arsalan-anwari/zaggr-data

Dutch Law Enforcement Entity Resolution Benchmark

A collection of synthetic benchmark datasets for entity resolution and record linkage research, modelled on real Dutch and EU/EEA law enforcement data schemas.

All data is 100% synthetic. No records correspond to real persons, vehicles, phone numbers, transactions, or criminal histories. Every name, date, plate number, IBAN, IMSI, and identifier is procedurally generated. The datasets are freely usable, shareable, and publishable without privacy or legal concerns.

Datasets are generated from real-world schemas (KvK Handelsregister, Basisregistratie Personen, SIS II Article 26/36 specification, Dutch ANPR kentekencamera feed, etc.) combined with authentic Dutch demographic distributions from CBS (Centraal Bureau voor de Statistiek) statistics. Error patterns — name variants, OCR confusion, romanization differences, estimated dates of birth — are injected to replicate the specific challenges found in Dutch and cross-Schengen identity data.

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Why this dataset exists

Standard entity resolution benchmarks (DBLP-ACM, Febrl, Cora) do not reflect the challenges of Dutch law enforcement data:

• Dutch name complexity — tussenvoegsel prefixes (van, de, van der, van den, van 't) are frequently omitted, abbreviated, or formatted inconsistently across registrations
• Multicultural name variation — Dutch-Moroccan and Dutch-Turkish communities produce names with authentic spelling variants (Mohammed / Mohamed / Muhammad, dropped Turkish diacritics like Çelik → Celik)
• Cross-Schengen identity — SIS II entries are registered by different member states in different scripts; the same person may appear as Benabdallah Fatima in one state and Fatima Benabdallah in another, with Arabic-script aliases alongside Latin ones
• ANPR OCR errors — Dutch highway cameras produce predictable single-character plate confusions (0<->O, 1<->I, 8<->B, 5<->S, 2<->Z) that require fuzzy matching rather than exact lookup
• Estimated dates of birth — asylum and immigration records frequently use YYYY-01-01 (January 1st fallback) when the exact birth date is unknown, creating year-boundary matching challenges

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Datasets

1. KvK Director Records

Company director registrations from the Dutch Chamber of Commerce (Kamer van Koophandel). The same person often directs multiple companies, with each registration entered independently — producing name variants, formatting differences, and address drift over time.

Files: phase-01/kvk/kvk_director_flat.csv, phase-01/kvk/ground_truth_pairs.csv

Schema:

Field	Type	Description
kvkNummer	string	Chamber of Commerce registration number (8 digits)
rechtsvorm	categorical	Legal form (Eenmanszaak, BV, VOF, …)
handelsnaam	string	Company trading name
voornamen	string	Given names
tussenvoegsel	string	Dutch name infix (van, de, van der, …)
achternaam	string	Surname
geboortedatum	date	Date of birth (ISO 8601)
geboorteplaats	string	Place of birth
woonplaats	string	City of residence
postcode	string	Dutch postcode (DDDDLL)
straatnaam	string	Street name
huisnummer	string	House number
sbiCode	string	SBI sector code
aantalWerkzamePersonen	integer	Number of employees

Ground truth format: kvk_a, kvk_b, is_match, match_type

Scale: ~10,000 records, ~3,600 true pairs

Injected error patterns:

• Tussenvoegsel variants: van der / v/d / vd / omitted entirely
• Abbreviated given names: Johannes → J. or J.A.
• Case and whitespace inconsistencies
• Address drift across multiple company registrations

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2. SIS II Wanted and Missing Persons

Person records modelled on the Schengen Information System II (SIS II) Article 26 (wanted persons) and Article 36 (missing persons) schemas. The key challenge is cross-Schengen alias pairs: the same individual entered by different member states using different name order conventions, romanizations, or scripts.

Files: phase-01/sis/sis_persons.csv, phase-01/sis/ground_truth_alias_pairs.csv

Schema:

Field	Type	Description
sis_id	string	SIS II alert identifier (8 hex chars)
categorie	categorical	Alert category (wanted_person, missing_person)
voornamen	string	Given names
achternaam	string	Surname
alias_namen	string	Pipe-delimited list of aliases (alias1|alias2|alias3)
geboortedatum	date	Date of birth; YYYY-01-01 for estimated DOBs
geboorteplaats	string	Place of birth
geboorteland	string	Country of birth
nationaliteit	string	Nationality (may be dual: Nederland/Marokko)
geslacht	categorical	Sex (M, V)
document_type	categorical	Document type (Paspoort, Identiteitskaart, …)
document_nummer	string	Document number
uitgevend_land	string	ISO 3166-1 alpha-2 issuing country
lengte	integer	Height in cm
haarkleur	categorical	Hair colour
oogkleur	categorical	Eye colour
aanmakende_staat	string	SIS II member state that created the alert

Ground truth format: sis_id_a, sis_id_b, is_match, match_type

Match types in ground truth: alias_romanization_variant, name_transposition, estimated_dob_variant, cross_script_alias

Scale: ~8,984 records, ~3,984 true pairs

Injected error patterns:

• Name transposition: Benabdallah Fatima <-> Fatima Benabdallah (given name / surname order varies by country convention)
• Romanization variants: Mohammed / Mohamed / Muhammed; Youssef / Yousef / Yusuf
• Estimated date of birth: ~30% of records use YYYY-01-01 (January 1st fallback)
• Non-Latin script aliases: Arabic-script aliases alongside Latin primary names (e.g. فاطمة بنعبدالله)
• Document number prefix variation: same serial number with different country-code prefixes

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3. ANPR Vehicle Passages

Highway camera passage events modelled on the Dutch ANPR (Automatische NummerPlaat Herkenning) kentekencamera feed operated by Dienst Verkeerspolitie. The primary challenge is single-character OCR confusion: a camera may read CX-I80-W when the true plate is CX-180-W.

Files: phase-01/anpr/anpr_passages.csv, phase-01/anpr/ground_truth_vehicle_pairs.csv

Schema:

Field	Type	Description
passage_id	string	Passage identifier ({8hex}-{3hex})
tijdstip	datetime	Passage timestamp (ISO 8601 with time)
camera_id	string	Camera identifier (CAM-{road}-{seq}, e.g. CAM-A12-001)
weg	string	Highway designation (A1, A2, A4, …)
lat	float	Camera latitude (decimal degrees, WGS84)
lon	float	Camera longitude (decimal degrees, WGS84)
rijrichting	categorical	Direction of travel (N, Z, O, W)
kenteken	string	License plate as read by OCR (may contain errors)
kenteken_betrouwbaarheid	integer	OCR confidence score 0–100
merk_herkend	string	Vehicle make (if detected)
kleur_herkend	categorical	Vehicle colour (if detected)
snelheid_kmh	integer	Speed in km/h

Ground truth format: passage_id_a, kenteken_true, kenteken_ocr, is_match, match_type

Match types in ground truth: ocr_confusion (with specific confusion noted, e.g. 1_to_I)

Scale: ~50,000 records, ~2,490 true pairs

Injected OCR confusion pairs (single substitution per plate, bidirectional):

Digit	Confused as
0	O
1	I
8	B
5	S
2	Z

Dutch license plate formats used: DD-LLL-D, LL-DD-LL, DD-LL-DD, L-DDD-LL, LL-DDD-L (current RDW series). Coordinates are sampled from bounding boxes around real Dutch highway corridors (A1, A2, A4, A10, A12, A20, etc.).

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Future datasets (not yet committed)

The following datasets will be added in later phases. Schemas are stable.

Dataset	Schema based on	Key challenges
BRP population register	Basisregistratie Personen	Name variants, address moves, dual nationality
HKS criminal antecedents	Herkenningsdienstsysteem (NL Police)	Alias vs. registered name, BSN linkage
Interpol notices	Interpol I-24/7 notice schema	Cross-source romanization, partial DOB
CDR call detail records	Telecom CDR / ETSI LI standard	IMSI/MSISDN suffix, cell tower clustering
SIM subscriber registrations	Telecom subscriber database	SIM swap events, IMEI reuse
FIU transaction reports	FIU-NL GTR / FATF schema	Structuring patterns, money mule ring detection

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Demographic composition

Name distributions reflect Dutch population demographics (CBS 2023):

Name origin	Share
Dutch	~55%
Moroccan / Arabic	~20%
Turkish	~12%
Other (Surinamese, Antillean, Eastern European)	~13%

Arabic and Turkish names are generated with authentic spelling variants to replicate the romanization challenges found in real Dutch administrative data. SIS II records include a proportion of non-Latin script aliases.

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File formats

All committed files are UTF-8 encoded CSV with a header row. Ground truth files always include an is_match column (True / False) and a match_type column describing the specific error pattern.

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License

All data is fully synthetic and released under CC BY 4.0. No real personal data, criminal records, or vehicle information is included. Attribution: cite this dataset as Dutch Law Enforcement Entity Resolution Benchmark (synthetic), arsalan-anwari, 2025.