Artificial Intelligence in Knowledge Management

Permanent URI for this collectionhttps://hdl.handle.net/10125/112519

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Now showing 1 - 7 of 7

AI in KM: A Framework and Practitioner Insights
(2026-01-06) Jennex, Murray; Abby Sen, Abraham; Joy, Jeen
This paper presents a framework for integrating artificial intelligence (AI) into knowledge management (KM) using the Jennex–Olfman KM Success Model as a foundation. Through a literature review and a thematic analysis of 400 practitioner comments from the global SIKM Leaders community, the study examines how AI is being applied in KM and the implications for practice. Findings highlight that AI expands KM across diverse sectors, enhances efficiency through automation and workflow integration, and supports human judgment in knowledge tasks. At the same time, risks concerning bias, accuracy, transparency, governance, and infrastructure remain central challenges. Mapping these insights to the KM Success Model shows that AI strengthens system and knowledge quality while requiring leadership and governance to safeguard service quality. The analysis extends the model by emphasizing explainability, transparency, and human oversight as critical for AI-enabled KM. Overall, the study concludes that AI offers significant potential to advance KM success, but its value depends on careful alignment with organizational strategy, investment in infrastructure, and sustained commitment to ethical and responsible implementation.
Design and Implementation of a Semiconductor Supply Chain Knowledge Graph for Developing a GraphRAG-Based Question Answering Model
(2026-01-06) Ham, Seunghoon; Lee, Kangbae; Park, Sungho; Kang, Jisu; Hwang, Hyeonji
A knowledge graph (KG) supports effective knowledge extraction and management, driving active research in supply chain management (SCM). This study constructs a KG representing the semiconductor manufacturing supply chain and develops a GraphRAG-based question answering (QA) model. Representing such processes as a KG requires domain-specific ontology design, which is typically manual, time-consuming, and costly. To address this, we adapt an existing ontology from the automotive manufacturing domain to the semiconductor context. The QA model integrates the domain ontology into Chain-of-Thought (CoT) prompts to generate accurate responses to complex queries. This approach improves knowledge retrieval efficiency, mitigates the context-length limitations of a large language model (LLM), and enables interpretable answers by exposing the model’s reasoning process. We quantitatively evaluate the proposed GraphRAG model against the VectorRAG baseline, demonstrating superior performance in BERTScore and F1-score metrics. This framework highlights the benefits of combining ontological knowledge and CoT prompting for enhancing graph-based QA systems.
Probabilistic Soft Logic for Toxic Intent Prediction in Conversation: a Moral Foundations Theory-Driven Neural-Symbolic Framework
(2026-01-06) Falade, Tope; Agarwal, Nitin
Transformer-based models (RoBERTa, DeBERTa, ToxicBERT) achieve high accuracy but suffer critical limitations: they operate as black boxes, classify content based on surface features regardless of toxic intent, and exhibit biases when profanity is present. We present a neural-symbolic framework that integrates Probabilistic Soft Logic with Moral Foundations Theory, enabling accurate prediction of toxic intent and moral reasoning. Our approach improves prediction accuracy, provides interpretable explanations, and demonstrates synergistic effects. We evaluated 748,283 toxic instances (from a total of 1,268,154 conversations) across Telegram war discourse and Reddit climate debates. The results show a 4.9 percentage point improvement over the strongest transformer baseline with statistical significance. The framework provides interpretable explanations for 91.3\% of predictions through PSL rules grounded in moral psychology. Ablation analysis confirms genuine neural-symbolic synergy with full integration, achieving F1=0.847, significantly outperforming individual neural and symbolic components. This advances ethical AI and conversation moderation.
Adaptive Machine Learning for Dynamic Environments: Evaluating Data Drift-Triggered Retraining in COVID-19 Severity Prediction
(2026-01-06) Ryu, Young; Ayvaci, Mehmet; Jacob, Varghese; Agrawal, Harshal
Machine learning (ML) models deployed in operational systems often face performance degradation due to data drift. Designing effective, scalable strategies for adaptive model maintenance remains an open challenge. This study evaluates four retraining strategies—naïve (static), periodic, clinically guided context-driven, and data drift-triggered—in a dynamic, high-stakes environment: predicting severe COVID-19 outcomes. Using a large-scale CDC dataset, we find that data drift-triggered retraining strikes an effective tradeoff between predictive performance and retraining cost. It matches the performance of periodic retraining while requiring far fewer retraining cycles and offers a fully automated mechanism for adaptive learning. In contrast, context-driven retraining performs well and requires fewer retraining cycles but depends on expert input and lacks automation. Our findings provide empirical insights and practical guidance for designing adaptive ML systems in dynamic environments, with implications for both researchers and practitioners deploying ML models in healthcare and other domains with evolving data landscapes.
Trust Anchors as Pillars of Humanistic Governance in AI-Driven Knowledge Management
(2026-01-06) Kannan, Selvi
As knowledge management systems increasingly rely on artificial intelligence, it becomes imperative to reassert the significance of human agency in shaping, guiding, and governing these technologies. This paper introduces the concept of Trust Anchors, a novel theoretical framework developed which identifies critical human touchpoints that stabilize and ethically ground AI-driven knowledge processes. Drawing from traditions in self-monitoring ethics, organizational culture, and social governance, Trust Anchors function as multi-level mechanisms that embed individual, organizational, and societal principles into AI systems enabling responsible intervention, contextual judgement, and alignment with human values. This paper proposes a humanistic stewardship governance approach to digital knowledge ecosystems by identifying the relationship between humans and machines. It contributes to the discourse on innovation of AI in KM by situating knowledge translation as a dynamic, relational process in which trust, culture, and care are essential for transformation and sustainable knowledge management.
Integrating Blockchain and Ontologies in Artificial Intelligence Regulation
(2026-01-06) Idemudia, Efosa; Elnagar, Samaa; Goel, Rajni
Artificial intelligence has evolved to possess advanced cognitive abilities, leading to a heightened awareness of ethical and legal dilemmas and potential threats. The training of AI models involves processing data that may violate established legal and moral norms, prompting widespread concern. It is essential to closely monitor and document the deployment of AI models to prevent illicit activities. Despite the establishment of various standardized frameworks for AI governance, achieving transparency and uniformity remains a persistent challenge. This study proposes a novel framework that combines blockchain technology with ontologies to provide a regulation-approval record showing that AI models have passed compliance auditing. The decentralized nature of blockchain ensures no authority over the trained models. Moreover, developing a concise ontology to save AI models in compliance will reduce the cost of keeping the data on the blockchain. Adherence to this framework provides a roadmap for ensuring transparency, semantic traceability, and unified standards of compliance with international ethical and legal standards.
Introduction to the Minitrack on Artificial Intelligence in Knowledge Management
(2026-01-06) Abby Sen, Abraham; Joy, Jeen

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