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Exploring Traditional and AI-Driven Approaches on Knowledge Graphs and Semantic...
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Exploring Traditional and AI-Driven Approaches on Knowledge Graphs and Semantic Web Technologies

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Information Applications".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 8833

Editor

Dr. Charalampos Bratsas

E-Mail Website
Guest Editor
Department of Information and Electronic Engineering, International Hellenic University, Sindos, P.O. Box 141, 57400 Thessaloniki, Greece
Interests: intelligent systems; knowledge graphs; machine learning; semantic web; open data; data science; data journalism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ongoing advancements in artificial intelligence (AI), knowledge graphs, and semantic web technologies are transforming the way we represent, analyze, and interact with data. These fields have become instrumental in tackling complex challenges, enabling more intelligent systems, and facilitating innovative applications across diverse domains, such as healthcare, finance, education, and beyond. This Special Issue aims to bring together cutting-edge research and practical applications that explore the intersection of AI, knowledge graphs, and semantic web technologies. We welcome contributions addressing theoretical developments, novel methodologies, innovative applications, and case studies.

Topics of interest include the following:

  • Development and optimization of knowledge graphs and ontologies for large-scale systems.
  • Real-world applications of semantic web technologies and ontologies to enhance decision making.
  • Machine learning approaches incorporating ontologies, semantic data, and LLMs for improved reasoning, knowledge discovery, and problem solving.
  • Advances in natural language processing using semantic technologies.
  • Enhancing interoperability, reasoning, and querying across complex, multi-domain datasets through AI and ontological frameworks.
  • AI-driven techniques for the automated construction, enrichment, and maintenance of knowledge graphs that utilize LLMs and semantic methods.

This Special Issue aspires to showcase the latest advancements, foster interdisciplinary collaboration, and inspire further innovation at the convergence of these transformative technologies.

We invite original research articles and reviews to contribute to this rapidly evolving field.

Dr. Charalampos Bratsas
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly 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 1800 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.

Keywords

  • knowledge graphs
  • semantic web technologies
  • ontologies and reasoning
  • machine learning with ontologies and knowledge graphs
  • linked data and semantic data integration
  • natural language processing with knowledge graphs
  • large language models for semantic technologies
  • ontology and reasoning on knowledge graphs
  • automated construction and enrichment of knowledge graphs

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

24 pages, 3598 KB  
Article
EcoRestore-KG: A Multi-Agent Framework for Knowledge Graph Construction in Territorial Ecological Restoration
by Shibin Zhong, Xiaoji Lan, Wenhao Yi and Shengdong Nie
Information 2026, 17(8), 778; https://doi.org/10.3390/info17080778 - 13 Aug 2026
Viewed by 209
Abstract
Territorial ecological restoration involves a continuous chain of tasks, including degradation diagnosis, target identification, zoning-based governance, process monitoring, effectiveness assessment and adaptive management. The relevant knowledge is widely distributed across policy plans, monitoring reports, restoration cases and the academic literature, and is characterized [...] Read more.
Territorial ecological restoration involves a continuous chain of tasks, including degradation diagnosis, target identification, zoning-based governance, process monitoring, effectiveness assessment and adaptive management. The relevant knowledge is widely distributed across policy plans, monitoring reports, restoration cases and the academic literature, and is characterized by multi-source heterogeneity, cross-scale associations and dynamic change. Existing knowledge organization approaches mainly rely on textual synthesis, indicator systems or general-purpose knowledge graph construction tools, and therefore struggle to simultaneously handle cross-context implicit relations, domain-rule constraints, inconsistent entity expressions and evidence traceability in ecological restoration knowledge. To address these limitations, this paper proposes EcoRestore-KG, a multi-agent knowledge graph construction framework for territorial ecological restoration. The framework unifies heterogeneous inputs through controlled evidence representation and adaptive context segmentation, and organizes ontology-guided triple mining, cross-context relation inference, graph quality control, entity canonicalization, relation endpoint remapping and evidence binding into a progressive workflow for the automatic extraction, auditing and assembly of ecological restoration knowledge. Experimental results show that EcoRestore-KG outperforms general-purpose large language models and existing knowledge graph construction baselines in relation extraction, entity coverage and semantic-quality evaluation. It achieves relation precision, recall and F1 scores of 71.4% ± 0.3%, 69.5% ± 4.4% and 70.3% ± 2.3%, respectively, improving relation F1 by 9.9 percentage points over the strongest baseline. Its entity F1 reaches 79.8% ± 0.7%, and its LLM-S score reaches 8.48 ± 0.11. Single-module and combined ablation experiments further demonstrate that evidence representation, context segmentation, cross-context relation inference, relation quality auditing and entity canonicalization jointly support the performance gains of the framework. This study provides a verifiable methodological pathway for structured organization, quality auditing, evidence tracing and subsequent integration of newly available knowledge. Full article
(This article belongs to the Special Issue Exploring Traditional and AI-Driven Approaches on Knowledge Graphs and Semantic Web Technologies)
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18 pages, 1182 KB  
Article
Co-MedGraphRAG: A Collaborative Large–Small Model Medical Question-Answering Framework Enhanced by Knowledge Graph Reasoning
by Sizhe Chen and Tao Chen
Information 2026, 17(3), 247; https://doi.org/10.3390/info17030247 - 2 Mar 2026
Cited by 1 | Viewed by 1942
Abstract
Large language models (LLMs) have demonstrated significant capabilities in natural language processing (NLP), but they often encounter challenges in the medical domain. This can result in insufficient alignment between generated answers and user intent, as well as factual deviations. To address these issues, [...] Read more.
Large language models (LLMs) have demonstrated significant capabilities in natural language processing (NLP), but they often encounter challenges in the medical domain. This can result in insufficient alignment between generated answers and user intent, as well as factual deviations. To address these issues, we propose Co-MedGraphRAG, a novel framework combining knowledge graph reasoning with large–small model collaboration, aimed at improving the structural grounding and interpretability of medical responses. The framework operates through a multi-stage collaborative mechanism to augment question answering. First, a large language model constructs a question-specific knowledge graph (KG) containing pending entities (denoted as “none”) to explicitly define known and unknown variables. Subsequently, a hybrid reasoning strategy is employed to populate the pending entities, thereby completing the question-specific knowledge graph. Finally, this graph serves as critical structured evidence, combined with the original question, to augment the large language model in generating the final answer, implemented using Qwen2.5-7B and GLM4-9B in this paper. To evaluate the generated answers, we introduce a larger-parameter LLM(GPT-4o) to assess performance across five dimensions and compute an overall score. Experiments on three medical datasets demonstrate that Co-MedGraphRAG achieves consistent improvements in relevance, practicality, and structured knowledge support compared with mainstream Retrieval-Augmented Generation (RAG) frameworks. This work serves as a reference for researchers and developers designing medical question-answering frameworks and exploring decision-support applications. Full article
(This article belongs to the Special Issue Exploring Traditional and AI-Driven Approaches on Knowledge Graphs and Semantic Web Technologies)
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17 pages, 4473 KB  
Article
RAG-Based Natural Language Interface for Goal-Oriented Knowledge Graphs and Its Evaluation
by Kosuke Yano, Yoshinobu Kitamura and Kazuhiro Kuwabara
Information 2026, 17(1), 55; https://doi.org/10.3390/info17010055 - 7 Jan 2026
Viewed by 2010
Abstract
Procedural knowledge is essential in specialized domains, and natural language tools for retrieving procedural knowledge are necessary for non-expert users to facilitate their understanding and learning. In this study, we focus on function decomposition trees, a framework for representing procedural knowledge, and propose [...] Read more.
Procedural knowledge is essential in specialized domains, and natural language tools for retrieving procedural knowledge are necessary for non-expert users to facilitate their understanding and learning. In this study, we focus on function decomposition trees, a framework for representing procedural knowledge, and propose a natural language interface leveraging Retrieval-Augmented Generation (RAG). The natural language interface converts the user’s inputs into SPARQL queries, retrieving relevant data and subsequently presenting them in an accessible and chat-based format. Such a flexible and purpose-driven search facilitates users’ understanding of functions of artifacts or human actions and their performance of these actions. We demonstrate that the tool effectively retrieves actions, goals, and dependencies using an illustrative real-world example of a function decomposition tree. In addition, we evaluated the system by comparing it with ChatGPT 4o and Microsoft GraphRAG. The results suggest that the system can deliver responses that are both necessary and sufficient for users’ needs, while the outputs of other systems lack the key elements and return redundant information. Full article
(This article belongs to the Special Issue Exploring Traditional and AI-Driven Approaches on Knowledge Graphs and Semantic Web Technologies)
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27 pages, 18762 KB  
Article
From Data to Decision: A Semantic and Network-Centric Approach to Urban Green Space Planning
by Elisavet Parisi and Charalampos Bratsas
Information 2025, 16(8), 695; https://doi.org/10.3390/info16080695 - 16 Aug 2025
Viewed by 3042
Abstract
Urban sustainability poses a deeply interdisciplinary challenge, spanning technical fields like data science and environmental science, design-oriented disciplines like architecture and spatial planning, and domains such as economics, policy, and social studies. While numerous advanced tools are used in these domains, ranging from [...] Read more.
Urban sustainability poses a deeply interdisciplinary challenge, spanning technical fields like data science and environmental science, design-oriented disciplines like architecture and spatial planning, and domains such as economics, policy, and social studies. While numerous advanced tools are used in these domains, ranging from geospatial systems to AI and network analysis-, they often remain fragmented, domain-specific, and difficult to integrate. This paper introduces a semantic framework that aims not to replace existing analytical methods, but to interlink their outputs and datasets within a unified, queryable knowledge graph. Leveraging semantic web technologies, the framework enables the integration of heterogeneous urban data, including spatial, network, and regulatory information, permitting advanced querying and pattern discovery across formats. Applying the methodology to two urban contexts—Thessaloniki (Greece) as a full implementation and Marine Parade GRC (Singapore) as a secondary test—we demonstrate its flexibility and potential to support more informed decision-making in diverse planning environments. The methodology reveals both opportunities and constraints shaped by accessibility, connectivity, and legal zoning, offering a reusable approach for urban interventions in other contexts. More broadly, the work illustrates how semantic technologies can foster interoperability among tools and disciplines, creating the conditions for truly data-driven, collaborative urban planning. Full article
(This article belongs to the Special Issue Exploring Traditional and AI-Driven Approaches on Knowledge Graphs and Semantic Web Technologies)
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