Overview

When the same entity (e.g., your Organization) is described on multiple pages with different names, addresses, or properties, it confuses search engines and AI agents. This checker finds those inconsistencies.

How It Works

1. Identifies entities that appear across multiple pages (same @type + matching identifiers)
2. Compares property values across occurrences
3. Flags conflicts:
– Different names for the same entity
– Conflicting addresses or contact info
– Inconsistent descriptions
4. Ranks conflicts by severity and frequency

Tier Availability

| Tier | Available |
|——|———–|
| Enterprise | Yes |

Related Features

– NAP Consistency Checker: Specialized for Name/Address/Phone
– Entity Coverage Map: Find where entities appear
– AI Entity Graph Analyzer: Impact on AI interpretation

Mini-Tutorial

Finding and Resolving Entity Conflicts

1. Run the consistency check across all validated pages:

   GET /validgraph/v1/entity-consistency?site_id=abc123
   

2. Review the conflict report showing:
– Entity name and type
– Pages where inconsistencies occur
– Specific property differences
– Severity level (high/medium/low)

3. Audit conflicting values:
– Page A: "name": "Acme Corp"
– Page B: "name": "Acme Corporation"
– These represent the same entity but with different names

4. Choose the canonical version for your organization (e.g., “Acme Corp”)

5. Update all pages to use the canonical values:

   {
     "@context": "https://schema.org",
     "@type": "Organization",
     "@id": "https://example.com/org",
     "name": "Acme Corp",
     "telephone": "+1-555-0123",
     "address": {
       "@type": "PostalAddress",
       "streetAddress": "123 Main St",
       "addressLocality": "Springfield",
       "addressRegion": "IL",
       "postalCode": "62701"
     }
   }
   

6. Validate to confirm all instances match.

Technical Details

Consistency Checking Algorithm

1. Entity Deduplication:
– Groups entities by matching @id values
– Falls back to normalized name matching (lowercased, whitespace normalized)
– Uses similar_text() function for fuzzy matching with >95% similarity threshold

2. Property Comparison:
– Compares all properties across occurrences: name, description, telephone, url, logo
– Detects direct conflicts (different values for same property)
– Detects missing values (property on some pages but not others)

3. Severity Calculation:
– High Severity (<60% similarity): Fundamental mismatch in entity description
– Medium Severity (60-80% similarity): Partial inconsistency (e.g., abbreviated name variant)
– Low Severity (>80% similarity): Minor variation (whitespace, punctuation)

REST API Example

Request:

GET /validgraph/v1/entity-consistency

Response:

{
  "conflicts": [
    {
      "entity": {
        "@type": "Organization",
        "canonical_id": "org-acme",
        "pages_count": 5
      },
      "conflict_details": [
        {
          "property": "name",
          "severity": "high",
          "occurrences": [
            {
              "page": "https://example.com/",
              "value": "Acme Corp"
            },
            {
              "page": "https://example.com/about",
              "value": "ACME CORPORATION"
            }
          ],
          "similarity": 0.58
        }
      ]
    }
  ],
  "total_conflicts": 3,
  "avg_similarity": 0.71
}

References

– Schema.org Organization Schema
– Knowledge Graph Consistency Best Practices
– Entity Canonicalization Standards