Graph Database, Knowledge Graph and Natural Language Processing
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 139
Special Issue Editor
Interests: natural language processing; text mining; question answering; knowledge graph
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Graph databases, knowledge graphs, and natural language processing are pivotal technologies in data-intensive fields. They integrate graph theory, semantic understanding, and machine learning to provide powerful tools for modeling, storing, and analyzing complex data relationships. These technologies have demonstrated immense potential across various application scenarios, including search engine optimization, recommendation systems, medical data analysis, automated question-answering, knowledge reasoning, and knowledge management. Graph databases and knowledge graphs facilitate the extraction, storage, and utilization of valuable knowledge from unstructured and semi-structured data by representing entities and their relationships. Combining this data representation with NLP techniques offers new possibilities for in-depth understanding and reasoning of textual data. The automatic construction and expansion of knowledge graphs from natural language texts through NLP models make the discovery, retrieval, and questioning of knowledge more intelligent and efficient.
This Special Issue collects papers with the aim of compiling the latest research findings on graph databases, knowledge graphs, and natural language processing. Special attention is devoted to the theory and application of graph databases, the construction and application of knowledge graphs, and the integration of natural language processing with graph technologies. The topics encompass theories, methods, and applications in natural language processing, text mining, information extraction, question answering, text generation, language models, knowledge graphs/bases, knowledge representation learning, knowledge augmentation, graph datasets, and graph computation.
Prof. Dr. Tianyong Hao
Guest Editor
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Keywords
- natural language processing
- large language model
- information extraction
- graph databases
- knowledge graphs
- knowledge representation learning
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