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Applied Sciences
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Natural Language Processing and Semantic Technologies: From Theories to Applications
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Natural Language Processing and Semantic Technologies: From Theories to Applications

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 September 2025 | Viewed by 2008

Special Issue Editors

Dr. André Pomp

E-Mail Website
Guest Editor
Institute for Technologies and Management of Digtial Transformation, University of Wuppertal, Gaußstraße 20, 42119 Wuppertal, Germany
Interests: semantic interoperability; knowledge graphs; natural language processing; large language models; information extraction; deep and machine learning
Prof. Dr. Christoph Quix

E-Mail Website
Guest Editor
Information Systems and Data Science, Hochschule Niederrhein, Krefeld, Germany
Interests: data science; data integration and modelling; metadata management; data lake and data warehouse systems

Special Issue Information

Dear Colleagues,

The recent advances in large language models have greatly expanded the capabilities of NLP, enabling nuanced understandings and the creation of human-like text. At the same time, the use of semantic technologies, especially knowledge graphs and embeddings, has enriched our contextual understanding by encoding complex relationships between the entities. Together, these technologies can simplify our access to information. This Special Issue focuses on the synergistic fusion of large language models and semantic technologies, exploring how their integration improves the natural language processing (NLP) and data accessibility landscapes. The contributors may explore the collaborative potential of these technologies, addressing how semantic structures improve the context awareness of language models and knowledge representation. This Special Issue aims to shed light on novel methods, applications, and interdisciplinary approaches that leverage the combined strengths of large language models and semantic technologies to push the boundaries of intelligent natural language processing systems.

Dr. André Pomp
Prof. Dr. Christoph Quix
Guest Editors

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. Applied Sciences 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 2400 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

  • natural language processing
  • large language models
  • semantic web
  • knowledge graphs
  • ontologies
  • semantic interoperability
  • semantic modeling

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Published Papers (2 papers)

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Research

38 pages, 2033 KiB  
Article
DCAT: A Novel Transformer-Based Approach for Dynamic Context-Aware Image Captioning in the Tamil Language
by Jothi Prakash Venugopal, Arul Antran Vijay Subramanian, Manikandan Murugan, Gopikrishnan Sundaram, Marco Rivera and Patrick Wheeler
Appl. Sci. 2025, 15(9), 4909; https://doi.org/10.3390/app15094909 - 28 Apr 2025
Viewed by 130
Abstract
The task of image captioning in low-resource languages like Tamil is fraught with challenges due to limited linguistic resources and complex semantic structures. This paper addresses the problem of generating contextually and linguistically coherent captions in Tamil. We introduce the Dynamic Context-Aware Transformer [...] Read more.
The task of image captioning in low-resource languages like Tamil is fraught with challenges due to limited linguistic resources and complex semantic structures. This paper addresses the problem of generating contextually and linguistically coherent captions in Tamil. We introduce the Dynamic Context-Aware Transformer (DCAT), a novel approach that synergizes the Vision Transformer (ViT) with the Generative Pre-trained Transformer (GPT-3), reinforced by a unique Context Embedding Layer. The DCAT model, tailored for Tamil, innovatively employs dynamic attention mechanisms during its Initialization, Training, and Inference phases to focus on pertinent visual and textual elements. Our method distinctively leverages the nuances of Tamil syntax and semantics, a novelty in the realm of low-resource language image captioning. Comparative evaluations against established models on datasets like Flickr8k, Flickr30k, and MSCOCO reveal DCAT’s superiority, with a notable 12% increase in BLEU score (0.7425) and a 15% enhancement in METEOR score (0.4391) over leading models. Despite its computational demands, DCAT sets a new benchmark for image captioning in Tamil, demonstrating potential applicability to other similar languages. Full article
(This article belongs to the Special Issue Natural Language Processing and Semantic Technologies: From Theories to Applications)
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21 pages, 2080 KiB  
Article
A Sentence-Matching Model Based on Multi-Granularity Contextual Key Semantic Interaction
by Jinhang Li and Yingna Li
Appl. Sci. 2024, 14(12), 5197; https://doi.org/10.3390/app14125197 - 14 Jun 2024
Viewed by 1071
Abstract
In the task of matching Chinese sentences, the key semantics within sentences and the deep interaction between them significantly affect the matching performance. However, previous studies mainly relied on shallow interactions based on a single semantic granularity, which left them vulnerable to interference [...] Read more.
In the task of matching Chinese sentences, the key semantics within sentences and the deep interaction between them significantly affect the matching performance. However, previous studies mainly relied on shallow interactions based on a single semantic granularity, which left them vulnerable to interference from overlapping terms. It is particularly challenging to distinguish between positive and negative examples within datasets from the same thematic domain. This paper proposes a sentence-matching model that incorporates multi-granularity contextual key semantic interaction. The model combines multi-scale convolution and multi-level convolution to extract different levels of contextual semantic information at word, phrase, and sentence granularities. It employs multi-head self-attention and cross-attention mechanisms to align the key semantics between sentences. Furthermore, the model integrates the original, similarity, and dissimilarity information of sentences to establish deep semantic interaction. Experimental results on both open- and closed-domain datasets demonstrate that the proposed model outperforms existing baseline models in terms of matching performance. Additionally, the model achieves matching effectiveness comparable to large-scale pre-trained language models while utilizing a lightweight encoder. Full article
(This article belongs to the Special Issue Natural Language Processing and Semantic Technologies: From Theories to Applications)
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