Entity Resolution
By Seme Research Team · Updated May 22, 2026
Definition
Entity Resolution is the process of determining whether different records or data points refer to the same real-world entity. In identity investigation, entity resolution links profiles across platforms — matching a Twitter handle to a LinkedIn profile, connecting an email address to a real name, or associating a phone number with multiple online accounts. This is one of the most challenging aspects of identity investigation because people use different names, handles, and identifiers across platforms. Modern entity resolution uses probabilistic matching algorithms that consider multiple signals: exact matches (same email, phone), fuzzy matches (similar names, partial matches), behavioral signals (writing style, activity patterns), and contextual signals (shared connections, overlapping timelines).
How It Works
Entity resolution uses a multi-signal matching pipeline. Signal 1 — Identifier Matching: exact matches on email addresses, phone numbers, and profile photos (using perceptual hashing). Signal 2 — Name Similarity: fuzzy string matching using Levenshtein distance, Jaro-Winkler similarity, and phonetic algorithms (Soundex, Metaphone) to handle spelling variations. Signal 3 — Behavioral Matching: comparing writing style (vocabulary, sentence structure), posting patterns (time of day, frequency), and content interests. Signal 4 — Network Overlap: checking if two profiles share connections, follow similar accounts, or belong to the same communities. Signal 5 — Contextual Matching: comparing biographical details (location, employer, education) across profiles. All signals are combined in a weighted scoring model to produce a match probability.
Example
Resolving that "@johndoe" on Twitter, "john.doe@company.com" in a press release, "John Doe, PhD" in an academic paper, and "jdoe82" on GitHub all refer to the same person. The system identifies: exact email match between Twitter bio and press release (Signal 1), name similarity 0.92 between Twitter handle and academic paper author (Signal 2), matching writing style between GitHub comments and Twitter posts (Signal 3), 15 mutual connections between LinkedIn and Twitter profiles (Signal 4), and consistent location and employer data across all profiles (Signal 5). Combined match probability: 97.3%.
Applications
- •Building unified identity profiles from fragmented online presence
- •Anti-fraud detection linking multiple fraudulent accounts
- •KYC compliance connecting customer records across systems
- •Investigative journalism connecting anonymous sources to real identities
Key Statistics
| Metric | Value | Source |
|---|---|---|
| Matching Signals | 5 categories | Seme methodology |
| Match Confidence Threshold | ≥85% | Seme configuration |
| Average Aliases Found | 3-10 per subject | Seme analytics |
| Cross-Platform Accuracy | 94%+ | Internal benchmarks |