systems mapping

Digital transformation of public-sector information services faces challenges in restructuring the information ecosystem, stemming from deep coupling among heterogeneous stakeholders at perceptual, linguistic, and conceptual levels—resulting in high epistemic uncertainty that undermines semantic interoperability and decision traceability. To address this, we propose Representation Disentanglement: the first application of representation disentanglement to public-sector knowledge modeling. It hierarchically decouples knowledge complexity across perceptual, semantic, and ontological dimensions, enabling structured separation of knowledge representations. Integrated with ontology-driven conceptual modeling and semantic re-engineering, the approach yields a modeling architecture balancing theoretical rigor and engineering feasibility. Empirical evaluation demonstrates significant improvements in semantic transparency, decision interpretability, and audit verifiability of digital governance systems. The method establishes a traceable, verifiable knowledge infrastructure for intelligent governance platforms, advancing foundational support for accountable and explainable AI-enabled public administration.

This study addresses the challenge of aligning ESG controversy events—extracted from unstructured news—with international normative frameworks (e.g., the UN Global Compact). To bridge this gap, we propose a semi-automated, lightweight ontology construction method that integrates large language models (LLMs) with formal RDF schema design, transforming abstract sustainability principles into reusable, interpretable semantic templates. These templates enable structured event knowledge extraction from news texts and the construction of an ESG controversy knowledge graph explicitly aligned with global standards. Our key contribution is the automated, accurate, and interpretable mapping of normative principles to machine-readable rules—ensuring cross-regional consistency. The resulting framework supports precise identification and semantic provenance tracing of regulatory violations, providing a scalable, transparent knowledge infrastructure for regulatory compliance assessment and sustainable investment decision-making.

The growing complexity of the open-source ecosystem lacks a systematic, evolution-grounded classification theory and tooling, hindering both academic research and industrial practice. Method: We propose the first dynamic, empirically grounded taxonomy of open-source project lifecycles, transcending static typologies by integrating socio-technical perspectives and constructing a reusable four-stage model—Emergence, Growth, Maturity, and Decline—via a mixed-methods design: longitudinal analysis of millions of code repositories, community behavioral mining, in-depth interviews with 67 developers, and grounded-theory coding. Contribution/Results: Validated across two major ecosystems—scientific open source (e.g., Apache projects) and enterprise open source (e.g., Linux Foundation initiatives)—the model significantly enhances explanatory power and discriminative capacity regarding governance models, collaboration mechanisms, and long-term sustainability.

To address limitations in engineering and systems modeling—including insufficient expressiveness of binary relations, implicit semantics, and difficulty representing multilevel structures in diagrammatic notations—this paper proposes a hypernetwork modeling paradigm grounded in *n*-ary relations. Methodologically, it introduces typed hyper-simplices (alpha/beta) to enable explicit semantic binding and defines five semantics-preserving, deterministic structural operators (merge, meet, difference, prune, split), integrated with boundary operators and an ordered role mechanism to form a mechanizable structural algebra. The framework uniformly supports modeling, comparison, decomposition, and reconstruction of both hierarchical and non-hierarchical systems. Under the open-world assumption, it guarantees decidable reasoning and model executability. This work achieves a critical transition from symbolic representation to verifiable, reconstructable system models.

Existing AI governance research predominantly addresses macro-level regulatory principles, leaving a critical gap in enterprise-level implementation frameworks. This paper proposes a three-layer conceptual framework for organizational AI governance—spanning data, models, and systems—structured around the triad of “actor–artifact–mechanism.” It introduces two novel elements: (1) a data-value quantification methodology and (2) formally defined, role-specific AI governance positions. Leveraging literature-driven modeling, multi-dimensional structural decomposition, and cross-layer alignment techniques, the framework is designed for seamless integration into existing corporate governance infrastructures. The resulting implementation pathway bridges the translational gap between high-level regulatory guidance and operational AI governance practice. By unifying theoretical rigor with practical feasibility, this work establishes a new paradigm for institutionalized AI governance, directly addressing the longstanding challenge of converting abstract governance principles into actionable, organizationally embedded practices. (149 words)

This study addresses the empirical gap in understanding power distribution and participation patterns within interoperability standard-setting processes for AI agent protocols. It introduces, for the first time, a large language model–driven mixed-methods framework that integrates automated text annotation, BERTopic neural topic modeling, and multilayer network analysis to conduct a large-scale comparative examination of governance mechanisms led by decentralized autonomous organizations (DAOs) and corporations. Analysis of 4,323 governance records reveals that, although governance form shapes issue focus, both models exhibit pronounced participation inequality. Notably, in permissionless environments, discursive alignment is more densely clustered, suggesting that open governance may foster greater thematic consensus. The findings offer novel empirical evidence and a methodological framework for assessing the institutional design effects in AI governance.

This study addresses the challenge of capturing the dynamic complexity of global multi-relational corporate networks in the semiconductor industry, which traditional proprietary databases—often costly and lagging—struggle to track in a timely manner. The authors propose a generalizable framework that leverages large language models to automatically extract and classify supply chain, collaboration, and ownership relationships from 170 million open web pages, constructing a time-series multi-relational network encompassing over 1,300 firms. Integrating web crawling, natural language processing, and graph analytics, the approach enables high-tempo, automated structuring of relational data, achieving a precision of 0.884 and an F1 score of 0.784 in link extraction. Empirical analysis reveals network contraction during the 2022 chip shortage, a sharp rise in centrality among AI-critical firms, and geographically reconfigured ties driven by geopolitical forces.

This study addresses the structural tensions between regulatory transparency and data sovereignty in the global semiconductor value chain, intensified by the EU’s Carbon Border Adjustment Mechanism (CBAM). Building on the International Data Spaces Association (IDSA) framework, it proposes a RegTech reference architecture integrating Digital Product Passports (DPP), Agentic AI for autonomous compliance, and green fintech to enable trusted sharing of environmental telemetry data across semiconductor–petrochemical supply chains. The architecture differentiates CBAM’s mandatory obligations from Science-Based Targets initiative (SBTi) voluntary commitments and incorporates the complexities of Safe and Sustainable by Design (SSbD) principles. By transforming upstream physical vulnerabilities into closed-loop negative feedback mechanisms, the approach delivers a scalable blueprint for the Taipei–Penang technology corridor, advancing sovereign-controlled, transparent, and sustainable governance of global value chains.

This work addresses the limitation of existing text-to-process modeling approaches, which predominantly focus on control flow while neglecting resource and collaboration perspectives, thereby struggling to generate complete multi-party models. To overcome this, the authors propose a resource-aware generative pipeline that systematically incorporates the resource dimension into large language model (LLM)-driven process modeling for the first time. The method automatically constructs BPMN 2.0 collaboration diagrams from natural language descriptions, explicitly capturing organizational pools, role-based lanes, and inter-organizational message events, and employs an orthogonal layout algorithm for automated diagram arrangement. Experimental results across ten business processes and nine LLMs demonstrate that the approach accurately extracts resource-related information, maintains high control-flow quality, and incurs only minimal runtime overhead, advancing generative process modeling toward more collaborative and resource-complete representations.

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.