our-claude-skills/custom-skills/28-seo-knowledge-graph/desktop/SKILL.md

name, description

name	description
seo-knowledge-graph	Knowledge Graph and entity SEO analysis. Triggers: knowledge panel, entity SEO, knowledge graph, PAA, FAQ schema, Wikipedia, Wikidata, brand entity, 지식 그래프, 엔티티 SEO, 지식 패널, 브랜드 엔티티, 위키데이터.

Knowledge Graph & Entity SEO

Analyze brand entity presence in Google Knowledge Graph, Knowledge Panels, People Also Ask (PAA), and FAQ rich results. Check entity attribute completeness, Wikipedia/Wikidata presence, and Korean equivalents (Naver knowledge iN, Naver encyclopedia).

Capabilities

Knowledge Graph Analysis

• Knowledge Panel detection and attribute extraction
• Entity attribute completeness scoring (name, description, logo, type, social profiles, website, founded, CEO)
• Wikipedia article presence check
• Wikidata entity presence check (QID lookup)
• Naver encyclopedia (네이버 백과사전) presence
• Naver knowledge iN (지식iN) presence

Entity SEO Audit

• People Also Ask (PAA) monitoring for brand-related queries
• FAQ schema presence tracking (FAQPage schema -> SERP appearance)
• Entity markup audit (Organization, Person, LocalBusiness schema on website)
• Social profile linking validation (sameAs in schema)
• Brand SERP analysis (what appears when you search the brand name)
• Entity consistency across web properties

Workflow

Knowledge Graph Analysis

1. Use WebSearch to search for the entity name on Google
2. Analyze search results for Knowledge Panel indicators
3. Use WebFetch to check Wikipedia article existence
4. Use WebFetch to check Wikidata QID existence
5. Use WebFetch to check Naver encyclopedia and 지식iN
6. Score entity attribute completeness
7. Save report to Notion SEO Audit Log

Entity SEO Audit

1. Use WebFetch to fetch the website and extract JSON-LD schemas
2. Validate Organization/Person/LocalBusiness schema completeness
3. Check sameAs links accessibility
4. Use WebSearch to search brand name and analyze SERP features
5. Monitor PAA questions for brand keywords
6. Use WebSearch for SERP feature detection
7. Save report to Notion SEO Audit Log

Data Source Selection

Entity / Knowledge Graph work spans KG API lookups, on-page schema audit, third-party presence checks (Wikipedia, Wikidata, Naver), and brand SERP analysis. Different backends own different slices.

Backend	Best for	Notes
OurSEO (CLI + MCP)	Default for KG lookups, entity audit, schema generation + fix	CLI: our research kg lookup, our research kg resolve, our audit entity, our audit sameas, our build kg-schema, our fix entity-schema. MCP: mcp__ourseo__search_knowledge_graph. Only backend with an integrated KG + entity-fix path.
WebSearch / WebFetch	Knowledge Panel detection, PAA monitoring, Wikipedia / Wikidata / Naver encyclopedia presence	KG Panel detection via direct Google search is heuristic but unavoidable — no MCP exposes Knowledge Panel structure directly.
Ahrefs MCP (mcp__ahrefs__*)	SERP-level entity queries — PAA, brand SERP, FAQ schema appearance	serp-overview, gsc-keywords (first-party brand query data), site-audit-issues for missing schema.
Semrush MCP (mcp__semrush__*)	Brand SERP overview, organic positions for entity-related queries	No dedicated KG endpoints — supplementary only.

How to pick

1. User named a backend explicitly → use it.
2. User preference memory — read feedback_seo_tool_preferences.md; honor the task-type default.
3. Task is KG lookup / entity resolution / sameAs validation / schema fix → use OurSEO (CLI primary, MCP for Desktop). No alternative covers this end-to-end.
4. Task is Wikipedia / Wikidata / Naver encyclopedia presence → WebSearch + WebFetch (no MCP backend covers third-party encyclopedias).
5. Task is brand SERP analysis → Ahrefs serp-overview or Semrush overview_research, paired with WebSearch for Knowledge Panel detection.
6. Default: OurSEO our research kg + WebSearch for third-party presence.
7. Still ambiguous + non-trivial → ask once via AskUserQuestion.

Backend call patterns

OurSEO CLI (default — KG lookup + entity audit + fix):

our research kg lookup "<entity>" --language ko
our research kg resolve "<entity>" --domain <site> --language ko
our audit entity https://<site> --entity "<entity>" --language ko
our audit sameas https://<site> --brand "<brand>"
our build kg-schema --type Organization --name "<entity>" --url https://<site> --auto-sameas
our fix entity-schema --site https://<site> --type Organization --entity-name "<entity>" --auto-sameas --cms ghost --deploy

OurSEO MCP (Claude Desktop):

mcp__ourseo__search_knowledge_graph(query="<entity>", language="ko")

Third-party presence (WebSearch / WebFetch):

WebSearch: "<entity>" site:wikipedia.org
WebFetch: https://www.wikidata.org/wiki/Special:Search?search=<entity>
WebSearch: "<entity>" site:terms.naver.com           # Naver encyclopedia
WebSearch: "<entity>" site:kin.naver.com              # Naver 지식iN

Brand SERP analysis (Ahrefs / Semrush):

mcp__ahrefs__serp-overview(keyword="<brand>", country="us")
mcp__ahrefs__gsc-keywords(project_id="<id>")          # First-party brand query data
mcp__semrush__overview_research(query="<brand>", database="us")

Always record the chosen data source(s) in the report Overview so future audits can compare like-for-like.

Notion Output

All reports must be saved to the OurDigital SEO Audit Log database.

Field	Value
Database ID	2c8581e5-8a1e-8035-880b-e38cefc2f3ef
Category	Knowledge Graph & Entity SEO
Audit ID	KG-YYYYMMDD-NNN

Report content should be written in Korean (한국어), keeping technical English terms as-is.

Output Format

{
  "entity_name": "OurDigital",
  "knowledge_panel": {
    "present": false,
    "attributes": {}
  },
  "entity_presence": {
    "wikipedia": false,
    "wikidata": false,
    "wikidata_qid": null,
    "naver_encyclopedia": false,
    "naver_knowledge_in": false,
    "google_knowledge_panel": false
  },
  "entity_schema": {
    "organization_count": 2,
    "person_count": 1,
    "same_as_links": ["https://linkedin.com/...", "https://facebook.com/..."],
    "same_as_count": 2,
    "issues": [
      "Duplicate Organization schemas with inconsistent names",
      "Placeholder image in Organization schema",
      "Only 2 sameAs links (recommend 6+)"
    ]
  },
  "paa_questions": [],
  "faq_schema_present": false,
  "entity_completeness_score": 12,
  "recommendations": [
    "Create Wikidata entity for brand recognition",
    "Add 4-6 more sameAs social profile links",
    "Replace placeholder image with actual brand logo",
    "Consolidate duplicate Organization schemas",
    "Add FAQPage schema to relevant pages"
  ],
  "audit_id": "KG-20250115-001",
  "timestamp": "2025-01-15T14:30:00"
}

Limitations

• Google Knowledge Panel detection via search results is not guaranteed (personalization, location-based)
• Direct Google scraping may be blocked (403/429); prefer WebSearch tool
• Wikipedia/Wikidata creation requires meeting notability guidelines
• PAA questions vary by location and device
• Entity completeness scoring is heuristic-based

Reference Scripts

Located in code/scripts/:

• knowledge_graph_analyzer.py — Knowledge Panel and entity presence analysis
• entity_auditor.py — Entity SEO signals and PAA/FAQ audit
• base_client.py — Shared async client utilities