Construction of Journal Knowledge Graph Based on Deep Learning and LLM

Abstract

Knowledge graphs are powerful tools for representing the relationships between concepts and entities in the real world through triples. Due to their superior knowledge representation and efficient reasoning abilities, knowledge graphs have gained widespread attention across various fields, leading to their development in multiple domains. However, research on the construction of journal knowledge graphs remains relatively limited, posing challenges for the integration and utilization of knowledge in the journal domain. To address this gap, this study explores effective methods for constructing journal knowledge graphs and develops a journal knowledge graph-based question answering system. Specifically, journal datasets were collected from multiple sources using the Scrapy framework, encompassing structured, semi-structured, and unstructured data. A BERT-BiLSTM-CRF framework was then employed to extract entities, attributes, and relationships from semi-structured and unstructured data. In addition, the constructed journal knowledge graph was integrated with large language models (LLMs) to build a journal-related question answering system, facilitating efficient querying and utilization. Finally, Neo4j was used for storing the constructed journal knowledge graph.