# Schema-Miner: A Human-Machine Collaborative Scientific Schema Mining Framework Based on Large Language Models

> Schema-Miner is an innovative open-source framework that leverages large language models (LLMs) and continuous human feedback to automate and enhance scientific schema mining tasks. This tool uses a three-stage iterative process to extract and organize scientific attributes from unstructured text, anchors schema elements to formal ontologies (such as QUDT), and provides a new method for structured knowledge representation in scientific research.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-08-11T14:46:51.000Z
- 最近活动: 2026-08-11T14:54:19.496Z
- 热度: 150.9
- 关键词: 大语言模型, 知识抽取, 模式挖掘, 人机协作, 科学文献, 本体工程, 自然语言处理, 知识图谱
- 页面链接: https://www.zingnex.cn/en/forum/thread/schema-miner
- Canonical: https://www.zingnex.cn/forum/thread/schema-miner
- Markdown 来源: floors_fallback

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## Schema-Miner: An Open-Source Human-in-the-Loop Framework for Scientific Schema Mining with LLMs

Schema-Miner is an innovative open-source framework developed by the sciknoworg organization. It combines large language models (LLMs) with continuous human feedback to automate and enhance scientific schema mining tasks. The framework uses a three-stage iterative process to extract and organize scientific attributes from unstructured text, and anchors schema elements to formal ontologies like QUDT, providing a new method for structured knowledge representation in scientific research.

Key resources:
- GitHub repo: https://github.com/sciknoworg/schema-miner
- PyPI package: https://pypi.org/project/schema-miner/
- Documentation: https://schema-miner.readthedocs.io/
- Academic citation: ESWC Proceedings (https://link.springer.com/chapter/10.1007/978-3-031-94578-6_14)

## Background & Motivation

In scientific research, most knowledge exists as unstructured text in papers, reports, and technical documents. Traditional knowledge extraction methods require extensive manual annotation and domain expert involvement, which are inefficient and hard to scale.

While LLMs enable automation, pure automated approaches struggle to capture domain nuances and professional constraints. Thus, the human-in-the-loop paradigm was adopted—combining LLM automation with expert judgment to balance efficiency and accuracy in knowledge representation.

## Framework Overview & Core Features

Schema-Miner's core innovation is integrating LLMs with human feedback via an iterative process. Key features:
1. Three-stage progressive schema discovery: From domain specs → curated literature → large corpus
2. Multi-LLM provider support: OpenAI, HuggingFace, Ollama, GWDG/SAIA, OpenRouter
3. Human-in-loop feedback: Expert input at key nodes to ensure schema quality
4. Ontology grounding: Mapping schema elements to formal ontologies like QUDT
5. Flexible deployment: Local GPU, remote API, or hybrid mode

## Technical Workflow & Ontology Anchoring

The framework uses a modular three-stage architecture:
1. **Initial Schema Mining**: Uses domain specs (PDF/text) and LLM to generate initial JSON schema (configurable via `PROCESS_NAME` and `PROCESS_DESCRIPTION`)
2. **Preliminary Refinement**: Combines curated papers with initial schema; expert feedback iteratively refines the schema
3. **Final Refinement**: Processes large uncurated corpus to auto-integrate new attributes

Schema-Miner Pro adds ontology anchoring: Agentic AI uses semantic similarity and heuristics to map schema elements to ontologies like QUDT, enhancing standardization and interoperability.

## Application Scenarios & Value

Schema-Miner applies to multiple scientific fields:
- **Materials science**: Extract experiment parameters to build knowledge graphs for new materials
- **Biomedicine**: Analyze clinical trials to support evidence-based medicine and drug repurposing
- **Environmental science**: Integrate climate/ecosystem knowledge for policy support
- **Chemical engineering**: Extract reaction conditions to optimize production

Value: Converts unstructured literature into structured, computable knowledge; accelerates discovery and cross-disciplinary integration.

## Limitations & Future Directions

**Current Limitations**: 
1. Dependent on LLM quality (small models may lack complex concept understanding)
2. Domain adaptation requires custom prompts and feedback mechanisms
3. High compute resources for local GPU deployment

**Future Directions**: 
- Multi-modal support for charts/formulas
- Domain-specific prompt libraries
- Enhanced visualization for schema editing
- Federated learning for sensitive data collaboration

## Installation & Quick Start

**Installation**: 
- PyPI: `pip install schema-miner`
- Source: `git clone https://github.com/sciknoworg/schema-miner.git && cd schema-miner && pip install -r requirements.txt`

**Configuration**: Copy `.env.example` to `.env` and set:
- LLM provider (OPENAI/SAIA/OLLAMA/HUGGINGFACE)
- Model name, API keys
- Process definitions (`PROCESS_NAME`, `PROCESS_DESCRIPTION`)
- Data paths

**Commands**: 
- Stage1: `schema-miner --stage 1`
- Stage2 with feedback: `schema-miner --stage 2 --schema results/stage1/schema.json --expert-feedback feedback.txt`
- Stage3: `schema-miner --stage3 --schema results/stage2/schema.json`
- Ontology grounding: `schema-miner --ontology-grounding agentic --schema results/stage3/schema.json`