entities, whereas ontology integration preserves semantic consistency and hierarchical structure. Subsequently, a schema-based organization was applied to allow lightweight reasoning across financial concepts, in which nodes were aligned to their ontology classes and subclasses. Finally, the experimental results showed the proposed FinCaKG-Onto outperformed FinCaKG by attaining an ontology consistency of 95.6% across large-scale financial reports.