Big Data Mining and Knowledge Graph with Application

Dear Colleagues,

Modern AI systems often exhibit critical limitations in semantic grounding, explainable reasoning, and robustness when deployed in complex, dynamic environments. These challenges largely stem from the difficulty of extracting, representing, and leveraging structured knowledge from massive, heterogeneous data sources. To address this gap, knowledge graph-based semantic modeling, combined with large-scale data mining and generative frameworks (e.g., GraphRAG), has emerged as a promising direction for building more interpretable, controllable, and scalable intelligent systems.

This Special Issue aims to promote research that bridges methodological innovations in knowledge modeling with domain-grounded applications, demonstrating both technical novelty and practical impact. Submissions are expected to clearly articulate methodological advances validated in real-world scenarios, ensuring scientific rigor, reproducibility, and relevance.

Scope and Tracks:

We particularly welcome submissions that align with one or both of the following interconnected tracks:

1. Track 1: Methodological Foundations

• Knowledge graph construction, alignment, and dynamic evolution;
• Representation learning and distributional semantic modeling (e.g., normalizing flows, diffusion-based reversible generation);
• Integration of structured knowledge with generative AI frameworks, including GraphRAG;
• Knowledge-guided mining and reasoning with enhanced interpretability and controllability.

2. Track 2: Domain-Grounded Applications

• Scene-oriented knowledge graphs and reasoning in intelligent transportation, urban safety, and low-altitude airspace (e.g., UAV coordination);
• Knowledge-based systems in education, healthcare, and scientific knowledge discovery;
• Deployment and evaluation of scalable, knowledge-enhanced AI systems in real-world settings;

Submissions that effectively bridge these two tracks—showcasing methodological innovation validated in concrete domains—are especially encouraged.

Prof. Dr. Xia Xie
Guest Editor

Dr. Wenwen Zhang
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• knowledge graph
• big data mining
• semantic modeling
• reasoning
• GraphRAG
• scene graph-based scene understanding