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Configure AX ontology governance research topic #4
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| # PaperOps Runbook: AX Ontology Governance Topic | ||
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| ## Research topic | ||
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| **온톨로지 기반 AX 운영체계와 생성형 AI 거버넌스/보안/의사결정 자동화 연구** | ||
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| Working English topic: | ||
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| **Ontology-based AX operating model and generative AI governance, security, and decision automation** | ||
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| ## Goal | ||
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| Use PaperOps to collect, triage, and evidence-govern papers for a master's-level AI/Big Data engineering thesis. The output should support a thesis that treats ontology and knowledge graphs not as general philosophy, but as an engineering mechanism for enterprise AX operations, LLM governance, security control, traceability, and automated decision workflows. | ||
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| ## Success criteria | ||
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| 1. Collect papers across ontology/KG, GraphRAG, LLM governance, AI security, policy-as-code, decision automation, MLOps/LLMOps, and enterprise AI operations. | ||
| 2. Screen papers into `important`, `to_read`, `candidate`, and `screened` using the topic profile. | ||
| 3. Produce digest, brief, gap report, paper cards, and evidence candidate outputs. | ||
| 4. Keep all evidence in human-review mode. Do not mark `verified=true` automatically. | ||
| 5. Produce a defensible related-work base for an AI/Big Data engineering thesis. | ||
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| ## Recommended first run | ||
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| ```bash | ||
| git clone https://github.com/SakJaeLim/paperops.git | ||
| cd paperops | ||
| git checkout topic/ax-ontology-governance | ||
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| python -m venv .venv | ||
| # Windows | ||
| .venv\Scripts\activate | ||
| # macOS/Linux | ||
| # source .venv/bin/activate | ||
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| pip install -r requirements.txt | ||
| python scripts/paperops.py init | ||
| python scripts/paperops.py collect --limit 30 | ||
| python scripts/paperops.py score | ||
| python scripts/paperops.py screen --limit 120 | ||
| python scripts/paperops.py digest --top 30 | ||
| python scripts/paperops.py gap | ||
| python scripts/paperops.py brief | ||
| python scripts/paperops.py status | ||
| ``` | ||
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| ## If PDFs are needed | ||
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| ```bash | ||
| python scripts/paperops.py download-pdfs --limit 15 | ||
| ``` | ||
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| Optional GROBID parsing: | ||
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| ```bash | ||
| docker run -d -p 8070:8070 lfoppiano/grobid:0.8.0 | ||
| python scripts/paperops.py parse-grobid --paper-id <paper_id> --apply | ||
| python scripts/paperops.py extract-evidence-candidates --paper-id <paper_id> --apply | ||
| python scripts/paperops.py review-evidence-candidates --paper-id <paper_id> | ||
| python scripts/paperops.py promote-evidence --paper-id <paper_id> --apply | ||
| python scripts/paperops.py guard-no-auto-verified --promoted-only | ||
| python scripts/paperops.py smoke-test | ||
| ``` | ||
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| ## Thesis framing to use while reviewing papers | ||
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| ### Core research question | ||
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| How can an ontology-based operating model improve traceability, governance, security control, and decision automation in enterprise AX systems using generative AI? | ||
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| ### Sub-questions | ||
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| 1. What structural gap exists between conventional RAG/LLM applications and ontology/KG-based operational systems? | ||
| 2. Which ontology components are required for AX operations: object, relation, policy, state, action, evidence, user role, risk, control, decision, and audit log? | ||
| 3. How can SHACL/rule constraints and access-control metadata reduce unsafe or non-compliant LLM actions? | ||
| 4. How should GraphRAG and agentic workflows be evaluated beyond answer accuracy, using traceability, reproducibility, governance compliance, and decision quality? | ||
| 5. What reference architecture can integrate ontology/KG, RAG, LLM agents, governance controls, security guardrails, and human approval gates? | ||
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| ### Candidate contribution | ||
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| A reference architecture and evaluation framework for ontology-based AX operating systems that combines: | ||
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| - enterprise ontology / knowledge graph | ||
| - GraphRAG context construction | ||
| - policy and security constraints | ||
| - decision automation workflow | ||
| - human-in-the-loop approval | ||
| - audit and lineage tracking | ||
| - engineering evaluation metrics | ||
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| ## Review lens | ||
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| For each paper, extract only the following: | ||
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| 1. Problem definition | ||
| 2. Limitation of existing methods | ||
| 3. Engineering contribution | ||
| 4. Dataset/system setting | ||
| 5. Evaluation metrics | ||
| 6. Relevance to AX ontology governance | ||
| 7. Whether it supports architecture, governance, security, or decision automation | ||
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| ## Expected thesis chapter skeleton | ||
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| 1. Introduction | ||
| - AX systems need controlled, traceable, and governable generative AI. | ||
| 2. Related Work | ||
| - Ontology/KG, GraphRAG, AI governance, LLM security, decision automation, LLMOps. | ||
| 3. Problem Definition | ||
| - Conventional LLM/RAG systems lack operational semantics, policy binding, and auditability. | ||
| 4. Proposed Architecture | ||
| - Ontology-based AX operating model with KG, GraphRAG, agent workflow, policy/rule layer, security guardrails, and approval gates. | ||
| 5. Implementation / Prototype | ||
| - Domain ontology, graph schema, retrieval pipeline, decision workflow, logging, and validation rules. | ||
| 6. Evaluation | ||
| - Accuracy, groundedness, traceability, rule violation rate, decision reproducibility, latency, and human-review efficiency. | ||
| 7. Discussion | ||
| - Enterprise applicability, limitations, governance risks, and future work. | ||
| 8. Conclusion | ||
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| ## Immediate manual gates | ||
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| After the first run, inspect: | ||
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| - `reports/daily_digest/` | ||
| - `reports/survey_reports/` | ||
| - `data/metadata/papers.sqlite` | ||
| - `matrices/evidence_matrix.csv` | ||
| - `logs/ACTIVITY_LOG.md` | ||
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| Do not promote evidence until the paper has been manually checked. | ||
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In this AX-specific runbook, these steps are presented as producing the gap report and thesis brief for the new governance topic, but
scripts/paperops.pystill hard-codes bothcmd_gapandcmd_briefto the old "Ontology-enhanced GraphRAG ... 논문작성 지원" PaperOps/literature-writing thesis and meeting decisions about citation correctness/workflow efficiency, ignoringconfig/topic_profile.yaml. Users following the recommended sequence will therefore get reports framed around the previous topic rather than AX governance/security/decision automation; update those generators to read the profile or remove these steps from the AX runbook.Useful? React with 👍 / 👎.