Automated Product Taxonomy Discovery and Governance

Modern enterprises operate increasingly complex product portfolios that are distributed across fragmented digital environments including digital asset management systems, collaborative repositories, marketing archives, ecommerce catalogs, and internal documentation platforms. Over time, these systems accumulate inconsistent naming conventions, duplicate assets, ambiguous variant structures, and ad hoc organizational schemes that obscure the underlying product system. The resulting fragmentation imposes substantial operational costs, slows marketing and product operations, and prevents organizations from establishing the structured product knowledge foundation required for automation, analytics, and artificial intelligence applications.

This paper examines the theoretical and practical foundations of automated product taxonomy discovery and governance, proposing an AI-driven platform architecture capable of reconstructing structured product knowledge from unstructured enterprise data. The proposed approach integrates methods from knowledge representation, natural language processing, entity extraction, graph theory, and automated data engineering to infer product hierarchies, variant structures, ecosystem relationships, and canonical naming grammars from heterogeneous sources such as file systems, digital asset repositories, and product documentation.

The platform introduces a continuous discovery architecture in which machine learning systems and large language models collaboratively infer and maintain a product knowledge graph that serves as the canonical representation of an organization’s product portfolio. Beyond reconstructing taxonomies, the system operationalizes product structure by normalizing naming conventions, reorganizing asset repositories, governing schema evolution, and enabling product-aware analytics and AI reasoning.

By transforming fragmented enterprise data into structured ontological systems, automated product taxonomy platforms represent a critical infrastructural layer for organizations seeking to operationalize product knowledge at scale. This work situates such systems within the broader context of enterprise knowledge management, data architecture, and emerging AI-native organizational infrastructures.

1. Introduction

Product structure lies at the core of nearly every commercial organization. Whether in sportswear, consumer electronics, pharmaceuticals, or industrial manufacturing, companies manage portfolios of products composed of hierarchical relationships, variant dimensions, compatibility dependencies, and marketing representations. Yet despite the centrality of product knowledge to organizational functioning, many enterprises lack a coherent representation of their product system.

Consider a global sportswear company such as Nike. Across marketing campaigns, ecommerce platforms, retail catalogs, and internal product documentation, thousands of assets reference product models such as Air Max, Pegasus, or Vaporfly. These references appear in many forms: filenames, campaign materials, spreadsheets, packaging artwork, and internal documents. Over time, these representations diverge. A single product may appear under multiple naming variations, assets may be stored in inconsistent folder hierarchies, and variant structures such as colorways or seasonal releases may be inconsistently represented across systems.

As a result, product information becomes fragmented across disparate digital environments. Digital asset management systems store marketing imagery and campaign materials; collaboration platforms contain presentations and internal documents; ecommerce databases maintain incomplete SKU registries; and spreadsheets track operational product lists. Each system captures a partial view of the product portfolio.

This fragmentation produces significant operational inefficiencies. Marketing teams struggle to locate assets associated with particular products. Data analysts encounter inconsistent identifiers across datasets. Ecommerce teams duplicate product definitions across platforms. Product managers lack visibility into variant proliferation and ecosystem relationships. Most critically, artificial intelligence systems—which increasingly rely on structured knowledge graphs—lack a canonical representation of the organization’s product ontology.

Historically, organizations have attempted to address this problem through product information management (PIM) systems or manual taxonomy governance initiatives. However, these approaches assume that product structures can be manually defined and remain stable over time. In practice, product portfolios evolve continuously, and enterprise data sources rarely conform to rigid schemas.

Recent advances in machine learning and large language models provide an alternative paradigm. Instead of requiring structured inputs, AI systems can infer structure from messy, real-world data. By analyzing patterns in filenames, metadata, textual descriptions, and asset relationships, machine learning models can reconstruct the latent product system embedded within enterprise data.

This paper explores the design of an AI-native product taxonomy platform capable of automatically discovering and maintaining structured product knowledge within complex enterprise environments.

2. Product Knowledge and Taxonomy Theory

Taxonomies have long served as a foundational concept within information science and knowledge organization. Traditionally, taxonomy refers to the hierarchical classification of entities based on shared characteristics. Within enterprise contexts, product taxonomies provide the structural framework that organizes product portfolios into categories, families, models, and variants.

However, contemporary product ecosystems extend far beyond simple hierarchical classification. Modern product systems often involve multiple relational dimensions, including:

• hierarchical relationships (category → product family → model)

• variant relationships (model → colorway, size, material)

• compatibility relationships (device → accessory)

• bundle relationships (products marketed or sold together)

• representational relationships (digital assets depicting products)

Consequently, product knowledge is more accurately represented as an ontology—a structured representation of entities, attributes, and relationships—rather than as a purely hierarchical taxonomy.

Traditional enterprise systems attempt to model such ontologies through relational databases or manually curated metadata structures. However, these approaches struggle to capture the emergent and evolving nature of product systems within large organizations.

From a theoretical perspective, the challenge parallels problems studied in knowledge graph construction and entity extraction within natural language processing. In both cases, structured representations must be inferred from unstructured or semi-structured data sources.

3. The Problem of Enterprise Product Data Fragmentation

In practice, most product-related data within organizations exists in unstructured or semi-structured form. Typical sources include:

• filenames within asset repositories

• folder hierarchies in file systems

• metadata tags in digital asset management systems

• marketing copy and packaging descriptions

• spreadsheets listing SKUs or variants

• campaign documentation referencing products

These sources contain numerous signals about product structure, but they rarely follow standardized conventions. For example, the same product might appear in multiple forms across systems:

AirMax270_TripleWhite
Nike_AM270_White
Air_Max_270_White
AM270_triplewhite_packshot

Similarly, variant naming may appear inconsistently across assets:

Triple White
White Triple
White/White
TripleWhite

Such inconsistencies arise because product data is generated by multiple teams operating within different workflows. Marketing teams may name assets according to campaign needs, while product teams reference internal model numbers and regional offices introduce localized naming conventions.

Over time, enterprise repositories accumulate thousands of files and documents referencing products in inconsistent ways. The underlying product system becomes implicit rather than explicit.

The central challenge is therefore not merely to manage product data but to discover the product system embedded within organizational data.

4. Automated Product Taxonomy Discovery

An automated taxonomy discovery platform begins by ingesting enterprise repositories and extracting structured signals from unstructured sources.

The discovery process typically involves several computational stages.

4.1 Repository Crawling

The system scans enterprise repositories to generate an inventory of assets, documents, and metadata. This stage extracts attributes such as filenames, folder paths, file types, timestamps, and associated metadata tags.

4.2 Tokenization and Vocabulary Analysis

Filenames and textual metadata are decomposed into constituent tokens. For example:

AirMax270_TripleWhite_packshot_v3.png

may be tokenized into:

airmax
270
triplewhite
packshot

Aggregating tokens across large repositories reveals vocabulary patterns and term frequencies that form the basis for entity detection.

4.3 Product Entity Detection

Statistical co-occurrence analysis identifies tokens that frequently appear together, suggesting product entities. Repeated occurrences of token pairs such as “AirMax” and “270” across many assets strongly indicate a product model.

Machine learning models and language models further refine these detections by evaluating semantic context.

4.4 Variant Detection

Clusters of tokens associated with a common product line reveal variant structures. For example, tokens representing colorways or materials may appear in association with a specific model, indicating variant dimensions.

4.5 Asset Type Classification

Many tokens correspond not to product entities but to asset descriptors such as “packshot,” “lifestyle,” or “render.” Distinguishing between product tokens and asset tokens is essential for accurate taxonomy construction.