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docs: fix conflict resolution implementation mismatches

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Sameer6305
2026-06-29 17:00:01 +05:30
parent 5c512a5011
commit 8d60f68fcd
1 changed files with 63 additions and 49 deletions
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docs/guides/conflict-resolution.md
+63 -49
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@@ -110,14 +110,14 @@ for r in results:
```text
Conflicts found: 1
Property : email
Values : ['alice@example.com', 'alice.smith@example.com']
Severity : low
Values : ['alice@example.com', 'alice.smith@example.com', 'alice.smith@example.com']
Severity : medium
[RESOLVED] cust-001_email_conflict
Resolved value : alice.smith@example.com
Strategy : voting
Confidence : 67%
Sources used : ['erp', 'ldap']
Sources used : ['crm', 'erp', 'ldap']
```
`detect_entity_conflicts()` scanned both `email` and `phone` automatically — you did not name them. Because `phone` is identical across all three records, no conflict was detected for it. The email disagreement resolves to `alice.smith@example.com` because two of three sources agree on that value.
@@ -168,7 +168,7 @@ cve_records = [
"cvss_score": 10.0,
"exploit_status": "unconfirmed",
"vector": "AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H",
"credibility_score": 0.98,
"metadata": {"timestamp": "2024-04-11T12:00:00Z"},
},
{
"id": "cve-2024-3400",
@@ -176,7 +176,7 @@ cve_records = [
"cvss_score": 9.1,
"exploit_status": "in_wild",
"vector": "AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"credibility_score": 0.91,
"metadata": {"timestamp": "2024-04-12T15:30:00Z"},
},
{
"id": "cve-2024-3400",
@@ -184,7 +184,7 @@ cve_records = [
"cvss_score": 9.5,
"exploit_status": "in_wild",
"vector": "AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H",
"credibility_score": 0.87,
"metadata": {"timestamp": "2024-04-12T12:00:00Z"},
},
]
@@ -217,7 +217,7 @@ Conflict: cve-2024-3400_cvss_score_conflict
Values : [10.0, 9.1, 9.5]
Severity : medium
Sources : ['nvd', 'commercial_feed', 'vendor_paloalto']
Action : Compare source documents and use most recent or authoritative source
Action : Multiple conflicting values detected. Manual review recommended.
```
Each `Conflict` captures the full picture: which entity, which property, every disagreeing value, and which source reported each. This is already enough to build a review queue — but the goal is to resolve these automatically according to rules you set.
@@ -244,8 +244,13 @@ This is cleaner than calling `set_resolution_rule()` in a loop over every entity
```python
resolver = ConflictResolver()
# Register source credibility scores so CREDIBILITY_WEIGHTED can use them
resolver.source_tracker.set_source_credibility("nvd", 0.98)
resolver.source_tracker.set_source_credibility("commercial_feed", 0.91)
resolver.source_tracker.set_source_credibility("vendor_paloalto", 0.87)
# For this CVE, NVD is the most authoritative source on scoring.
# CREDIBILITY_WEIGHTED will use the credibility_score field on each source record
# CREDIBILITY_WEIGHTED uses the registered source credibility
# to weight the vote — NVD at 0.98 will dominate over the commercial feed at 0.91.
resolver.set_resolution_rule(
"cve-2024-3400",
@@ -290,9 +295,9 @@ for r in results:
[RESOLVED] cve-2024-3400_cvss_score_conflict
Resolved value : 10.0
Strategy used : credibility_weighted
Confidence : 72%
Confidence : 36%
Sources used : ['nvd', 'commercial_feed', 'vendor_paloalto']
Notes : Resolved by credibility-weighted voting (weight: 0.98)
Notes : Resolved by credibility-weighted voting (weight: 0.49)
[RESOLVED] cve-2024-3400_exploit_status_conflict
Resolved value : in_wild
@@ -314,14 +319,11 @@ from semantica.conflicts import ConflictDetector, ConflictResolver, ResolutionSt
# Drug trial data: efficacy agreed, primary endpoint disputed
trial_records = [
{"id": "dapagliflozin", "source": "declare_timi58",
"primary_endpoint": "MACE", "hba1c_reduction_pct": 0.54,
"credibility_score": 0.92},
"primary_endpoint": "MACE", "hba1c_reduction_pct": 0.54},
{"id": "dapagliflozin", "source": "dapa_hf",
"primary_endpoint": "HF_hospitalization", "hba1c_reduction_pct": 0.48,
"credibility_score": 0.95},
"primary_endpoint": "HF_hospitalization", "hba1c_reduction_pct": 0.48},
{"id": "dapagliflozin", "source": "meta_analysis",
"primary_endpoint": "HbA1c_reduction", "hba1c_reduction_pct": 0.52,
"credibility_score": 0.88},
"primary_endpoint": "HbA1c_reduction", "hba1c_reduction_pct": 0.52},
]
detector = ConflictDetector()
@@ -330,6 +332,11 @@ endpoint_conflicts = detector.detect_value_conflicts(trial_records, "primary_en
resolver = ConflictResolver()
# Register source credibility scores
resolver.source_tracker.set_source_credibility("declare_timi58", 0.92)
resolver.source_tracker.set_source_credibility("dapa_hf", 0.95)
resolver.source_tracker.set_source_credibility("meta_analysis", 0.88)
# Efficacy: credibility-weighted across trials — the meta-analysis (0.88) and
# the two RCTs (0.92, 0.95) will produce a weighted resolution.
resolver.set_resolution_rule(
@@ -427,9 +434,9 @@ report = detector.get_conflict_report()
print(f"Total conflicts detected : {report['total_conflicts']}")
print(f"By type : {report['by_type']}")
print(f"By severity : {report['by_severity']}")
# Total conflicts detected : 2
# By type : {'value_conflict': 2}
# By severity : {'medium': 2}
# Total conflicts detected : 6
# By type : {'value_conflict': 6}
# By severity : {'medium': 6}
```
The report aggregates every conflict the detector has seen across its lifetime — useful for pipeline monitoring and for identifying which entity types or data sources generate the most disagreements.
@@ -471,11 +478,11 @@ from semantica.conflicts import ConflictDetector, ConflictResolver, ResolutionSt
actor_profiles = [
{"id": "apt29", "source": "mandiant", "nation_state": "Russia",
"first_seen": "2008", "credibility_score": 0.95},
"first_seen": "2008"},
{"id": "apt29", "source": "crowdstrike", "nation_state": "Russia",
"first_seen": "2009", "credibility_score": 0.92},
"first_seen": "2009"},
{"id": "apt29", "source": "oss_blog", "nation_state": "China", # wrong
"first_seen": "2015", "credibility_score": 0.30},
"first_seen": "2015"},
]
detector = ConflictDetector()
@@ -483,15 +490,19 @@ nation_conflicts = detector.detect_value_conflicts(actor_profiles, "nation_s
first_seen_conflicts = detector.detect_value_conflicts(actor_profiles, "first_seen")
resolver = ConflictResolver()
resolver.source_tracker.set_source_credibility("mandiant", 0.95)
resolver.source_tracker.set_source_credibility("crowdstrike", 0.92)
resolver.source_tracker.set_source_credibility("oss_blog", 0.30)
resolver.set_resolution_rule("apt29", "nation_state", ResolutionStrategy.CREDIBILITY_WEIGHTED)
resolver.set_resolution_rule("apt29", "first_seen", ResolutionStrategy.CREDIBILITY_WEIGHTED)
results = resolver.resolve_conflicts(nation_conflicts + first_seen_conflicts)
for r in results:
print(f"{r.conflict_id}: {r.resolved_value!r} [{r.confidence:.0%} confidence]")
# apt29_nation_state_conflict: 'Russia' [83% confidence]
# apt29_first_seen_conflict: '2008' [73% confidence]
# The blog's China attribution (weight 0.30) loses to Mandiant+CrowdStrike (0.95+0.92).
# apt29_nation_state_conflict: 'Russia' [86% confidence]
# apt29_first_seen_conflict: '2008' [44% confidence]
# The blog's China attribution (weight 0.15) loses to Mandiant+CrowdStrike (0.475+0.46).
history = resolver.get_resolution_history()
print(f"Audit log entries: {len(history)}")
@@ -510,14 +521,11 @@ from semantica.conflicts import ConflictDetector, ConflictResolver, ResolutionSt
cve_records = [
{"id": "cve-2024-3400", "source": "nvd",
"cvss_score": 10.0, "vector": "AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H",
"credibility_score": 0.98},
"cvss_score": 10.0, "vector": "AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H"},
{"id": "cve-2024-3400", "source": "mitre",
"cvss_score": 9.8, "vector": "AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"credibility_score": 0.96},
"cvss_score": 9.8, "vector": "AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H"},
{"id": "cve-2024-3400", "source": "paloalto",
"cvss_score": 9.5, "vector": "AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H",
"credibility_score": 0.90},
"cvss_score": 9.5, "vector": "AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H"},
]
detector = ConflictDetector()
@@ -525,6 +533,10 @@ score_conflicts = detector.detect_value_conflicts(cve_records, "cvss_score")
vector_conflicts = detector.detect_value_conflicts(cve_records, "vector")
resolver = ConflictResolver()
resolver.source_tracker.set_source_credibility("nvd", 0.98)
resolver.source_tracker.set_source_credibility("mitre", 0.96)
resolver.source_tracker.set_source_credibility("paloalto", 0.90)
resolver.set_resolution_rule(
"cve-2024-3400", "cvss_score", ResolutionStrategy.CREDIBILITY_WEIGHTED
)
@@ -537,8 +549,8 @@ for r in results:
if r.resolved:
print(f"Canonical {r.conflict_id.split('_')[2]}: {r.resolved_value} "
f"({r.confidence:.0%} confidence)")
# Canonical cvss_score: 10.0 (72% confidence) — NVD wins
# Canonical vector: AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H (54% confidence)
# Canonical cvss_score: 10.0 (35% confidence) — NVD wins
# Canonical vector: AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H (35% confidence)
```
</Tab>
@@ -554,14 +566,11 @@ from semantica.conflicts import ConflictDetector, ConflictResolver, ResolutionSt
drug_records = [
{"id": "dapagliflozin", "source": "declare_timi58",
"hba1c_reduction_pct": 0.54, "primary_endpoint": "MACE",
"credibility_score": 0.92},
"hba1c_reduction_pct": 0.54, "primary_endpoint": "MACE"},
{"id": "dapagliflozin", "source": "dapa_hf",
"hba1c_reduction_pct": 0.48, "primary_endpoint": "HF_hospitalization",
"credibility_score": 0.95},
"hba1c_reduction_pct": 0.48, "primary_endpoint": "HF_hospitalization"},
{"id": "dapagliflozin", "source": "meta_analysis",
"hba1c_reduction_pct": 0.52, "primary_endpoint": "HbA1c_reduction",
"credibility_score": 0.88},
"hba1c_reduction_pct": 0.52, "primary_endpoint": "HbA1c_reduction"},
]
detector = ConflictDetector()
@@ -569,6 +578,10 @@ efficacy_conflicts = detector.detect_value_conflicts(drug_records, "hba1c_reduct
endpoint_conflicts = detector.detect_value_conflicts(drug_records, "primary_endpoint")
resolver = ConflictResolver()
resolver.source_tracker.set_source_credibility("declare_timi58", 0.92)
resolver.source_tracker.set_source_credibility("dapa_hf", 0.95)
resolver.source_tracker.set_source_credibility("meta_analysis", 0.88)
resolver.set_resolution_rule(
"dapagliflozin", "hba1c_reduction_pct", ResolutionStrategy.CREDIBILITY_WEIGHTED
)
@@ -584,7 +597,7 @@ review = [r for r in results if not r.resolved]
print(f"Auto-resolved : {len(auto)}")
for r in auto:
print(f" {r.conflict_id}: {r.resolved_value} [{r.confidence:.0%}]")
# dapagliflozin_hba1c_reduction_pct_conflict: 0.48 [38%]
# dapagliflozin_hba1c_reduction_pct_conflict: 0.48 [35%]
print(f"Expert queue : {len(review)}")
for r in review:
@@ -605,14 +618,11 @@ from semantica.conflicts import ConflictDetector, ConflictResolver, ResolutionSt
client_records = [
{"id": "corp-acme-uk", "source": "crm",
"legal_name": "ACME UK Ltd", "sic_code": "7372",
"credibility_score": 0.75},
"legal_name": "ACME UK Ltd", "sic_code": "7372"},
{"id": "corp-acme-uk", "source": "lei_registry",
"legal_name": "ACME United Kingdom Limited", "sic_code": "7371",
"credibility_score": 0.99}, # LEI registry is authoritative
"legal_name": "ACME United Kingdom Limited", "sic_code": "7371"},
{"id": "corp-acme-uk", "source": "credit_bureau",
"legal_name": "ACME UK Ltd", "sic_code": "7372",
"credibility_score": 0.85},
"legal_name": "ACME UK Ltd", "sic_code": "7372"},
]
detector = ConflictDetector()
@@ -620,6 +630,10 @@ name_conflicts = detector.detect_value_conflicts(client_records, "legal_name")
sic_conflicts = detector.detect_value_conflicts(client_records, "sic_code")
resolver = ConflictResolver()
resolver.source_tracker.set_source_credibility("lei_registry", 0.99)
resolver.source_tracker.set_source_credibility("credit_bureau", 0.50)
resolver.source_tracker.set_source_credibility("crm", 0.40)
resolver.set_resolution_rule(
"corp-acme-uk", "legal_name", ResolutionStrategy.CREDIBILITY_WEIGHTED
)
@@ -629,10 +643,10 @@ resolver.set_resolution_rule(
results = resolver.resolve_conflicts(name_conflicts + sic_conflicts)
for r in results:
print(f"Canonical {r.conflict_id.split('_')[2]}: {r.resolved_value!r} "
print(f"Canonical {r.conflict_id.split('_')[1]}: {r.resolved_value!r} "
f"[{r.confidence:.0%}]")
# Canonical legal_name: 'ACME United Kingdom Limited' [53%] — LEI registry wins
# Canonical sic_code: '7371' [53%] — LEI registry wins
# Canonical legal_name: 'ACME United Kingdom Limited' [52%] — LEI registry wins
# Canonical sic_code: '7371' [52%] — LEI registry wins
# Aggregate conflict statistics for the compliance report
report = detector.get_conflict_report()