Fusion and Construction Strategy of Knowledge Graphs from Multi-Source Data

The complexity and diversity of multi-source data present significant challenges in integrating and extracting meaningful insights. Knowledge graphs have emerged as a powerful tool for information integration, enhancing data connectivity and interpretability. This paper proposes a systematic approach to the fusion and construction of knowledge graphs from multi-source data. Our method encompasses efficient data collection and preprocessing, knowledge extraction from diverse data forms, and semantic fusion through ontology alignment and conflict resolution. We leverage graph database technologies and advanced reasoning algorithms to construct and manage the knowledge graph, ensuring its integrity and extensibility. The proposed framework is evaluated using case studies, demonstrating its accuracy and completeness. This research contributes to the field by offering a novel methodology for constructing knowledge graphs that facilitate improved data analysis and decision-making, with potential applications across various domains.