Lifecycle-Aligned Taxonomy Overview

• Lifecycle-aligned taxonomy is a structured system that explicitly maps entities’ lifecycle stages to specific transitions, risks, and interventions.
• It employs formal representations such as network models, logical reasoning frameworks, and data provenance models to provide actionable insights in various domains.
• Methodological principles emphasize iterative expansion, expert integration, and normalization to ensure scalability, granularity, and coherent alignment as domains evolve.

A lifecycle-aligned taxonomy is a structured classification system whose categories and relationships are explicitly mapped to the distinct phases or transitions in the lifecycle of a system, artifact, or domain. It enables both static organization and dynamic tracking of changes, actions, or risks as entities progress through defined lifecycle stages. This concept is foundational in domains ranging from economics and enterprise innovation to scientific machine learning, AI safety, and health data privacy. Below, the main methodological elements, operative frameworks, and applications of lifecycle-aligned taxonomies are systematically documented.

1. Lifecycle Staging and Taxonomic Structure

A lifecycle-aligned taxonomy defines explicit stages governing the existence, use, or transformation of entities. Examples include:

• Economic Development: Stages from basic industrial production ("root products") to advanced, specialized exports, structured in a hierarchical product taxonomy network where nodes represent goods and directed edges denote likely activation sequences (Zaccaria et al., 2014).
• Enterprise Systems: Implementation → Shakedown → Onwards/Upwards, with each phase mapped to radical, administrative, or incremental innovation opportunities, bounded by systemic constraints (Lokuge et al., 2020).
• Scientific Machine Learning: Data curation → Learning data preparation → Learning (including training/validation/evaluation), where provenance schema explicitly model phase-specific data, metadata, and transformations (Souza et al., 2020).
• Machine Learning for IoT: Data Acquisition → Model Development → Model Deployment → Model Audit—each module encompassing dedicated sub-taxonomies (e.g., feature selection, distributed training methods, security audit techniques) for the respective lifecycle phase (Qian et al., 2019).
• Health Data Privacy: Creation, Storage, Access, Sharing, Linking, Learning, Destruction; with privacy risks mapped to each stage (e.g., eavesdropping in creation, traceability in storage, re-identification in sharing) and countermeasures triangulated to these stages (Bose et al., 2023).

The alignment is operationalized by constructing hierarchical or multidimensional taxonomies whose relationships track or predict transitions between stages (e.g., a progression from capability accumulation to production of higher-complexity exports in economic taxonomy).

2. Formal Representation and Network Models

Lifecycle-aligned taxonomies frequently employ formal mathematical frameworks for representation and inference:

• Bipartite and Projected Network Matrices: In economic development, the country–product matrix $M_{cp}$ and its projection via $$B_{p,p'} = [1 / \max(u_p, u_{p'})] \sum_c (M_{cp} M_{cp'}) / \sqrt{d_c}$$ yield a directed, sparsified taxonomy whose edges embody causal progressions in capabilities (Zaccaria et al., 2014).
• Logic Reasoning Frameworks: Taxonomic change is encoded in Region Connection Calculus (RCC-5) articulations which relate concepts (e.g., congruence, proper inclusion, overlap, exclusion); maximally informative inferred relations are computed via Answer Set Programming, iteratively refined by human expert adjustment (Franz et al., 2014).
• Data Provenance Models: Scientific ML employs the W3C PROV data model (and extensions such as PROV-ML) to annotate provenance of entities, activities, and agents across lifecycle stages, supporting both retrospective and prospective queries (Souza et al., 2020).
• Security and Privacy Mapping: ML security taxonomies map threat, vulnerability, and control types directly to assets segmented by lifecycle phase (development: data sources, models; operation: deployed systems, environment) with tabular representations capturing the mapping (Kawamoto et al., 2023). Health data privacy similarly uses formulaic representations (e.g., Shamir’s Secret Sharing polynomial, $$f(x) = K + a_1x + \dots + a_{k-1}x^{k-1} \ (\mathrm{mod}\ p)$$ for threshold cryptosystems) and formalized definitions (k-anonymity, t-closeness) to tie protection mechanisms to risk emergence in lifecycle stages (Bose et al., 2023).

3. Methodological Principles for Taxonomy Construction

Key methodological principles in building lifecycle-aligned taxonomies include:

• Iterative Expansion and Granularity Control: For evolving domains (e.g., scientific literature), iterative hierarchical classification algorithms dynamically expand taxonomy width and depth in response to corpus distribution signals (density, unmapped document sets), maintaining granularity and coherence across dimensions such as task, methodology, dataset, or evaluation metric (Kargupta et al., 12 Jun 2025). Algorithms monitor unmapped density via

$$\tilde{\rho}(n_{i,d}) = \left| P_{i,d} - \bigcup_{j=0}^{|N_d^i|} P_{j,d} \right|$$

and expand tree structure adaptively.

• Expert/Reasoner Synergy: Taxonomies tracking taxonomic change (e.g., in biology) must couple domain articulation with automated logical inference, allowing experts to resolve ambiguities and ensure the completeness and consistency of merges across time (Franz et al., 2014).
• Normalization and Filtering: Product taxonomies employ normalization factors sensitive to ubiquity and diversification, followed by algorithmic filtering to retain only the most causally informative links, yielding hierarchically directed networks from raw bipartite relations (Zaccaria et al., 2014).
• Triangulation: Health data privacy taxonomies triangulate among lifecycle stage, risk/concern, and technological countermeasure, ensuring that control deployment is both stage- and risk-aware (Bose et al., 2023).
• Supply Chain Lifecycle Mapping: Evaluation taxonomy frameworks for AI systems explicitly map evaluation types (accuracy, risk, impact, transparency) to each lifecycle stage and stakeholder within an accountable supply chain, ensuring that assessment responsibility and scope are contextually clear (Xia et al., 8 Apr 2024).

4. Application Domains and Impact

Lifecycle-aligned taxonomies have demonstrated impact in a variety of domains:

• Industrial Policy and Economic Development: Policy models use the taxonomy network to predict and stage development, with empirical enabling matrices validating that directed transitions correspond to actual country progressions; e.g., South Korea’s diffusion from root to peripheral nodes in the product taxonomy (Zaccaria et al., 2014).
• Innovation Management: Enterprise system taxonomies map innovation types and potential restraints by lifecycle phase, quantitatively modeling factors (e.g., $E = I \times (1 – R)$, CRI ratios) to forecast where and how innovation is feasible given systemic constraints (Lokuge et al., 2020).
• Scientific Reproducibility: Provenance-aware lifecycles in ML facilitate traceable, context-rich queries that span data curation, learning preparation, and ML training/evaluation, with demonstrated scalability and accelerated query performance in HPC environments (Souza et al., 2020).
• ML System Security: Comprehensive security taxonomies enumerate threats, vulnerabilities, and controls for all assets across ML system development and deployment, establishing explicit mappings for asset-level protection and risk mitigation throughout the lifecycle (Kawamoto et al., 2023).
• Health Data Privacy: Lifecycle-aligned taxonomies provide a phase-by-phase accounting of privacy risks and control mechanisms, with recognition of needs for emergency access, partial sharing, traceable anonymization, and verifiable destruction in distributed, heterogeneous healthcare environments (Bose et al., 2023).

5. Evaluation Metrics, Limitations, and Comparative Assessment

The effectiveness of lifecycle-aligned taxonomies is often measured using explicit metrics:

• Granularity Preservation and Coherence: For automatically constructed taxonomies, metrics such as path granularity and sibling coherence, as well as coverage and alignment with corpus dimensions, serve as quantitative benchmarks; TaxoAdapt, for example, demonstrates 26.51% improvement in granularity preservation and 50.41% improvement in coherence over baselines (Kargupta et al., 12 Jun 2025).
• Empirical Correlation: Economic disposition measures (page-rank-based, e.g., $D_c = \sum_p M_{c,p} (PR_p)^{-1}$) show high correlation ($R^2 = 0.92$) with fitness and growth potential in country-level export data (Zaccaria et al., 2014).
• System Scalability: Scientific ML provenance frameworks report sub-1% capture overhead and order-of-magnitude query acceleration, with nearly linear scalability in cluster experiments (Souza et al., 2020).

Limitations and challenges are primarily tied to complexity—requiring domain expert involvement for alignment tasks, difficulty maintaining clarity as taxonomies or systems become highly multidimensional, and the need for continual update as domains evolve or standards shift. The objective mapping between lifecycle phase and taxonomic class requires precise formalism and ongoing adjustment to emerging phenomena (e.g., research trends, new threats, or shifting regulatory contexts).

6. Future Research Directions

Several domains highlight concrete directions for future research:

• Dynamic Taxonomy Adaptation: Further development of automated expansion and update algorithms, especially those balancing corpus-driven signals against general LLM knowledge to maintain high granularity and coherence over time (Kargupta et al., 12 Jun 2025).
• Provenance Integration and Interoperability: Escalating the integration of workflow provenance with external knowledge graphs, scaling microservices architectures for scientific workflows, and ensuring interoperability across distributed ML environments (Souza et al., 2020).
• Privacy and Security: Advancing cryptographic schemes for emergency access, partial record sharing (content extraction signatures), and traceable anonymization in health data systems (Bose et al., 2023). Development of holistic controls covering all assets in ML system lifecycles, supporting layered risk mitigation in increasingly complex AI supply chains (Kawamoto et al., 2023, Xia et al., 8 Apr 2024).
• Taxonomic Change Tracking in Biology: Enhancing reasoner scalability for large, multi-level taxonomies, and refining expert-articulation protocols to efficiently resolve ambiguity and ensure alignment across phylogenetic revisions (Franz et al., 2014).
• Policy Adoption and Lifecycle Modeling: Formalizing mechanisms for policy-makers to leverage taxonomy networks in staging economic diversification, and extending modeling approaches for disposition metrics and adjacent possible activation prediction (Zaccaria et al., 2014).

This systematic body of research underlines the centrality of lifecycle alignment to robust, actionable taxonomies—enabling dynamic tracking, precise organization, and stage-appropriate intervention or evaluation across diverse scientific and technological domains.