Aug 2026· Discover Computing· Vol 29· 0 citations· 27 references
TL;DR
This study presents an automated knowledge extraction and prediction system using the advancements in Artificial Intelligence (AI) tools, referred to as APEX-LLM, which is a scalable, domain-independent system which can be customized and applied to health, financial and business sectors, and education.
Abstract
The rapid proliferation of unstructured data on digital platforms has generated an urgent demand for intelligent systems that can extract meaningful knowledge and produce accurate predictions. This study presents an automated knowledge extraction and prediction system using the advancements in Artificial Intelligence (AI) tools, which is referred to as APEX-LLM. The proposed system uses the latest architectures based on transformers for processing massive textual data, mining relevant entities, relationships, and patterns, and structuring them into knowledge-based representations. The framework integrates knowledge retrieval, natural language understanding, and machine learning techniques for immediate retrieval of knowledge from other sources such as documents, web content and databases. Furthermore, there is an integration of elements related to predictive modelling to analyze the knowledge extracted and predict trends, outcomes, or an action in various regions. It is a scalable, domain-independent system which can be customized and applied to health, financial and business sectors, and education. The experimental analysis demonstrates that the proposed method is much more accurate and efficient than the traditional rule-based and statistical methods. The results demonstrate that the LLM-based system has an overall automation rate of 99.2%, which is significantly higher than the 90.7% for rule-based and 83.9% for statistical methods, thereby minimizing the human factor by 99.6% and enhancing the accuracy of decision-making to 99.4%. This APEX-LLM will be added to the emerging branch of AI-enabled knowledge systems, offering a single solution for extraction and prediction tasks.
Experimental results show that AI-driven knowledge graphs significantly enhance decision accuracy, reduce ambiguity, and improve interpretability, achieving up to 85–92% higher decision efficiency compared to traditional methods.
Venkatesh Iyer, Nandhini Ravi· International Journal of Art...· 0 citations
This systematic literature review examines the application of artificial intelligence (AI) and natural language processing (NLP) techniques in intelligent business document processing. The study systematically analyses 46 peer-reviewed articles published between 2014 and 2025 and indexed in the Scopus database. The reviewed literature was grouped into six core NLP-based analytical tasks: semantic search, question answering, summarisation, text data integration and matching, event extraction, and business process management. The findings show that AI- and NLP-based methods have significantly improved the automation, retrieval, interpretation, and structuring of business documents. Semantic search methods enhance information retrieval by moving beyond keyword matching, while question-answering systems and summarisation techniques support automated knowledge discovery and content reduction. Deep learning and transformer-based models have also improved entity matching, event extraction, and predictive business process monitoring. However, the review identifies several persistent limitations, including the continued dominance of extractive approaches, limited adoption of abstractive summarisation, insufficient integration of knowledge graphs, fragmented system development, limited enterprise-scale validation, and a lack of reusable code and shared resources. The findings further indicate that large language models (LLMs), particularly when combined with prompt engineering, retrieval-augmented generation, knowledge graphs, and agent-based architectures, offer promising opportunities to address these gaps. Overall, this review highlights both the progress and remaining challenges in developing scalable, explainable, and domain-adaptable AI-driven systems for intelligent business document processing.
Naif N. Alotaibi, Morteza Saberi, M. Bandara et al.· Analytics· 0 citations
Scientific publishing, digital repositories, patents, and multidisciplinary research datasets have expanded rapidly, making traditional literature review methods increasingly inefficient. Large Language Models (LLMs) address this challenge by enabling intelligent knowledge discovery, semantic search, literature summarization, research gap identification, hypothesis generation, citation assistance, and academic writing support. By integrating Retrieval-Augmented Generation (RAG), vector databases, knowledge graphs, citation networks, and domain-specific ontologies, LLMs improve contextual relevance, reduce hallucinations, and enhance research accuracy. These capabilities accelerate interdisciplinary collaboration, automate research workflows, and support evidence-based decision-making. However, challenges such as hallucination, bias, outdated knowledge, explainability, privacy, intellectual property, reproducibility, and computational requirements remain significant. Modern AI-assisted research systems increasingly incorporate human-in-the-loop validation, explainable AI, and responsible governance to ensure trustworthy outcomes. This study presents a conceptual framework that combines semantic retrieval, intelligent reasoning, automated literature analysis, and workflow orchestration, demonstrating how LLM-powered systems can transform scientific research into scalable, accurate, ethical, and collaborative knowledge discovery processes.
Narendra Karmarkar, Iyengar P.K· International Journal of Eme...· 0 citations
The rapid development of Artificial Intelligence (AI) has significantly transformed organizational operations, particularly in Knowledge Management Systems (KMS). In today’s dynamic and data-driven environment, competitive advantage depends on effectively capturing, storing, retrieving, and utilizing knowledge. AI-based KMS (AI-KMS) represent an evolution from traditional systems, shifting from rule-based repositories to intelligent, adaptive, and context-aware platforms that enhance organizational intelligence. Traditional KMS faced limitations in scalability and efficiency due to reliance on structured data and manual input. In contrast, AI-enabled systems leverage machine learning, natural language processing, and data analytics to process unstructured data such as documents, emails, and multimedia, enabling semantic search, personalized recommendations, and predictive insights. These systems also support continuous learning by automatically updating knowledge bases through user interactions. This paper examines the architecture of AI-KMS, focusing on components like knowledge acquisition modules, inference engines, and user interfaces, along with the integration of deep learning and ontologies for improved knowledge representation. It also addresses key challenges including data quality, privacy, scalability, and ethical concerns. A detailed literature review traces the evolution of KMS and AI integration prior to 2018. The proposed methodology uses a hybrid model combining supervised and unsupervised learning for knowledge extraction and classification. Experimental results show improved accuracy in knowledge retrieval and decision-making efficiency compared to traditional systems, supported by quantitative analysis. Overall, AI-KMS enhance organizational intelligence by accelerating decisions, fostering collaboration, and driving innovation. The paper concludes by recommending future research in explainable AI, governance frameworks, and integration with emerging technologies like IoT and blockchain.
Z. Yusuf, Vinoj M· International Journal of Art...· 0 citations
Knowledge Management Systems (KMS) are required to organize and assign meaning to huge amounts of organizational knowledge that are largely in the form of unstructured text. Natural Language Processing (NLP), and more immediately methods of text categorization, has been one of the principal enabler technologies to enable KMS to be simpler by helping to automatically categorize documents, enhance searching for information, and assist in decision-making. This paper offers an outline of the evolution of NLP-based text classification methods from initial machine learning methods such as Naïve Bayes and Support Vector Machines to current sophisticated deep learning algorithms such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformers. We offer real-world industry use cases, issues of scalability, explainability, and ethics and encapsulate research areas of existing gaps. The findings underscore the enormous potential of NLP text classification to assist the effectiveness and efficiency of knowledge management (KM) activities.
Unknown authors· Engineering and Technology J...· 0 citations
To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph construction method for the workpiece machining distortion domain, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models. First, a domain ontology model is established around core concepts, including workpiece objects, deformation-driving factors, analytical resources, analytical methods, and optimization knowledge, thereby providing a unified semantic foundation for domain knowledge organization. Second, considering the characteristics of domain texts, such as dense technical terminology, ambiguous entity boundaries, and complex relation expressions, a dual-channel knowledge extraction method integrating BERT-BiLSTM-CRF and Universal Information Extraction (UIE) is developed to achieve high-precision extraction of entities and relations from unstructured texts. Knowledge fusion is further carried out through cross-validation, entity disambiguation, coreference resolution, and semantic alignment, and the extracted knowledge is ultimately stored and organized in Neo4j. Furthermore, an intelligent decision-making framework based on the collaboration of knowledge graphs and large language models is constructed. In this framework, a LoRA-tuned Qwen model is employed for user intent recognition and key information extraction, RapidFuzz WRatio is adopted for similar-node retrieval, and local subgraph construction, Label Propagation-based community detection, Betweenness Centrality-based key-node analysis, and evidence fusion are integrated to support process recommendation and intelligent question answering. Based on the proposed framework, an intelligent decision-making system is further developed for process recommendation and intelligent question answering in machining distortion scenarios. Experimental results show that the proposed dual-channel knowledge extraction model achieves an F1-score of 0.88, demonstrating its effectiveness in knowledge acquisition for the machining distortion domain. The constructed knowledge graph contains 4639 entities and 5822 relations, enabling a systematic representation of machining distortion knowledge. Case studies further demonstrate that the proposed method can generate interpretable recommendation results under complex process constraints in real industrial query scenarios. Overall, the proposed approach provides a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge.
Deguo Yao, Zhaoze Sun, Jie Gao et al.· Applied System Innovation· 0 citations