taxonomy design

This paper addresses core challenges in applied ontologies—namely, referential ambiguity of terms, weak semantic holism, and poor cross-domain understandability and reusability. To resolve these issues, we propose the “ontology component,” a novel structural unit centered on domain terms and integrating description logic-based formal semantics with natural language annotations in a principled manner. Leveraging an assertion-driven approach to semantic formalization, our method enables operational definitions of meaning. Crucially, this design unifies formal logical representation and natural language interpretation for the first time, substantially enhancing term-level semantic robustness, clarity, and interdisciplinary accessibility. Moreover, ontology components natively support version evolution and modular reuse, thereby establishing both a theoretical foundation and a practical methodology for building reusable, evolvable semantic infrastructure.

Scientific process descriptions are often embedded in unstructured text, hindering reproducibility, comparison, and automation. To address this challenge, this work presents the first cross-disciplinary, expert-driven repository of structured scientific process schemas, encompassing 16 expert-annotated patterns across five domains. Through a human-in-the-loop workflow, candidate schemas generated by large language models were iteratively refined via domain expert feedback, yielding reusable fields such as inputs, outputs, steps, and parameters. The resulting schemas are formalized in both JSON Schema and SHACL formats and accompanied by an integrated toolchain. The project also releases a comprehensive dataset—including schemas, intermediate artifacts, review records, and analysis scripts—to support knowledge graph construction, semantic publishing, and cross-study comparison.

This study addresses the lack of structured, reproducible, and high-precision prompt engineering methodologies for Google Gemini 3 Pro Image, which leads to insufficient consistency and compliance in professionally generated images. To resolve this, we propose SCHEMA, a systematic prompting framework featuring a three-tier control system (BASE/MEDIO/AVANZATO), a modular tag architecture, and a compliance decision tree that enables fine-grained regulation of the image generation process. As the first domain-specific prompting framework tailored to this multimodal large language model, SCHEMA supports progressive controllability from 5% to 95% and includes strategies to mitigate model limitations with alternative pathways. Evaluated on 621 test cases using 850 API calls and 4,800 generated images, SCHEMA achieves 91% compliance on mandatory requirements and 94% on prohibited constraints, with over 95% first-generation compliance in information design tasks and significantly improved cross-domain image consistency in independent validation (n=40).

To address the performance limitations of schema matching in open-domain and cross-domain settings—where scarce supervision severely hampers model accuracy—this paper proposes a Generative Tags mechanism. It integrates rule-based features, BERT-style semantic embeddings, and structured tags generated by large language models (LLMs), enabling a lightweight hybrid encoding and classification framework. Our key contributions are threefold: (1) We introduce the first generative tagging paradigm for schema matching, substantially reducing reliance on manual annotations; (2) We construct and publicly release HDXSM—the first large-scale, human-curated benchmark specifically designed for humanitarian-domain schema matching; (3) Our method achieves state-of-the-art performance, improving F1 score by 11.84% and ROC AUC by 5.08% over prior approaches across multiple public datasets and HDXSM.

Addressing the challenge of multi-label automatic annotation for large-scale, hierarchical classification systems in software requirements engineering, this study proposes a sentence-level zero-shot classification paradigm to circumvent the high annotation costs associated with supervised training. We introduce the first industrial-scale requirements annotation benchmark comprising 769 taxonomy labels and systematically demonstrate a strong negative correlation between the number of taxonomy leaf nodes and classification recall. We further propose a zero-shot multi-label classification method leveraging SBERT sentence embeddings, achieving significant improvements in recall. Empirical evaluation reveals that hierarchical strategies yield no consistent performance gain across settings. Our work validates the effectiveness and feasibility of zero-shot learning for large-scale requirements classification, offering a scalable, low-human-effort automation solution for requirements tracing. (138 words)

This study addresses the challenge in attributed graph schema design of whether repeatedly occurring descriptive attributes should be embedded within nodes or externalized as reusable metadata. Building upon Fifth Normal Form (5NF), the authors propose a principled decision framework that systematically identifies metadata candidates based on semantic criteria rather than mere repetition frequency. The approach classifies attributes into characteristic nodes, embedded properties, or borderline cases using five key principles: cross-element occurrence frequency, conceptual independence, lossless externalizability, reuse potential, and governance relevance. Empirical validation through a library domain case study and an entity classification task demonstrates that repetition alone is insufficient for externalization decisions—semantic judgment is essential. The proposed method significantly enhances the accuracy, consistency, and reusability of metadata modeling in graph-based systems.

This work proposes an interactive ontology construction paradigm that bridges the gap between purely manual and fully automated approaches, which are often hindered by laborious processes or insufficient user control, respectively. By leveraging weighted self-organizing maps, the method enables progressive clustering of tabular data while integrating instance grouping with mechanisms for defining conceptual intensions. This approach empowers users to flexibly adjust both the number of clusters and their semantic interpretations, thereby preserving the efficiency of automation while significantly enhancing controllability. As a result, it facilitates interpretable clustering of entities and the generation of high-quality ontological classifications directly from tabular data.

This study addresses the lack of systematic guidance on contextualization strategies for large language model (LLM) agents operating in structured data environments, particularly concerning effectiveness and efficiency across multi-file, large-scale schemas. Using SQL generation as a proxy task, the work presents the first systematic evaluation of eleven models across four context formats—YAML, Markdown, JSON, and TOON—at schema scales ranging from 10 to 10,000 tables. The findings reveal that model capability tiers critically determine optimal context architecture: tailored strategies significantly improve performance, with state-of-the-art models gaining 2.7% accuracy under native file-based contexts, while open-source models average a 7.7% decline. Moreover, native file-based agents scale efficiently to ten-thousand-table schemas while maintaining high navigation accuracy.

This work addresses the limitations of traditional knowledge graph construction approaches, wherein structural decisions are hard-coded into rigid pipelines, resulting in tight coupling between schema and construction process and hindering support for ontology-level tasks. To overcome this, the authors propose an ontology-oriented construction framework featuring a novel intrinsic-relational routing mechanism. This mechanism dynamically assigns attributes to corresponding schema modules through iterative attribute classification, enabling a declarative and reusable decoupled design. The pipeline integrates rule-based cleaning, tool-augmented large language model–assisted annotation, and human review. Evaluated on Wikidata (January 2026), the resulting graph comprises 34 million nodes and 61.2 million edges, achieving 93.3% schema coverage and 98.0% module assignment accuracy, effectively supporting five ontology-level applications.

This study addresses the challenges of maintaining consistency across heterogeneous schema languages—such as JSON Schema, XSD, and SHACL—during multilingual data model evolution, where fragmented converters, variable quality, and information loss impede reliable interoperability. The work proposes a novel approach that models schema languages and black-box converters as nodes and directed edges in a graph, enabling composable and evaluable conversion path orchestration. By integrating graph-based search, quality-aware ranking (combining agent-assisted and human evaluation), and failure backtracking, the method supports automated, reproducible cross-language schema transformation. The resulting open-source toolchain, Schema Conversion Orchestrator, integrated into the MetaConfigurator platform, successfully produced valid outputs for 43 out of 60 real-world tasks and precisely identified missing ecosystem components in the remaining 17, thereby delineating the current boundaries of schema conversion capabilities.