knowledge management

This paper addresses the long-standing systemic content gaps in the Wikidata knowledge graph. Through a systematic literature review and theoretical modeling, we propose the first integrative, multidimensional gap typology and conceptual framework, categorizing gaps into structural, topical, linguistic, and editor-behavior–associated types, and establishing an operational measurement-to-indicator mapping system. Our framework transcends prior descriptive, empirically grounded approaches by identifying— for the first time—a previously overlooked, collaboration-mechanism–driven gap dimension, and by clarifying coverage blind spots in existing evaluation methods and their critical linkages to editor behavior. The resulting framework provides a reusable theoretical foundation and methodological toolkit for knowledge graph content quality assessment, systemic bias identification, and mitigation. (149 words)

In the era of large language models, traditional record-centric data engineering struggles to meet the demand for organizational knowledge as executable infrastructure. This work proposes a novel paradigm—knowledge architecture—that systematically reimagines core data engineering mechanisms by upgrading ETL, data lineage, and catalogs into knowledge ingestion, change detection, provenance, and knowledge catalogs. It introduces knowledge views and a three-tier layered model (raw–refined–operational) to structure knowledge effectively. By integrating emerging standards such as LLM Wiki and Open Knowledge Format (OKF), this study formally defines knowledge architecture for the first time and establishes a theoretical framework that supports knowledge representation, governance, and operational delivery, enabling direct invocation of organizational knowledge by humans, agents, workflows, and models alike.

This study addresses the lack of a unified definition and modeling approach for knowledge-intensive processes, which hinders the efficiency and quality of digital transformation. By integrating theories from knowledge management and process modeling, the authors analyze the relationships among data, objects, artifacts, and personnel to propose a general definition of knowledge-intensive processes and develop a taxonomy classifying such processes into six levels of knowledge intensity. Drawing on real-world scenarios, they systematically identify representative knowledge process patterns and construct an integrated framework that supports modeling, execution, monitoring, and impact assessment. This work provides both a theoretical foundation and practical tools for standardizing and effectively implementing knowledge-intensive processes, thereby significantly enhancing the outcomes of digital transformation initiatives.

This study addresses persistent challenges in scientific communication and aerospace engineering—namely data silos, insufficient collaboration incentives, and legal barriers—that hinder the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) principles. To overcome these limitations, this work proposes a novel, scalable knowledge infrastructure framework that integrates human–AI collaboration, knowledge graphs, and user-centered design across technological, social, and legal dimensions. The framework encompasses automated information processing workflows, a wiki-style digital library, and demand-driven interactive interfaces. Pilot implementations demonstrate its effectiveness in consolidating fragmented knowledge resources and establishing a viable collaborative paradigm for sparsely networked domains. Nevertheless, institutional and sociocultural barriers remain significant and require further intervention to fully realize the framework’s potential.

This study investigates the root causes of ineffective knowledge management in multinational IT corporations, specifically addressing knowledge attrition and collaborative inefficiency stemming from the “cognition–practice gap.” Drawing on semi-structured focus group interviews with 50 employees, the research employs thematic coding and multi-dimensional causal analysis to systematically identify— for the first time—five interrelated categories of knowledge-sharing barriers: individual socio-cognitive, organizational socio-cultural, technological, environmental, and socio-technical. The study makes two key contributions: (1) a theoretically grounded, empirically validated framework for diagnosing knowledge management failures; and (2) actionable, behavior-oriented interventions—including personalized training, lightweight operational guidelines, and embedded institutional refinements—designed to enhance knowledge retention and cross-functional collaboration. Findings offer a transferable, evidence-based blueprint for knowledge-intensive organizations seeking scalable improvements in knowledge governance and collective efficacy.

Current agent capabilities lack large-scale infrastructure for production, governance, and evolution akin to Wikipedia and GitHub. This work proposes SkillWiki, the first dynamic knowledge infrastructure that enables symbiotic co-evolution of knowledge, skills, and execution experiences. By integrating knowledge extraction, skill assetization, provenance tracking, and an execution-driven feedback loop, SkillWiki establishes an end-to-end framework for the full lifecycle management of skills, supporting reusable organization, verifiable provenance, and continuous evolution. The system has been fully validated through a pipeline spanning knowledge ingestion, skill generation, and execution-driven refinement. Both the codebase and the system are publicly open-sourced.

Reproducibility, reuse of research data and methods, and discovery of heterogeneous scholarly resources remain severely hindered by high heterogeneity in both resources and metadata, compounded by the prevalence of unstructured, literature-based information. Method: This paper introduces the “Research Knowledge Graph” (RKG) paradigm—the first systematic formalization of its kind—comprising a conceptual framework, taxonomy, and core architectural modules. It integrates persistent identifier (PID) management, RDF/OWL-based semantic modeling, cross-source vocabulary alignment, and trustworthy data integration to enable structured representation and interoperability of multi-source research assets. Contribution/Results: Through a comprehensive survey spanning scale, modeling paradigms, ontologies, data sources, and trustworthiness criteria, we characterize empirical RKG implementations, map their methodological landscape, identify application pathways, and pinpoint critical technical and organizational challenges—thereby providing foundational theoretical insights and actionable engineering guidance for RKG development and deployment.

This study addresses the lack of understanding regarding why and how information professionals in memory institutions—such as archives, libraries, and museums—integrate Wikidata into their organizational workflows. Using semi-structured interviews and a CSCW-informed theoretical framework, it conducts qualitative analysis to uncover motivations and practices. The research identifies three distinct professional roles in Wikidata engagement: providers (contributing institutional data), consumers (reusing Wikidata for internal purposes), and reciprocators (engaging in bidirectional collaboration with volunteer communities). It elucidates institutional drivers—including enhanced data visibility and support for knowledge infrastructure—as well as concrete implementation pathways. Crucially, the study affirms the value of professional data curation and proposes actionable strategies for institutionalizing expert contributions. These findings advance theoretical reconceptualization and practical development of institutional roles within open-data ecosystems. (149 words)

High faculty turnover in higher education institutions leads to teaching knowledge loss, imposes heavy documentation burdens on domain experts, and creates steep learning curves for novices. Method: This study proposes a human-centered Visual Process Representation (VPR) framework that integrates Sequential Pattern Mining (SPM) with log data analysis to automatically transform instructor interaction logs into intuitive, story-like visual narratives—eliminating the need for manual process documentation. Contribution/Results: VPR innovatively unifies knowledge management with narrative visualization, supporting dual-modal (text + image) presentation. A user-perception evaluation involving 160 university instructors demonstrated that VPR significantly improves task completion efficiency, system usability, and user engagement; enhanced visual representations yielded superior outcomes. The framework provides a scalable, low-intervention, automated solution for knowledge retention and transfer in educational organizations.

This work addresses the long-standing absence of a unified knowledge management infrastructure in aerospace engineering, which has led to data fragmentation and redundant research efforts. To remedy this, the study introduces Wikibase into the domain for the first time, establishing an open, extensible, and decentralized collaborative knowledge graph platform. Through a systematic literature review, the authors structurally extracted and integrated over 700 core terms, constructing a standardized, reusable, and interlinkable conceptual framework. The platform is designed to simultaneously support public knowledge co-creation and safeguard project-specific private information, thereby providing a sustainable infrastructural foundation for cross-team collaboration and long-term knowledge accumulation in aerospace engineering.

Organizations face significant challenges in identifying, acquiring, and structuring tacit knowledge—manifested by incomplete initial information, difficulty locating domain experts, entanglement of formal hierarchies and informal networks, and unclear questioning strategies. Method: This paper proposes a large language model (LLM)-driven agent framework integrating agent-based modeling (ABM), self-critical feedback mechanisms, and the Susceptible-Infectious (SI) model of knowledge diffusion. It iteratively reconstructs fragmented expertise through simulated employee interactions, enabling tacit knowledge recovery without direct expert engagement. Contribution/Results: Across 864 simulation trials, the framework achieves a 94.9% complete knowledge recall rate. Self-critical evaluation scores correlate strongly with external literature-based assessments (Pearson’s r > 0.92), demonstrating the method’s validity, robustness, and scalability for organizational knowledge engineering.