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Knowledge Graphs and Semantic Understanding in Natural Language Processing

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 January 2027 | Viewed by 22200

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Dr. Wajahat Ali Khan

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Discipline of Computing and Mathematics, College of Science and Engineering, University of Derby, Kedleston Road, Derby DE22 1GB, UK
Interests: knowledge graphs; LLMs; ontologies; context awareness; digital twins
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Dear Colleagues,

Knowledge graphs (KGs) and semantic understanding are fundamental to advancing natural language processing (NLP), enabling machines to interpret context, relationships, and meaning more effectively. KGs provide structured representations of entities and their interconnections, significantly enhancing search engines, chatbots, and question-answering systems by linking queries to relevant knowledge. Meanwhile, semantic understanding, powered by techniques like word embeddings and transformer models, allows machines to capture implicit meanings in text, leading to breakthroughs in searches, recommendation systems, sentiment analysis, and specialized domains like healthcare, finance, and others.

This Special Issue will contribute in the future of NLP, which is set to be transformed by the deeper integration of KGs with large language models (LLMs), enabling more advanced reasoning, the improved explainability of AI-driven decisions, and more efficient real-time data processing. Emerging trends include self-evolving KGs that dynamically update with new information; multimodal knowledge integration that fuses text, images, and structured data; and hybrid AI approaches that combine symbolic reasoning with deep learning

Dr. Wajahat Ali Khan
Guest Editor

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Keywords

knowledge graphs
NLP
semantics
embeddings
LLMs

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Published Papers (6 papers)

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Research

40 pages, 15880 KB

Open AccessArticle

DIKWP-Guided Semantic Modeling of Intellectual Property Reasoning for Explainable Legal AI

by Zhendong Guo and Yucong Duan

Appl. Sci. 2026, 16(12), 6076; https://doi.org/10.3390/app16126076 - 16 Jun 2026

Viewed by 73

Abstract

Intellectual property reasoning depends on the interaction of factual context, doctrinal tests, exceptions, evidentiary uncertainty, and regulatory objectives. These features make patent, copyright, and trademark analysis difficult to support through text-level processing or isolated rule encoding. This article proposes a bounded DIKWP-guided semantic modeling framework for representing selected intellectual property reasoning patterns as queryable semantic structures. The framework is conceptual and design-oriented; it is specified at the design level through a formal graph characterization of DIKWP, a modular ontology fragment, rule schemas, SPARQL-style queries, and worked examples from patent, copyright, and trademark reasoning. Methodologically, the study uses a qualitative legal-informatics design approach. The three IP domains are selected because they represent complementary reasoning patterns: claim-element correspondence and equivalence screening in patent law, expression and exception analysis in copyright law, and factor-based confusion assessment in trademark law. The examples are used to derive semantic entities, relations, rule-linked structures, uncertainty annotations, explanation paths, and human-review triggers. DIKWP is treated not as a complete legal ontology or autonomous adjudicator, but as a network-structured meta-architecture for coordinating data, information, knowledge, wisdom, and purpose in reviewable legal decision support. The article illustrates how selected IP reasoning patterns can be represented in forms that remain traceable to legal sources and open to human review. It does not claim empirical validation, jurisdiction-specific doctrinal completeness, or autonomous legal decision-making. Its contribution is to specify how semantic legal representation can be made more operational, auditable, and institutionally constrained in the intellectual property domain. Full article

(This article belongs to the Special Issue Knowledge Graphs and Semantic Understanding in Natural Language Processing)

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28 pages, 3778 KB

Open AccessArticle

LLM-S2KG: LLM-Based Semantic–Structural Dual Knowledge Graph

by Jiang Jiang and Xiangtao Jiang

Appl. Sci. 2026, 16(10), 4720; https://doi.org/10.3390/app16104720 - 9 May 2026

Viewed by 578

Abstract

In knowledge graph construction tasks, recent research often leverages Large Language Models (LLMs) to enhance the efficiency and accuracy of unstructured data processing. However, current LLMs rely on lexical co-occurrence statistical patterns, making it difficult to capture deep semantic relationships. Furthermore, existing research largely focuses on entity-relation extraction or semantic-level optimization, overlooking the inherent hierarchical logical structures within text paragraphs (e.g., chapter organization, paragraph coherence). This leads to insufficient semantic completeness and damaged structural consistency in the constructed knowledge graphs. To address this dual limitation, we propose LLM-S2KG, a semantic–structural information extraction method that integrates LLMs with semantic correlation analysis. This method achieves synergistic modeling of semantic depth and logical structure by simultaneously performing dual parsing of keywords and structure, discovering and completing semantic associations, and finally integrating these dual graphs for construction. Experiments show that in query tasks, LLM-S2KG improved the F1 score by 0.1183, 0.1412, and 0.0231 compared with KeyBERT, TF-IDF, and LLM-KG, respectively. In fill-in-the-blank QA tasks, it achieved an accuracy of 94.81%; and in open-ended QA tasks, an accuracy of 85.885%, moderately outperforming LLM Triple Extraction (73.308%), LLM Triple Extraction with Source Sentence Augmentation (80.085%), and Chroma Database Import (76.150%). In summary, LLM-S2KG provides a unified modeling paradigm for structured knowledge extraction using LLMs, featuring mutual empowerment and co-evolution of semantics and structure. Full article

(This article belongs to the Special Issue Knowledge Graphs and Semantic Understanding in Natural Language Processing)

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22 pages, 5925 KB

Open AccessArticle

The Construction of a Design Method Knowledge Graph Driven by Multi-Source Heterogeneous Data

by Jixing Shi, Kaiyi Wang, Zhongqing Wang, Zhonghang Bai and Fei Hu

Appl. Sci. 2025, 15(19), 10702; https://doi.org/10.3390/app151910702 - 3 Oct 2025

Cited by 1 | Viewed by 3705

Abstract

To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source heterogeneous data and subsequently utilizing text mining and natural language processing techniques to extract design themes and method elements. A “theme–stage–attribute” three-dimensional mapping model is established to achieve semantic coupling of knowledge. The BERT-BiLSTM-CRF (Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field) model is employed for entity recognition and relation extraction, while the Sentence-BERT (Sentence Bidirectional Encoder Representations from Transformers) model is used to perform multi-source knowledge fusion. The Neo4j graph database facilitates knowledge storage, visualization, and querying, forming the basis for developing a prototype of a design method recommendation system. The framework’s effectiveness was validated through experiments on extraction performance and knowledge graph quality. The results demonstrate that the framework achieves an F1 score of 91.2% for knowledge extraction, and an 8.44% improvement over the baseline. The resulting graph’s node and relation coverage reached 94.1% and 91.2%, respectively. In complex semantic query tasks, the framework shows a significant advantage over traditional classification systems, achieving a maximum F1 score of 0.97. It can effectively integrate dispersed knowledge in the field of design methods and support method matching throughout the entire design process. This research is of significant value for advancing knowledge management and application in innovative product design. Full article

(This article belongs to the Special Issue Knowledge Graphs and Semantic Understanding in Natural Language Processing)

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29 pages, 1626 KB

Open AccessArticle

LLM-Driven Active Learning for Dependency Analysis of Mobile App Requirements Through Contextual Reasoning and Structural Relationships

by Nuha Almoqren and Mubarak Alrashoud

Appl. Sci. 2025, 15(18), 9891; https://doi.org/10.3390/app15189891 - 9 Sep 2025

Cited by 2 | Viewed by 2178

Abstract

In today’s fast-paced release cycles, mobile app user reviews offer a valuable source for tracking the evolution of user needs. At the core of these needs lies a structure of interdependencies—some enhancements are only relevant in specific usage contexts, while others may conflict when implemented together. Identifying these relationships is essential for anticipating feature interactions, resolving contradictions, and enabling context-aware, user-driven planning. The present work introduces an ontology-enhanced AI framework for predicting whether the requirements mentioned in reviews are interdependent. The core component is a Bidirectional Encoder Representations from Transformers (BERT) classifier retrained within a large-language-model-driven active learning loop that focuses on instances with uncertainty. The framework integrates contextual and structural reasoning; contextual analysis captures the semantic intent and functional role of each requirement, enriching the understanding of user expectations. Structural reasoning relies on a domain-specific ontology that serves as both a knowledge base and an inference layer, guiding the grouping of requirements. The model achieved strong performance on annotated banking app reviews, with a validation F1-score of 0.9565 and an area under the ROC curve (AUC) exceeding 0.97. The study results contribute to supporting developers in prioritizing features based on dependencies and delivering more coherent, conflict-free releases. Full article

(This article belongs to the Special Issue Knowledge Graphs and Semantic Understanding in Natural Language Processing)

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22 pages, 933 KB

Open AccessArticle

DRKG: Faithful and Interpretable Multi-Hop Knowledge Graph Question Answering via LLM-Guided Reasoning Plans

by Yan Chen, Shuai Sun and Xiaochun Hu

Appl. Sci. 2025, 15(12), 6722; https://doi.org/10.3390/app15126722 - 16 Jun 2025

Cited by 2 | Viewed by 6516

Abstract

Multi-Hop Knowledge Graph Question Answering (multi-hop KGQA) aims to obtain answers by analyzing the semantics of natural language questions and performing multi-step reasoning across multiple entities and relations in knowledge graphs. Traditional embedding-based methods map natural language questions and knowledge graphs into vector spaces for answer matching through vector operations. While these approaches have improved model performance, they face two critical challenges: the lack of clear interpretability caused by implicit reasoning mechanisms, and the semantic gap between natural language queries and structured knowledge representations. This study proposes the DRKG (Decomposed Reasoning over Knowledge Graph), a constrained multi-hop reasoning framework based on large language models (LLMs) that introduces explicit reasoning plans as logical boundary controllers. The innovation of the DRKG lies in two key aspects: First, the DRKG generates hop-constrained reasoning plans through semantic parsing based on LLMs, explicitly defining the traversal path length and entity-retrieval logic in knowledge graphs. Second, the DRKG conducts selective retrieval during knowledge graph traversal based on these reasoning plans, ensuring faithfulness to structured knowledge. We evaluate the DRKG on four datasets, and the experimental results demonstrate that the DRKG achieves 1%–5% accuracy improvements over the best baseline models. Additional ablation studies verify the effectiveness of explicit reasoning plans in enhancing interpretability while constraining path divergence. A reliability analysis further examines the impact of different parameters combinations on the DRKG’s performance. Full article

(This article belongs to the Special Issue Knowledge Graphs and Semantic Understanding in Natural Language Processing)

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19 pages, 2707 KB

Open AccessArticle

A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication

by Chang Guo, Jiaqi Liu, Wei Gao, Zhenhai Lu, Yao Li, Chengyuan Wang and Jungang Yang

Appl. Sci. 2025, 15(8), 4575; https://doi.org/10.3390/app15084575 - 21 Apr 2025

Cited by 10 | Viewed by 7493

Abstract

This study presents a knowledge graph construction scheme leveraging large language models (LLMs) for task-oriented semantic communication systems. The proposed methodology systematically addresses four critical stages: corpus collection, entity extraction and relationship analysis, knowledge base generation, and dynamic updating mechanisms. It is worth noting that prompt engineering is combined with few-shot learning to enhance reliability and accuracy in this methodology. Experimental demonstration showed that this methodology had superior entity extraction performance, achieving 89.7% precision and 92.3% recall rate. This scheme overcomes the demand for domain knowledge and the labor cost of traditional knowledge base construction schemes. It greatly improves the construction efficiency of knowledge graphs. This paper provides an efficient and reliable task knowledge base construction scheme for task-oriented semantic communication, which is expected to promote its wider application. Full article

(This article belongs to the Special Issue Knowledge Graphs and Semantic Understanding in Natural Language Processing)

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