From 0772434f0f6589ac6e0fb6005de2d392f66dff5d Mon Sep 17 00:00:00 2001 From: SEOK JAE LIM Date: Sat, 20 Jun 2026 05:50:14 +0900 Subject: [PATCH 1/2] Configure AX ontology governance research topic --- config/topic_profile.yaml | 114 ++++++++++++++++++++++++++------------ 1 file changed, 79 insertions(+), 35 deletions(-) diff --git a/config/topic_profile.yaml b/config/topic_profile.yaml index a1a982f..c9fb875 100644 --- a/config/topic_profile.yaml +++ b/config/topic_profile.yaml @@ -1,9 +1,9 @@ -profile_name: ontology_research_agent -main_topic: "Ontology, knowledge graph, semantic web, and AI-assisted literature review / academic writing workflows" +profile_name: ax_ontology_governance_research +main_topic: "Ontology-based AX operating model and generative AI governance, security, and decision automation" language: notes: ko - manuscript: en + manuscript: ko summaries: ko keywords: @@ -13,37 +13,68 @@ keywords: - knowledge graph - knowledge graphs - semantic web - - linked data - RDF - OWL + - SHACL - SPARQL - - SKOS - - taxonomy - - metadata schema + - property graph + - Neo4j + - operational ontology + - enterprise ontology method: - - literature review automation - - systematic literature review - - research assistant - - academic writing - - scientific writing - - paper recommendation - - citation recommendation - - evidence extraction + - GraphRAG - retrieval augmented generation - - research agent - - AI scientist + - agentic workflow + - AI agent + - multi-agent system + - decision automation + - decision support system + - workflow automation + - policy-as-code + - rule-based reasoning + - explainable AI + - traceability + - lineage + - auditability + - human-in-the-loop + governance_security: + - generative AI governance + - AI governance + - LLM governance + - AI risk management + - model risk management + - AI security + - LLM security + - prompt injection + - data leakage + - access control + - attribute-based access control + - guardrails + - compliance automation + - trustworthy AI + - responsible AI application: - - knowledge organization - - scholarly knowledge graph - - research workflow - - document intelligence - - scientific discovery + - AX transformation + - digital transformation + - enterprise AI platform + - AI operating model + - AI operations + - MLOps + - LLMOps + - AIOps + - enterprise architecture + - information systems planning + - public sector AI + - financial AI governance exclude: - - SEO article + - purely philosophical ontology + - biomedical ontology only + - cryptocurrency ontology + - SEO knowledge graph - marketing knowledge graph - - purely clinical ontology without reusable methodology - non-academic blog post - - cryptocurrency ontology + - AI art prompt + - pure chatbot tutorial without governance or evaluation target_outputs: - daily_digest @@ -53,28 +84,41 @@ target_outputs: - gap_matrix - related_work - manuscript_outline + - governance_framework + - security_control_matrix + - decision_automation_architecture preferred_venues: - Semantic Web Journal - Journal of Web Semantics - ISWC - ESWC - - WWW - The Web Conference + - WWW - ACL - EMNLP - NAACL - SIGIR - CHI - - JCDL + - IEEE Access + - ACM Computing Surveys + - Information Systems Frontiers + - Decision Support Systems + - Expert Systems with Applications + - Computers & Security + - IEEE Security & Privacy - arXiv query_groups: - - name: ontology_core - query: "ontology knowledge graph semantic web RDF OWL SPARQL" - - name: literature_review_automation - query: "automated literature review research assistant academic writing citation evidence extraction" - - name: scholarly_kg - query: "scholarly knowledge graph scientific literature ontology" - - name: research_agent - query: "AI research agent paper writing scientific discovery literature review" + - name: operational_ontology_ax + query: "operational ontology enterprise knowledge graph AI operating model digital transformation decision automation" + - name: ontology_graphrag_governance + query: "ontology knowledge graph GraphRAG generative AI governance traceability auditability" + - name: llm_governance_security + query: "LLM governance generative AI security prompt injection data leakage guardrails access control" + - name: decision_automation_policy_reasoning + query: "decision automation policy as code rule based reasoning ontology explainable AI human in the loop" + - name: enterprise_ai_operations + query: "enterprise AI platform LLMOps MLOps AI governance risk management compliance automation" + - name: public_financial_ai_governance + query: "public sector AI governance financial AI governance model risk management trustworthy AI" \ No newline at end of file From 5970f0418c1fd4f223b37c363fcfaeff6e78f205 Mon Sep 17 00:00:00 2001 From: SEOK JAE LIM Date: Sat, 20 Jun 2026 05:51:39 +0900 Subject: [PATCH 2/2] Add AX ontology governance runbook --- docs/RUN_AX_ONTOLOGY_GOVERNANCE.md | 131 +++++++++++++++++++++++++++++ 1 file changed, 131 insertions(+) create mode 100644 docs/RUN_AX_ONTOLOGY_GOVERNANCE.md diff --git a/docs/RUN_AX_ONTOLOGY_GOVERNANCE.md b/docs/RUN_AX_ONTOLOGY_GOVERNANCE.md new file mode 100644 index 0000000..0875335 --- /dev/null +++ b/docs/RUN_AX_ONTOLOGY_GOVERNANCE.md @@ -0,0 +1,131 @@ +# PaperOps Runbook: AX Ontology Governance Topic + +## Research topic + +**온톨로지 기반 AX 운영체계와 생성형 AI 거버넌스/보안/의사결정 자동화 연구** + +Working English topic: + +**Ontology-based AX operating model and generative AI governance, security, and decision automation** + +## Goal + +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. + +## Success criteria + +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. + +## Recommended first run + +```bash +git clone https://github.com/SakJaeLim/paperops.git +cd paperops +git checkout topic/ax-ontology-governance + +python -m venv .venv +# Windows +.venv\Scripts\activate +# macOS/Linux +# source .venv/bin/activate + +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 +``` + +## If PDFs are needed + +```bash +python scripts/paperops.py download-pdfs --limit 15 +``` + +Optional GROBID parsing: + +```bash +docker run -d -p 8070:8070 lfoppiano/grobid:0.8.0 +python scripts/paperops.py parse-grobid --paper-id --apply +python scripts/paperops.py extract-evidence-candidates --paper-id --apply +python scripts/paperops.py review-evidence-candidates --paper-id +python scripts/paperops.py promote-evidence --paper-id --apply +python scripts/paperops.py guard-no-auto-verified --promoted-only +python scripts/paperops.py smoke-test +``` + +## Thesis framing to use while reviewing papers + +### Core research question + +How can an ontology-based operating model improve traceability, governance, security control, and decision automation in enterprise AX systems using generative AI? + +### Sub-questions + +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? + +### Candidate contribution + +A reference architecture and evaluation framework for ontology-based AX operating systems that combines: + +- 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 + +## Review lens + +For each paper, extract only the following: + +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 + +## Expected thesis chapter skeleton + +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 + +## Immediate manual gates + +After the first run, inspect: + +- `reports/daily_digest/` +- `reports/survey_reports/` +- `data/metadata/papers.sqlite` +- `matrices/evidence_matrix.csv` +- `logs/ACTIVITY_LOG.md` + +Do not promote evidence until the paper has been manually checked.