Search Results (65)

Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively support flexible multi-dietary reasoning in combination with user preferences and restrictions. For example, users may seek gluten-free and dairy-free dinners with suitable substitutions, or compound goals such as vegan and low-fat desserts. Recent systematic reviews report that most food recommender systems are content-based and often non-personalized, with limited support for dietary restrictions, ingredient-level exclusions, and multi-criteria nutrition goals. This paper introduces DietQA, an end-to-end, language-adaptable chatbot system that integrates a Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and a Large Language Model (LLM) to support personalized, dietary-aware recipe search and question answering. DietQA crawls Greek-language recipe websites to extract structured information such as titles, ingredients, and quantities. Nutritional values are calculated using validated food composition databases, and dietary tags are inferred automatically based on ingredient composition. All information is stored in a Neo4j-based knowledge graph, enabling flexible querying via Cypher. Users interact with the system through a natural language chatbot friendly interface, where they can express preferences for ingredients, nutrients, dishes, and diets, and filter recipes based on multiple factors such as ingredient availability, exclusions, and nutritional goals. DietQA supports multi-diet recipe search by retrieving both compliant recipes and those adaptable via ingredient substitutions, explaining how each result aligns with user preferences and constraints. An LLM extracts intents and entities from user queries to support rule-based Cypher retrieval, while the RAG pipeline generates contextualized responses using the user query and preferences, retrieved recipes, statistical summaries, and substitution logic. The system integrates real-time updates of recipe and nutritional data, supporting up-to-date, relevant, and personalized recommendations. It is designed for language-adaptable deployment and has been developed and evaluated using Greek-language content. DietQA provides a scalable framework for transparent and adaptive dietary recommendation systems powered by conversational AI. Full article

Table-based question answering (TableQA) has made significant progress in recent years; however, most advancements have focused on English datasets and SQL-based techniques, leaving Arabic TableQA largely unexplored. This gap is especially critical given the widespread use of structured Arabic content in domains such as government, education, and media. The main challenge lies in the absence of benchmark datasets and the difficulty that large language models (LLMs) face when reasoning over long, complex tables in Arabic, due to token limitations and morphological complexity. To address this, we introduce Arabic WikiTableQA, the first large-scale dataset for non-SQL Arabic TableQA, constructed from the WikiTableQuestions dataset and enriched with natural questions and gold-standard answers. We developed three methods to evaluate this dataset: a direct input approach, a sub-table selection strategy using SQL-like filtering, and a knowledge-guided framework that filters the table using semantic graphs. Experimental results with an LLM show that the graph-guided approach outperforms the others, achieving 74% accuracy, compared to 64% for sub-table selection and 45% for direct input, demonstrating its effectiveness in handling long and complex Arabic tables. Full article

This study presents a domain-adapted retrieval-augmented generation (RAG) pipeline that integrates geological knowledge with large language models (LLMs) to support intelligent question answering in the metallogenic domain. Focusing on the Qin–Hang metallogenic belt in South China, we construct a bilingual question-answering (QA) corpus derived from 615 authoritative geological publications, covering topics such as regional tectonics, ore-forming processes, structural evolution, and mineral resources. Using the ChatGLM3-6B language model and LangChain framework, we embed the corpus into a semantic vector database via Sentence-BERT and FAISS, enabling dynamic retrieval and grounded response generation. The RAG-enhanced model significantly outperforms baseline LLMs—including ChatGPT-4, Bing, and Gemini—in a comparative evaluation using BLEU, precision, recall, and F1 metrics, achieving an F1 score of 0.8689. The approach demonstrates high domain adaptability and reproducibility. All datasets and codes are openly released to facilitate application in other metallogenic belts. This work illustrates the potential of LLM-based knowledge engineering to support digital geoscientific research and smart mining. Full article

Civil aviation airworthiness regulations are the fundamental basis for the design and operational safety of aircraft. Their provisions exhibit a high degree of specialization, cross-disciplinary complexity, and hierarchical structure. Moreover, the regulations are frequently updated, posing unique challenges for automated question-answering systems. While large language models (LLMs) have demonstrated remarkable capabilities in dialog and reasoning; however, they still face challenges such as difficulties in knowledge updating and a scarcity of high-quality domain-specific datasets when tackling knowledge-intensive tasks in the field of civil aviation regulations. This study introduces a retrieval-augmented generation (RAG) approach that integrates retrieval modules with generative models to enable more efficient knowledge acquisition and updating, encompassing data processing and retrieval-based reasoning. The data processing stage comprises document conversion, information extraction, and document parsing modules. Additionally, a high-quality airworthiness regulation QA dataset was specifically constructed, covering multiple-choice, true/false, and fill-in-the-blank questions, with a total of 4688 entries. The retrieval-based reasoning stage employs vector search and re-ranking strategies, combined with prompt optimization, to enhance the model’s reasoning capabilities in specific airworthiness certification regulation comprehension tasks. A series of experiments demonstrate the effectiveness of the retrieval-augmented generation approach in this domain, significantly improving answer accuracy and retrieval hit rates. Full article

Recently, the application of large language models (LLMs) in psychology has gained increasing attention. However, their psychological competence still requires further investigation. This study explores this issue through the lens of Chinese psychological knowledge question answering (QA). Specifically, we constructed a dedicated dataset based on Chinese qualification examinations for psychological counselors and psychotherapists. Subsequently, we evaluated dense, Mixture-of-Expert, and reasoning LLMs with varying parameter sizes and evaluation modes in the Chinese context, measuring answer accuracy in both closed-ended and open-ended settings. The experimental results showed that the larger and more recent LLMs achieved higher accuracy in psychological QA. While few-shot learning led to improvements in accuracy, Chain-of-Thought prompting and reasoning LLMs provided only limited gains. Notably, LLMs achieved higher accuracy in closed-ended settings than in open-ended ones. Furthermore, error analysis indicated that LLMs can produce incorrect or hallucinated responses, primarily due to insufficient psychological knowledge and conceptual confusion. Although current LLMs show promise in psychological QA tasks, users should remain cautious about over-reliance on their responses. A complementary, human-AI collaborative approach is recommended for practical use. Full article

The rapid retrieval and precise acquisition of land use approval information are crucial for enhancing the efficiency and quality of land use approval, as well as for promoting the intelligent transformation of land use approval processes. As an advanced retrieval method, question-answering (Q&A) technology has become a core technical support for addressing current issues such as low approval efficiency and difficulty in obtaining information. However, existing Q&A technologies suffer from significant hallucination problems and limitations in considering spatiotemporal factors in the land use approval domain. To effectively address these issues, this study proposes a spatiotemporal–semantic coupling intelligent Q&A method for land use approval based on knowledge graphs (KGs) and intelligent agent technology, aiming to enhance the efficiency and quality of land use approval. Firstly, a land use approval knowledge graph (LUAKG) is constructed, systematically integrating domain knowledge such as policy clauses, legal regulations, and approval procedures. Then, by combining large language models (LLMs) and intelligent agent technology, a spatiotemporal–semantic coupling Q&A framework is designed. Through the use of spatiotemporal analysis tools, this framework can comprehensively consider spatial, temporal, and semantic factors when handling land approval tasks, enabling dynamic decision-making and precise reasoning. The research results show that, compared to traditional Q&A based on LLMs and Q&A based on retrieval-enhanced generation (RAG), the proposed method improves accuracy by 16% and 9% in general knowledge Q&A tasks. In the project review Q&A task, F1 scores and accuracy increase by 2% and 9%, respectively, compared to RAG-QA. Particularly, under the spatiotemporal–semantic multidimensional analysis, the improvement in F1 score and accuracy ranges from 2 to 6% and 7 to 10%, respectively. Full article

Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and enhances answer generation by incorporating Knowledge Graphs (KGs) built upon a document’s intrinsic structure into the RAG pipeline. Through the application of the Design Science Research methodology, we systematically design, implement, and evaluate GraphRAG, leveraging graph-based document structuring and a keyword-based semantic linking mechanism to improve retrieval quality. The evaluation, conducted on well-established datasets including SQuAD, HotpotQA, and a newly developed manufacturing dataset, demonstrates consistent performance gains over a naive RAG baseline across both retrieval and generation metrics. The results indicate that GraphRAG improves Context Relevance metrics, with task-dependent optimizations for chunk size, keyword density, and top-k retrieval further enhancing performance. Notably, multi-hop questions benefit most from GraphRAG’s structured retrieval strategy, highlighting its advantages in complex reasoning tasks. Full article

Background: Pre-Trained Language Models hold significant promise for revolutionizing mental health care by delivering accessible and culturally sensitive resources. Despite this potential, their efficacy in mental health applications, particularly in the Arabic language, remains largely unexplored. To the best of our knowledge, comprehensive studies specifically evaluating the performance of PLMs on diverse Arabic mental health tasks are still scarce. This study aims to bridge this gap by evaluating the performance of pre-trained language models in classifying questions and answers within the mental health care domain. Methods: We used the MentalQA dataset, which comprises Arabic Questions and Answers interactions related to mental health. Our experiments involved four distinct learning strategies: traditional feature extraction, using PLMs as feature extractors, fine-tuning PLMs, and employing prompt-based techniques with models, such as GPT-3.5 and GPT-4 in zero-shot and few-shot learning scenarios. Arabic-specific PLMs, including AraBERT, CAMelBERT, and MARBERT, were evaluated. Results: Traditional feature-extraction methods paired with Support Vector Machines (SVM) showed competitive performance, but PLMs outperformed them due to their superior ability to capture semantic nuances. In particular, MARBERT achieved the highest performance, with Jaccard scores of 0.80 for the question classification and 0.86 for the answer classification. Further analysis revealed that fine-tuning PLMs enhances their performance, and the size of the training dataset plays a critical role in model effectiveness. Prompt-based techniques, particularly few-shot learning with GPT-3.5, demonstrated significant improvements, increasing the accuracy of question classification by 12% and the accuracy of answer classification by 45%. Conclusions: The study demonstrates the potential of PLMs and prompt-based approaches to provide mental health support to Arabic-speaking populations, providing valuable tools for individuals seeking assistance in this field. This research advances the understanding of PLMs in mental health care and emphasizes their potential to improve accessibility and effectiveness in Arabic-speaking contexts. Full article

In recent years, the rapid development of the new energy vehicle (NEV) industry has exposed significant deficiencies in intelligent fault diagnosis and information retrieval technologies, especially in intelligent fault information retrieval, which faces persistent challenges including inadequate system adaptability and reasoning bottlenecks. To address these challenges, this study proposes a Retrieval-Augmented Generation (RAG) framework that integrates large language models (LLMs) with knowledge graphs (KGs). The framework consists of three key components: fault data collection, knowledge graph construction, and fault knowledge model training. The primary research contributions are threefold: (1) A domain-optimized fine-tuning strategy for LLMs based on NEV fault characteristics, verifying the superior accuracy of the Bidirectional Encoder Representations from Transformers (BERT) model in fault classification tasks. (2) A structured knowledge graph encompassing 122 fault categories, developed through the ChatGLM3-6B model completing named entity and knowledge relation extraction to generate fault knowledge and build a paraphrased vocabulary. (3) An intelligent fault information retrieval system that significantly outperforms traditional models in NEV-specific Q&A scenarios, providing multi-level fault cause analysis and actionable solution recommendations. Full article

To enhance the accessibility and accuracy of plant protection knowledge for agricultural practitioners, this study develops an intelligent question-answering (QA) system based on a large language model (LLM). A local knowledge base was constructed by vectorizing 7000 research papers and books in the field of plant protection, from which 568 representative papers were selected to generate QA data using an LLM. After data cleaning and filtering, a fine-tuning dataset comprising 9000 question–answer pairs was curated. To optimize the model’s performance, low-rank adaptation (LoRA) was applied to the InterLM-20B model, resulting in the InterLM-20B-LoRA, which was integrated with Langchain-ChatChat and the local knowledge base to develop the QA system. Additionally, retrieval-augmented generation (RAG) technology was implemented to enhance response accuracy by enabling the model to retrieve relevant field-specific knowledge before generating answers, effectively mitigating the risk of hallucinated information. The experimental results demonstrate that the proposed system achieves an answer accuracy of 97%, highlighting its potential as an advanced solution for intelligent agricultural QA services. Full article

Table-based question answering (TableQA) has emerged as an important task in natural language processing, yet existing models face challenges in handling complex reasoning and mitigating hallucinations, especially when dealing with diverse table structures. We introduce TQAgent, a framework designed to enhance table-based reasoning by incorporating knowledge graphs and tree-structured reasoning paths. TQAgent reduces hallucinations and improves model reliability by grounding reasoning in external knowledge and dynamically sampling high-confidence paths. Additionally, it employs knowledge distillation techniques for lightweight deployment. Experimental results on the TabFact, WikiTQ, and FeTaQA datasets show significant performance improvements, with accuracy increases of up to 4% over baseline models. TQAgent’s dynamic operation planning and knowledge graph integration enable effective multi-step reasoning and better handling of diverse table data. Furthermore, the framework achieves state-of-the-art results, surpassing traditional large-scale models in both reasoning accuracy and computational efficiency. These findings open new avenues for future research in table-based question answering and model deployment optimization. Full article

While large language models (LLMs) are vital for retrieving relevant information from extensive knowledge bases, they always face challenges, including high costs and issues of credibility. Here, we developed a question answering system focused on human health risk using Retrieval-Augmented Generation (RAG). We first proposed a framework to generate question–answer pairs, resulting in 300 high-quality pairs across six subfields. Subsequently, we created both a Naive RAG and an Advanced RAG-based Question-Answering (Q&A) system. Performance evaluation of the 300 question–answer pairs in individual research subfields demonstrated that the Advanced RAG outperformed traditional LLMs (including ChatGPT and ChatGLM) and Naive RAG. Finally, we integrated the developed module for a single subfield to launch a multi-knowledge base question answering system. Our study represents a novel application of RAG technology and LLMs to optimize knowledge retrieval methods in human health risk assessment. Full article

In China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of professional knowledge that represent the cutting-edge progress in fruit disease research. Traditional knowledge engineering methods for knowledge acquisition require extensive and cumbersome preparatory work, and they demand a high level of professional background and information technology skills from the handlers. This paper, from the perspective of fruit tree industry knowledge dissemination, aims at users such as fruit farmers, fruit tree experts, fruit tree knowledge communicators, and information gatherers. It proposes a fast, cost-effective, and low-technical-barrier method for extracting fruit tree disease knowledge from research paper abstracts—K-Extract, based on large language models (LLMs) and prompt engineering. Under zero-shot conditions, K-Extract utilizes conversational LLMs to automate the extraction of fruit tree disease knowledge. The K-Extract method has constructed a comprehensive classification system for fruit tree diseases and, through a series of optimized prompt questions, effectively overcomes the deficiencies of LLM models in providing factual accuracy. This paper tests multiple LLM models available in the Chinese market, and the results show that K-Extract can seamlessly integrate with any conversational LLM model, with the DeepSeek model and the Kimi model performing particularly well. The experimental results indicate that LLM models have a high accuracy rate in handling judgment tasks and simple knowledge Q&A tasks. The K-Extract method is simple, efficient, and accurate, and can serve as a convenient tool for knowledge extraction in the agricultural field. Full article

Background: Several studies highlighted that tailored health communication interventions improve cervical screening participation, vaccination coverage, and awareness about self-sampling benefits. The “COMUNISS” project was aimed at increasing awareness about cervical cancer prevention, identifying barriers to screening, and promoting screening uptake in under-screened women. Methods: A dedicated website with a Q&A session regarding HPV-associated diseases has been set up. Participants were invited to complete a questionnaire to gather demographic information, knowledge about HPV and cervical cancer, and attitudes toward screening based on the Health Beliefs Model (HBM). Women can also require a vaginal self-sampling kit at your home to perform the HPV-DNA analysis. Results: The website registered over 1000 users over 6 months, and 256 women completed the survey. Nearly half were under-screened. The HBM revealed a high susceptibility and severity perception of diseases, regardless of screening participation, whereas older women declared a high perception of barriers. One-quarter of the women who had requested the self-collection kit returned it for the HPV-DNA testing. Conclusions: The project found significant gaps in knowledge regarding extra-cervical HPV-related cancers, interpretation of screening results, and effectiveness of self-collection. These findings highlight the need to plan targeted information campaigns to enhance awareness and adherence to screening programs. Full article

The LBS2ITS project, titled “Curricula Enrichment Delivered through the Application of Location-Based Services to Intelligent Transport Systems”, is a collaborative initiative funded by the Erasmus+ program of the European Union. The primary objectives of the project were twofold: to develop new curricula and modernize existing programs at four universities in Sri Lanka. This effort was driven by the need to align educational offerings with the rapidly evolving fields of Location-Based Services (LBSs) and Intelligent Transport Systems (ITSs). A key feature of the LBS2ITS project is its interdisciplinary approach, which draws on expertise from a range of academic disciplines. The project has successfully developed curricula that integrate diverse fields such as geomatics, cartography, transport engineering, urban planning, environmental engineering, and computer science. By blending these perspectives, the curricula provide students with a holistic understanding of LBSs and ITSs, preparing them to address complex, real-world challenges that span multiple sectors. In this paper, the curriculum development and modernization process is detailed, with a particular focus on the two key phases: teacher training and curriculum development. The teacher training phase was crucial in equipping educators with the skills and knowledge necessary to deliver the new and updated courses. This phase also provided an opportunity for teachers to familiarize themselves with the latest trends and technologies in LBSs and ITSs, ensuring that they could effectively convey this information to students. The development phase focused on the creation of the curriculum itself, ensuring that it met both academic standards and industry needs. The curriculum was designed to be flexible and responsive to emerging technologies and methodologies, allowing for continuous improvement and adaptation. Additionally, the paper delves into the theoretical frameworks underpinning the methodologies employed in the project. These include Problem-Based Learning (PBL) and Problem-Based e-Learning (PBeL), both of which encourage active student engagement and foster critical thinking by having students tackle real-world problems. The emphasis on PBL ensures that students not only acquire theoretical knowledge but also develop practical problem-solving skills applicable to their future careers in LBSs and ITSs. Furthermore, the project incorporated rigorous quality assurance (QA) mechanisms to ensure that the teaching methods and curriculum content met high standards. This included regular feedback loops, stakeholder involvement, and iterative refinement of course materials based on evaluations from both students and industry experts. These QA measures are essential for maintaining the relevance, effectiveness, and sustainability of the curricula over time. In summary, the LBS2ITS project represents a significant effort to enrich and modernize university curricula in Sri Lanka by integrating cutting-edge technologies and interdisciplinary approaches. Through a combination of innovative teaching methodologies, comprehensive teacher training, and robust quality assurance practices, the project aims to equip students with the skills and knowledge needed to excel in the fields of LBSs and ITSs. Full article