The application of GAI in thesaurus construction presents both opportunities and challenges. Prompt engineering has emerged as a key technique for optimizing AI-generated outputs, particularly in areas such as controlled vocabulary development, semantic relationship identification, and metadata standardization (Chow et al, 2024; Hannah et al, 2025). Recent discussions at the Networked Knowledge Organization Systems (NKOS) Consolidated Workshop 2024, themed “KOS in AI & AI in KOS”, underscored AI’s potential to revolutionize knowledge organization systems (Busch, 2024; Networked Knowledge Organization Systems, 2024).

At the 2024 Getty International Terminology Working Group (ITWG) meeting, multiple presenters explored the application of generative AI in cultural heritage vocabularies. These included experiments involving Iconclass and iconographic metadata (Brandhorst, 2024), subject tagging for the Metropolitan Museum collection (Choi, 2024), and multilingual metadata enrichment (Zeng, 2024). In this context, the J. Paul Getty Research Institute (GRI), in collaboration with international partners, has spearheaded multilingual development of the Art and Architecture Thesaurus (AAT). The AAT-Taiwan project was the only initiative to apply generative AI directly to AAT’s editorial structures and bilingual translation workflows. This study builds on that preliminary work, first introduced at the 2024 ITWG meeting (Chen, 2024), by systematically evaluating prompt design strategies, AI-generated output quality, and human-AI collaboration in thesaurus construction.

This study examines a collaborative initiative between the Academia Sinica Center for Digital Cultures (ASCDC) and the Getty Research Institute (GRI) aimed at translating AAT into Chinese and incorporating culturally specific Chinese and Taiwanese concepts to AAT’s English edition. Researchers have proposed four alignment models for controlled vocabulary structures in Chinese and English (Chen et al, 2016) and developed systematic methodologies for thesaurus editing (Chen, 2021).

Building on this foundation, the study explores how GAI can enhance thesaurus construction, particularly in:

$\mathrm{◆}$ Accelerating vocabulary development,

$\mathrm{◆}$ Improving editorial efficiency, and

$\mathrm{◆}$ Enhancing collaboration workflows.

Using ChatGPT-4o (version gpt-4o-2024-08-06, hereafter AI; available at https://openai.com/chatgpt), the research investigates AI’s ability to assist in thesaurus contributions, specifically in constructing vocabulary relationships and drafting scope notes.

Among thesaurus relationships, equivalence relationships are fundamental to retrieval accuracy and consistency. Given the semantic and cultural complexities of equivalence relationships, they serve as ideal entry point for evaluating AI’s role in thesaurus development. This study addresses the following research questions:

RQ1: To what extent can AI identify and recommend equivalence relationships among vocabularies during thesaurus construction?

RQ2: How can Generative AI support and enhance scope notes drafting for localized vocabularies in thesauri?

Additionally, the study explores human-AI collaboration in vocabulary construction, assessing AI’s ability to improve efficiency while maintaining semantic integrity. Special emphasis is placed on prompt design and optimization, employing an iterative refinement process to enhance AI-generated outputs while minimizing manual intervention.

The findings offer empirical insights and a methodological framework for integrating AI into thesaurus development, with broader implications for digital humanities and knowledge organization systems.

Generative AI (GAI), powered by Large Language Models (LLMs), has revolutionized natural language processing, particularly in multilingual translation, semantic reasoning, and domain-specific knowledge organization. Brown et al. (2020) demonstrated that models like GPT-3 excel in few-shot learning, enabling task-specific adaptation without extensive training. These advancements provide a foundation for integrating GAI into Knowledge Organization Systems (KOS), particularly in thesaurus construction, where synonym identification and scope note drafting demand semantic precision and contextual awareness.

Existing research has explored LLM applications across diverse fields. Jauhiainen and Guerra (2024) examined LLM performance in semantic recognition and multilingual translation, emphasizing their capacity to handle linguistic and cultural diversity. Similarly, Ziems et al. (2024) assessed LLMs in computational social sciences, combining automated metrics with expert evaluations to measure relevance, fluency, and coherence in generated content. This study extends these studies insights by applying to KOS tasks, addressing challenges of multilingual and culturally nuanced vocabulary development.

LLMs are widely recognized for their text classification and generation capabilities, making them valuable in social sciences and digital humanities (Ziems et al, 2024). Their ability to analyze textual structures and uncover semantic patterns has facilitated research in history, linguistics, literature, psychology and sociology.

This study focuses on two core thesaurus construction tasks:

(1) Vocabulary relationship analysis

(2) Scope note drafting

These tasks align closely with LLM competencies in text structuring and content summarization. Prior studies have demonstrated how models like ChatGPT assist in preliminary diagnoses from radiology reports, underscoring their value in domains requiring semantic precision (Hu et al, 2024; Jauhiainen and Guerra, 2024; Ziems et al, 2024). LLMs have also been effective in semantic recognition, translation, linguistic analysis, and language prediction, supporting both vocabulary development and scope note generation (Chang et al, 2024; Jauhiainen and Guerra, 2024).

Despite these advancements, LLMs still face challenges in deep semantic analysis and cultural interpretation, reinforcing the need for human expertise in AI-assisted knowledge organization. Research suggests that human-AI collaboration enhances efficiency and reliability in text analysis, yet optimizing this synergy remains an open research question (Ziems et al, 2024). This study explores strategies for leveraging LLMs in thesaurus construction, focusing on vocabulary relationship analysis and scope note drafting.

Prompt engineering has emerged as a key methodology for optimizing the LLM performance in semantic reasoning, text generation, and multilingual applications. By bridging the gap between user intent and model output, structured prompts enable LLMs to generate precise and contextually relevant results, particularly in tasks requiring cultural and linguistic sensitivity (Brown et al, 2020; Marvin et al, 2024).

Recent study highlights the critical role of prompt design in improving accuracy and contextual alignment of AI-generated outputs. Sahoo et al. (2024) categorize prompt engineering techniques into zero-shot, few-shot, and chain-of-thought approaches, demonstrating how structured prompts—incorporating task objectives, semantic frameworks, and contextual examples—significantly enhance task performance. These insights provide a theoretical foundation for this study’s exploration of synonym identification and scope note drafting in thesaurus construction.

Building on this framework, the study integrates:

$\mathrm{◆}$ Domain-specific instructions,

$\mathrm{◆}$ Contextual examples, and

$\mathrm{◆}$ Structured output formatting

Building on this framework, the study integrates domain-specific instructions, contextual examples, and structured output formatting to enable LLMs to effectively handle nuanced cultural and semantic relationships. For example, chain-of-thought techniques applied in scope note drafting enhance the model’s ability to generate accurate and comprehensive content aligned with professional standards.

This iterative refinement process advocated by Sahoo et al. (2024) ensures continuous prompt optimization based on performance metrics and expert feedback, addressing the complexities of thesaurus construction.

Further, Meskó (2023) emphasize essential prompt components, including:

$\mathrm{◆}$ Task roles,

$\mathrm{◆}$ Instructions,

$\mathrm{◆}$ Examples, and

$\mathrm{◆}$ Output formats.

Ekin (2023) reinforces the importance of domain-specific knowledge, cultural nuances, and task customization for optimizing LLM-generated outputs in specialized fields. These insights directly inform this study’s approach to leveraging GAI for culturally nuanced thesaurus development.

Assessing the quality of LLM-generated content involves two primary methodologies:

(1) Automated evaluation metrics, such as BLEU, ROUGE-L, and METEOR, which measure form-based matching but often fail to capture semantic depth and contextual relevance.

(2) Human evaluation, which provide qualitative insights into practical usability and linguistic quality (Chang et al, 2024; Schuff et al, 2023; Ziems et al, 2024).

Chang et al. (2024) propose a systematic framework for evaluating LLMs, focusing on what to evaluate, where to evaluate, and how to evaluate. Building on the 3H principles (Helpfulness, Honesty, Harmlessness), they define six key evaluation dimensions: accuracy, relevance, fluency, transparency, safety, and human alignment. Ziems et al. (2024) further apply these evaluation criteria to assess 13 LLMs across 25 representative tasks in computational social science, highlighting the importance of metrics such as relevance, fluency, faithfulness, and coherence in evaluating generative tasks. The AAT Editorial Guidelines (Getty Research Institute, 2024) establish key criteria for scope note drafting, emphasizing clarity, simplicity, brevity, objectivity, and logical topic order. These guidelines serve as a foundational reference in thesaurus construction, ensuring consistency and usability in knowledge organization systems.

These guidelines inform the evaluation framework adopted in this research, integrates correctness, completeness, conciseness, fluency, and coherence as core evaluation metrics (see Table 1).

While LLMs demonstrate proficiency in semantic analysis, their performance remains constrained by limitations in historical and cultural comprehension. For example, Ziems et al. (2024) highlight LLMs’ difficulty in capturing domain-specific subtleties without extensive contextual training. Similarly, Hu et al. (2024) emphasize challenges in synthesizing accurate content across diverse and complex datasets.

In thesaurus construction, these limitations are particularly evident in tasks requiring cultural judgments and semantic refinement. AI-generated outputs often require expert review to ensure accuracy and appropriateness. This study seeks to optimize human-AI collaboration, leveraging LLMs’ computational efficiency while maintaining the interpretive depth provided by human expertise.

This study examines 21 musical instrument concept terms used in the Confucius Ceremony at the Taipei Confucius Temple. Selection was guided by the principles of “literary warrant” and “cultural warrant” (Barité, 2018). Literary warrant ensures that terms are supported by historical and bibliographic evidence, while cultural warrant emphasizes their enduring significance within confucian ceremonial traditions. As an annual ritual with a 2500-year history, the Confucius Ceremony represents a cornerstone of Confucian cultural heritage, integrating tangible and intangible elements, such as musical instruments, ritual vessels, architectural spaces, and ceremonial practices. Consequently, the selected instruments represent both a specific classification within Confucian rituals and broader cultural and epistemological significance in musicology and heritage studies.

These instruments exemplify the traditional Chinese “Pa Yin” (Eight Sounds) classification system, reflecting their cultural specificity (Thrasher, 2000). Additionally, their alignment with the Hornbostel-Sachs Classification (H-S Classification) (Lee, 2020) and Western music classifications illustrates their cross-cultural relevance in global musicological frameworks (see Table 2). This dual validation—both cultural and scholarly—reinforces their role in multilingual and comparative knowledge organization. Despite their historical grounding, these terms have undergone semantic shifts and polysemic variations due to cultural transmission and evolving contexts. Some instruments are known by multiple names across different texts and traditions, complicating semantic analysis and vocabulary standardization, particularly in generative AI (GAI) applications. This study provides an empirical case to assess how GAI can address semantic variability and polysemy while upholding literary and cultural warrants principles.

This study employs an iterative experimental design, focusing on synonym selection and scope note composition through human and Generative AI (GAI) collaboration. A structured 12-step framework guides the research process (see Fig. 1).

The methodology consists of four stages:

(1) Target Identification & Reference Collection—Identifying research targets and compiling reference materials.

(2) Standard Development—Establishing reference standards for consistency.

(3) Gold Sample Testing & Prompt Optimization—Refining AI-generated content through benchmarking and iterative adjustments.

(4) General Testing & Expert Evaluation—Conducting large-scale validation and expert review to ensure accuracy.

The editorial team curates the reference materials, drafts content, and designs prompts, while domain experts review and refine scope notes to enhance precision. AI-assisted content generation supplements human expertise, optimizing efficiency while maintaining interpretive depth. Fig. 1 outlines the research framework, detailing the collaborative human-AI workflow across the four stages.

Steps 6–11 constitute the core of the AI experimental framework, integrating structured prompt engineering with comprehensive evaluation metrics. Table 3 provides an overview of the AI research design.

The identification of equivalence relationships, particularly synonyms, is a fundamental aspect of thesaurus construction, ensuring semantic interoperability across linguistic and cultural contexts (ISO, 2011). In this study, AI’s role in synonym identification was assessed to determine its effectiveness in processing semantic nuances, a task traditionally dependent on human expertise.

Scope notes play a crucial role in defining and contextualizing terms within thesauri, particularly when addressing localized vocabularies. They ensure semantic clarity, standardization, and cross-cultural consistency, making them highly beneficial for knowledge organization systems (KOS), particularly those requiring precise term definitions and contextual clarity. This study demonstrates that structured prompting strategies significantly enhance the quality of AI-generated scope notes, improving accuracy, depth, and coherence while aligning outputs with professional editorial standards.

This section examines the comparative performance of AI and editorial teams in synonym identification, emphasizing AI’s complementary role in supporting human editors and its potential in semantic expansion.

To assess the accuracy and relevance of AI-generated results, this study established three comparative benchmarks:

(1) AI- Identified Synonyms: Synonyms generated by AI based on its internal semantic models.

(2) Core Synonyms Confirmed by the Editorial Team: Synonyms validated against existing literature and editorial expertise.

(3) Synonyms Verified Through Domain Experts Review: Synonyms confirmed by domain experts as definitive references.

Findings indicate that AI can reliably identify core synonyms while also uncovering additional latent synonyms not initially recognized through manual editorial processes. For instance, AI identified Du (Precursor to the Paiban) as a synonym for Paiban (Clappers) revealing its capacity to detect broad semantic relationships. These results suggest that AI’s internal semantic modelling can uncover meaningful connections that merit further expert validation, providing new avenues for refining editorial workflows and advancing semantic expansion research.

Based on expert evaluations, the AI-identified synonyms were categorized into four distinct types (See Table 11). Symbols used in Table 11 include: $\surd$ for confirmed terms, ○ for AI-suggested terms, – for rejected candidates, and blank for items not explicitly evaluated. For example, the two Di (笛) entries were included by AI as related terms but were not confirmed by expert reviewers, and thus marked as “–”.

(1) All AI-Identified Synonyms: Encompasses all potential synonyms generated by AI, reflecting its broad semantic interpretation capabilities.

(2) Editorially Confirmed Synonyms: Synonyms validated by the editorial team based on established standards and literature.

(3) Expert-Verified Synonyms: Synonyms confirmed through domain expert review, serving as a definitive benchmark.

(4) Latent Synonyms Identified by AI (Validated by Experts): Synonyms initially flagged by AI but uncertain for editorial teams, later confirmed as valid through expert review.

The comparative analysis in Table 11 provides critical insights into AI’s role in synonym identification, highlighting both its capabilities and limitations.

(1) Semantic Breadth

AI demonstrated a robust ability to identify semantically related terms, in some cases detecting a broader range of potential synonyms than those traditionally recognized by human editors. This underscores AI’s potential to extend beyond conventional lexicons, offering an expansive semantic interpretation. However, AI’ limitations in contextual comprehension, sensitivity to historical-cultural nuances, and precision in managing subtle semantic distinctions frequently lead to misinterpretations or semantic ambiguity.

(2) Uncovering Potential Overlooked Synonyms

AI successfully identified potential synonyms that were later validated by domain experts, as indicated in the “AI-Identified Potential Synonyms” column in Table 11. This ability to surface latent semantic relationships highlights AI’s utility in enriching synonym databases, particularly in specialized knowledge domain where manual identification may be constrained by cognitive or editorial biases.

(3) Human-AI Collaborative Model for Synonym Expansion

AI’s capacity to uncover latent semantic relationships that may not be immediately evident to human editors presents a significant advantage. However, expert validation remains indispensable for ensuring linguistic precision, historical integrity, and cultural relevance. This synergy between AI’s computational efficiency and human expertise offers a promising framework for refining synonym identification. By integrating automated discovery with expert oversight, this human-AI collaborative model balances scalability and accuracy, effectively addressing the limitations of both AI and manual editorial processes when used in isolation.

While AI-generated outputs provide valuable contributions to editorial workflows, expert review remains essential to refining results, resolving contextual inconsistencies, and ensuring semantic rigor. This collaborative approach not only enhances the reliability of synonym identification but also broadens the scope of AI applications in Knowledge organization systems. Future research should further explore how AI-driven synonym discovery can be systematically integrated into editorial processes to enhance efficiency while maintaining the highest standards of scholarly accuracy.

While AI demonstrates significant potential for semantic discovery, particularly in synonym identification, it faces notable challenges in handling historical contexts, domain-specific terminology, and nuanced semantic distinctions. This section categorizes the primary challenges (summarized in Table 12) and proposes corresponding mitigation strategies to improve AI’s performance in synonym identification.

(A) Conceptual Confusion

AI occasionally misidentifies distinct concepts as synonymous, conflating terms that serve different functional or contextual roles.

• Example: Jin Gu (large barrel drum) and Jian Gu (central-pillar drum) were erroneously classified as synonyms despite their differing functions and structural designs.

• Cause: These misinterpretations often stem from conflicting historical documentation or misalignment between primary data sources and expert perspectives.

• Mitigation Strategy: expert review is essential to verify and correct misidentified synonyms, ensuring conceptual clarity and precision.

(B) Ambiguities in Semantic Hierarchies

AI often fails to distinguish between general categories, coordinate terms, and precise object references, leading to overgeneralized semantic interpretations.

• Example: Sheng (苼, Mouth Organ) was inaccurately generalized to include Chao Sheng (巢笙, Multi-Reed Sheng) and He Sheng (和苼, Single-Reed Sheng), despite their distinct forms, structures and acoustic properties.

• Cause: AI lacks robust hierarchical frameworks to differentiate related but non-equivalent terms.

• Mitigation Strategy: Integrating structured semantic hierarchies into AI prompt design, coupled with expert validation, can refine AI’s handling of taxonomic distinctions.

(C) Lack of Support for Rare or Obscure Terms

AI encounters difficulties in identifying rare or under-documented terms, leading to either omissions or incorrect synonym pairings.

• Example: AI paired of Paiban (Clappers) and Chalaqi (different linguistic origin), an obscure foreign term with insufficient historical documentation to confirm semantic similarity.

• Cause: Limited corpus diversity results in erroneous classifications for poorly documented terms.

• Mitigation Strategy: AI prompts should explicitly exclude poorly documented terms to minimize false positives, and specialized corpora should be integrated to support rare terminology identification.

(D) Misinterpretation of Nomenclature or Functional Components

AI occasionally misreads terminology referring to specific attributes (e.g., pitch, components) as full synonyms of the instruments.

• Example: Bo Zhong (Hollow Ritual Bell) was mistakenly associated with with Nan Lü (南呂鐘, a specific pitch position), a term referring only to a specific pitch in the Chinese Twelve-Lü Tuning System, not to a bell itself.

• Cause: AI lacks the ability to differentiate whole objects from their specific attributes, leading to misinterpretations in specialized domains.

• Mitigation Strategy: Linking AI models to domain-specific databases and ontologies can improve the precision of terminology recognition.

While AI demonstrates strong capabilities in synonym discovery, including identifying latent relationships and generating broad synonym suggestions, it struggles with context sensitivity, historical accuracy, and semantic granularity. To address these limitations, a hybrid human-AI model is essential:

(1) AI can efficiently detect potential synonym relationships at scale.

(2) Human experts must validate and refine AI-generated outputs to ensure linguistic, historical, and cultural precision.

(3) Integrating structured ontologies or other formalized knowledge organization systems (KOS)—such as faceted thesauri like the AAT—along with domain-specific corpora and expert-driven quality control mechanisms, will enhance AI’s ability to process domain-specific terminology accurately.

This synergistic approach—leveraging AI’s computational efficiency while maintaining expert oversight—offers a viable framework for thesaurus construction, ensuring both scalability and precision in knowledge organization.

This study focuses specifically on the methodological design and evaluation of generative AI for thesaurus-related tasks, particularly synonym identification and scope note composition. Broader social, ethical, or environmental implications of generative AI—such as bias, carbon footprint, or data privacy—are recognized as important but fall outside the scope of this research. These concerns merit future interdisciplinary exploration, particularly in relation to long-term infrastructure integration in cultural heritage and knowledge organization systems.

In addition to methodological and contextual limitations, the issue of cost-effectiveness also warrants further investigation. While generative AI tools may offer notable productivity gains, their effective use often requires human oversight, iterative prompt refinement, and editorial validation—all of which entail labor, time, and potential funding investment. For smaller projects with limited resources, the benefits of AI integration may not always outweigh these overheads. However, as prompting techniques and AI models become more user-friendly and domain-aligned, the balance between human effort and AI augmentation may shift toward greater efficiency. Evaluating cost-effectiveness across different project scales remains an important area for future applied research.

(1) AI’s Synonym Identification Capabilities

AI can rapidly and accurately identify core synonyms and uncover latent semantic relationships beyond traditional manual editing. However, expert validation remains indispensable for ensuring historical accuracy, contextual appropriateness, and terminological precision, reinforcing the importance of a human-AI collaborative approach.

(2) AI’s Contribution to Scope Note Drafting

With structured prompting, AI can generate professionally structured scope notes aligned with domain-specific frameworks. The AAT-Guided approach excels in professional database-oriented applications, while the Pustejovsky-Guided method enhances interpretive and educational use cases.

(3) Challenges in Contextual and Cultural Understanding

AI struggles with historical languages, cultural nuances, and complex semantic hierarchies, limiting its ability to fully capture domain-specific contexts. These challenges highlight the need for comprehensive datasets, expert intervention, and refined prompting techniques to enhance accuracy and contextual depth.

(4) Importance of Structured Prompt Design

AI performance hinges on structured prompts that define task roles, contextual background, clear objectives, and explicit instruction rules. A well-defined prompt structure significantly improves accuracy, completeness, and consistency in AI-generated outputs.

Building on these findings, the next section outlines key areas for future research to enhance AI’s applicability across diverse domains and knowledge systems.

Future research should focus on enhancing AI’s applicability in multilingual, culturally nuanced, and domain-specific thesauri development. Five key areas warrant further investigation:

(1) Expanding AI’s Applicability in Historical and Multilingual Contexts

Refining workflows strategies for vocabulary translation and localization is essential, particularly for culturally embedded knowledge systems, such as Confucian ceremonial terminology. AI-driven methods must balance linguistic precision and cultural fidelity, ensuring that historical meanings are preserved while facilitating cross-linguistic knowledge representation.

Future research should also test AI’s adaptability in other historical lexicons, including:

$\mathrm{◆}$ Buddhist doctrinal vocabularies, where transliterations and semantic shifts affect synonym relationships.

$\mathrm{◆}$ Medieval European manuscripts, where terminological evolution and variant spellings pose challenges in knowledge organization.

$\mathrm{◆}$ Indigenous knowledge systems, where oral traditions and regional taxonomies demand new methodologies for AI-assisted thesaurus construction.

These case studies will help determine AI’s effectiveness in handling historically and contextually grounded synonym identification.

(2) Enhancing Human-AI Collaboration in Knowledge Organization

Effective human-AI collaboration remains crucial in ensuring semantic precision, contextual appropriateness, and cross-cultural validity. Future research should focus on:

$\mathrm{◆}$ Hybrid methodologies integrating ontology-based reasoning frameworks, domain-specific knowledge graphs, and expert validation mechanisms to improve AI’s reliability.

$\mathrm{◆}$ Interactive AI-assisted thesaurus editing platforms to streamline editorial workflows and enhance user engagement in thesaurus curation.

Investigating these methodologies will optimize AI’s role as an assistive tool, rather than a replacement for expert-driven lexicographic practices.

(3) Investigating AI’s Domain Adaptability and Generalizability

While this study focuses on Confucian ceremonial vocabulary, future research should assess whether AI-driven thesaurus construction models are transferable across disciplines. Comparative studies should explore AI’s performance in:

$\mathrm{◆}$ Historical-cultural thesauri, testing polysemy resolution and contextual inference across Buddhist, medieval, and indigenous lexicons.

$\mathrm{◆}$ Structured scientific thesauri, including medical subject headings (MeSH), environmental taxonomies, and technical vocabularies, where terminological precision and hierarchical structure are paramount. By conducting cross-domain evaluations, future studies can determine AI’s scalability and adaptability across diverse knowledge organization systems (KOS).

By conducting cross-domain evaluations, future studies can determine AI’s scalability, adaptability, and impact in diverse knowledge organization systems (KOS).

(4) Advancing Evaluation Metrics for AI-Generated Thesauri

Current evaluation frameworks rely heavily on expert review to validate AI-generated synonym identification and scope note generation. Future research should explore automated evaluation methodologies, such as machine learning-based consistency checks, knowledge graph embeddings, and explainable AI (XAI) techniques, to reduce reliance on manual assessments while maintaining scalability and interpretability.

(5) Comparative Analysis of Language Models in Thesaurus Construction

Investigating alternative AI models, such as Gemini, Claude, and Copilot, could provide insights into their effectiveness in synonym identification and scope note generation across varying linguistic and cultural settings. Future studies should conduct benchmarking experiments to assess which models best capture semantic nuance, hierarchical relationships, and domain-specific terminologies. Evaluate AI’s interpretability and explainability, ensuring greater alignment with thesaurus construction best practices. By addressing these research directions, AI-assisted thesaurus construction can evolve into a scalable, cross-disciplinary, and culturally inclusive approach to knowledge organization and multilingual lexicography.

This study focused on Confucian ceremonial instruments as a case study but did not explore the detailed mapping of Chinese-English terms within the AAT framework. This remains a promising avenue for future research, with the potential to optimize multilingual thesauri construction and further enhance AI’s role in knowledge organization.

While this study demonstrates the potential of Generative AI in thesaurus development, several ethical concerns must be addressed to ensure responsible AI integration in knowledge organization.

(1) Bias in AI-Generated Lexical Relationships

AI’s reliance on pre-trained language models introduces inherent biases. If historical terminology is underrepresented in training data, AI may favor modern language conventions, leading to semantic distortions. This raises ethical concerns regarding the fair representation of historically and culturally significant terminologies.

(2) Cultural Sensitivity and Knowledge Equity

The application of AI in Confucian ritual terminologies necessitates a culturally aware and historically precise approach. AI’s tendency to generalize across different temporal and linguistic contexts could lead to cultural appropriation or misrepresentation of ritual practices. Future work should explore incorporating ethically curated multilingual corpora to enhance cultural inclusivity.

(3) Transparency and Explainability in AI-Assisted Thesaurus Construction

In AI-assisted thesaurus development, transparency is essential to ensure expert validation and maintain scholarly rigor. Users and domain experts should be able to trace AI’s reasoning in synonym identification and scope note generation, allowing for systematic evaluation and refinement. Implementing explainable AI (XAI) mechanisms or structured logging of AI-generated suggestions would enhance expert oversight, facilitate interpretability, and support the iterative human-AI collaboration process (Ali et al, 2023; Kale et al, 2023).

(4) Human Oversight and Interpretive Authority

AI should complement, not replace, expert-driven lexicographic practices. This study reinforces the importance of human-AI collaboration as a safeguard against misinformation and semantic drift.