Update README.md

README.md

@@ -1,102 +1,240 @@
 ---
-license: mit
+language:
+  - en
+license: apache-2.0
 task_categories:
   - text-classification
   - feature-extraction
-language:
-  - en
 tags:
-  - entity-resolution
+  - entity-linking
   - named-entity-recognition
-  - company-data
-  - embeddings
+  - knowledge-base
+  - organizations
+  - people
   - sqlite
   - vector-search
+  - embeddings
 size_categories:
   - 1M<n<10M
+pretty_name: Entity References Database
+configs:
+  - config_name: full
+    description: Full database with complete source metadata
+  - config_name: lite
+    description: Core fields + embeddings only (recommended)
 ---
 
 # Entity References Database
 
-A pre-built SQLite database with vector embeddings for organization/entity resolution. Used by the [corp-extractor](https://pypi.org/project/corp-extractor/) library for fast entity qualification via embedding similarity search.
-
-## Overview
+A comprehensive entity database for organizations, people, roles, and locations with 768-dimensional embeddings for semantic matching. Built from authoritative sources (GLEIF, SEC, Companies House, Wikidata) for entity linking and named entity disambiguation.
+
+## Dataset Description
+
+- **Repository:** [Corp-o-Rate-Community/entity-references](https://huggingface.co/datasets/Corp-o-Rate-Community/entity-references)
+- **Paper:** N/A
+- **Point of Contact:** Corp-o-Rate-Community
+
+### Dataset Summary
+
+This dataset provides fast lookup and qualification of named entities using vector similarity search. It stores records from authoritative global sources with embeddings generated by `google/embeddinggemma-300m` (768 dimensions).
+
+**Key Features:**
+- **8M+ organization records** from GLEIF, SEC Edgar, Companies House, and Wikidata
+- **Notable people** including executives, politicians, athletes, artists, and more
+- **Roles and locations** with hierarchical relationships
+- **Vector embeddings** for semantic similarity search
+- **Canonical linking** across sources (same entity from multiple sources linked)
+
+### Supported Tasks
+
+- **Entity Linking**: Match extracted entity mentions to canonical database records
+- **Named Entity Disambiguation**: Distinguish between entities with similar names
+- **Knowledge Base Population**: Enrich extracted entities with identifiers and metadata
+
+### Languages
+
+English (en)
+
+## Dataset Structure
+
+### Schema (v2 - Normalized)
 
-This database contains organization records from multiple authoritative sources, each with:
-- Organization name (canonical)
-- Source identifier (LEI, CIK, UK Company Number, Wikidata QID)
-- Entity type classification (business, nonprofit, government, etc.)
-- Vector embeddings for semantic search (768-dim, using [embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m))
+The database uses SQLite with the [sqlite-vec](https://github.com/asg017/sqlite-vec) extension for vector similarity search.
+
+#### Organizations Table
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | INTEGER | Primary key |
+| `qid` | INTEGER | Wikidata QID as integer (e.g., 2283 for Q2283) |
+| `name` | TEXT | Organization name |
+| `name_normalized` | TEXT | Lowercased, normalized name |
+| `source_id` | INTEGER FK | Reference to source_types |
+| `source_identifier` | TEXT | LEI, CIK, Company Number, etc. |
+| `region_id` | INTEGER FK | Reference to locations |
+| `entity_type_id` | INTEGER FK | Reference to organization_types |
+| `from_date` | TEXT | Founding/registration date (ISO format) |
+| `to_date` | TEXT | Dissolution date (ISO format) |
+| `canon_id` | INTEGER | ID of canonical record |
+| `canon_size` | INTEGER | Size of canonical group |
+| `record` | JSON | Full source record (omitted in lite) |
+
+#### People Table
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | INTEGER | Primary key |
+| `qid` | INTEGER | Wikidata QID as integer |
+| `name` | TEXT | Display name |
+| `name_normalized` | TEXT | Lowercased, normalized name |
+| `source_id` | INTEGER FK | Reference to source_types |
+| `source_identifier` | TEXT | QID, Owner CIK, Person number |
+| `country_id` | INTEGER FK | Reference to locations |
+| `person_type_id` | INTEGER FK | Reference to people_types |
+| `known_for_role_id` | INTEGER FK | Reference to roles |
+| `known_for_org` | TEXT | Organization name |
+| `known_for_org_id` | INTEGER FK | Reference to organizations |
+| `from_date` | TEXT | Role start date (ISO format) |
+| `to_date` | TEXT | Role end date (ISO format) |
+| `birth_date` | TEXT | Date of birth (ISO format) |
+| `death_date` | TEXT | Date of death (ISO format) |
+| `record` | JSON | Full source record (omitted in lite) |
+
+#### Roles Table
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | INTEGER | Primary key |
+| `qid` | INTEGER | Wikidata QID (e.g., 484876 for CEO Q484876) |
+| `name` | TEXT | Role name (e.g., "Chief Executive Officer") |
+| `name_normalized` | TEXT | Normalized name |
+| `source_id` | INTEGER FK | Reference to source_types |
+| `canon_id` | INTEGER | ID of canonical role |
+
+#### Locations Table
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | INTEGER | Primary key |
+| `qid` | INTEGER | Wikidata QID (e.g., 30 for USA Q30) |
+| `name` | TEXT | Location name |
+| `name_normalized` | TEXT | Normalized name |
+| `source_id` | INTEGER FK | Reference to source_types |
+| `source_identifier` | TEXT | ISO code (e.g., "US", "CA") |
+| `parent_ids` | TEXT JSON | Parent location IDs in hierarchy |
+| `location_type_id` | INTEGER FK | Reference to location_types |
+
+#### Embedding Tables (sqlite-vec)
+
+| Table | Columns |
+|-------|---------|
+| `organization_embeddings` | org_id INTEGER, embedding FLOAT[768] |
+| `organization_embeddings_scalar` | org_id INTEGER, embedding INT8[768] |
+| `person_embeddings` | person_id INTEGER, embedding FLOAT[768] |
+| `person_embeddings_scalar` | person_id INTEGER, embedding INT8[768] |
+
+**Scalar (int8) embeddings** provide 75% storage reduction with ~92% recall at top-100.
+
+#### Enum Lookup Tables
+
+| Table | Values |
+|-------|--------|
+| `source_types` | gleif, sec_edgar, companies_house, wikidata |
+| `people_types` | executive, politician, government, military, legal, professional, academic, artist, media, athlete, entrepreneur, journalist, activist, scientist, unknown |
+| `organization_types` | business, fund, branch, nonprofit, ngo, foundation, government, international_org, political_party, trade_union, educational, research, healthcare, media, sports, religious, unknown |
+| `simplified_location_types` | continent, country, subdivision, city, district, other |
+
+### Data Splits
+
+| Config | Size | Contents |
+|--------|------|----------|
+| `entities-lite.db` | ~50GB | Core fields + embeddings only |
+| `entities.db` | ~74GB | Full records with source metadata |
+
+The lite version is recommended for most use cases.
+
+## Dataset Creation
+
+### Source Data
+
+#### Organizations
+
+| Source | Records | Identifier | Coverage |
+|--------|---------|------------|----------|
+| [GLEIF](https://www.gleif.org/) | ~3.2M | LEI (Legal Entity Identifier) | Global companies with LEI |
+| [SEC Edgar](https://www.sec.gov/) | ~100K+ | CIK (Central Index Key) | All SEC filers |
+| [Companies House](https://www.gov.uk/government/organisations/companies-house) | ~5M | Company Number | UK registered companies |
+| [Wikidata](https://www.wikidata.org/) | Variable | QID | Notable companies worldwide |
+
+#### People
+
+| Source | Records | Identifier | Coverage |
+|--------|---------|------------|----------|
+| [Wikidata](https://www.wikidata.org/) | Variable | QID | Notable people with English Wikipedia |
+| [SEC Form 4](https://www.sec.gov/) | ~280K/year | Owner CIK | US public company insiders |
+| [Companies House](https://www.gov.uk/government/organisations/companies-house) | ~15M+ | Person number | UK company officers |
+
+### Embedding Model
+
+| Property | Value |
+|----------|-------|
+| Model | `google/embeddinggemma-300m` |
+| Dimensions | 768 |
+| Framework | sentence-transformers |
+| Size | ~300M parameters |
 
-## Data Sources
+### Canonicalization
 
-| Source | Records | Identifier | Description |
-|--------|---------|------------|-------------|
-| GLEIF | ~3.2M | LEI (Legal Entity Identifier) | Global legal entities from the LEI system |
-| SEC Edgar | ~100K+ | CIK (Central Index Key) | US SEC-registered filers |
-| Companies House | ~5M | UK Company Number | UK registered companies |
-| Wikidata | Variable | Wikidata QID | Notable organizations from Wikidata |
+Records are linked across sources based on:
 
-## Entity Types
+**Organizations:**
+1. Same LEI (globally unique)
+2. Same ticker symbol
+3. Same CIK
+4. Same normalized name + region
 
-Organizations are classified into the following types:
+**People:**
+1. Same Wikidata QID
+2. Same normalized name + same organization
+3. Same normalized name + overlapping date ranges
 
-| Category | Types |
-|----------|-------|
-| Business | `business`, `fund`, `branch` |
-| Non-profit | `nonprofit`, `ngo`, `foundation`, `trade_union` |
-| Government | `government`, `international_org`, `political_party` |
-| Other | `educational`, `research`, `healthcare`, `media`, `sports`, `religious`, `unknown` |
-
-## Database Variants
-
-| File | Description | Use Case |
-|------|-------------|----------|
-| `entities.db` | Full database with complete source record metadata | When you need full record details |
-| `entities-lite.db` | Lite version without record data | Default - faster download, smaller size |
-| `entities.db.gz` | Compressed full database | When bandwidth is limited |
-| `entities-lite.db.gz` | Compressed lite database | Smallest download size |
-
-## Schema
-
-### organizations table
-```sql
-CREATE TABLE organizations (
-    id INTEGER PRIMARY KEY AUTOINCREMENT,
-    name TEXT NOT NULL,
-    name_normalized TEXT NOT NULL,
-    source TEXT NOT NULL,  -- 'gleif', 'sec_edgar', 'companies_house', 'wikipedia'
-    source_id TEXT NOT NULL,
-    region TEXT NOT NULL DEFAULT '',
-    entity_type TEXT NOT NULL DEFAULT 'unknown',
-    record TEXT NOT NULL,  -- JSON with full source record (empty in lite version)
-    UNIQUE(source, source_id)
-);
-```
+**Source priority:** gleif > sec_edgar > companies_house > wikidata
 
-### organization_embeddings table (sqlite-vec)
-```sql
-CREATE VIRTUAL TABLE organization_embeddings USING vec0(
-    org_id INTEGER PRIMARY KEY,
-    embedding float[768]
-);
-```
+## Usage
 
-## Usage with corp-extractor
+### Installation
 
 ```bash
-# Install
 pip install corp-extractor
+```
+
+### Download
 
-# Download the database (lite version by default)
+```bash
+# Download lite version (recommended)
 corp-extractor db download
 
 # Download full version
 corp-extractor db download --full
+```
+
+**Storage location:** `~/.cache/corp-extractor/entities-v2.db`
+
+### Search
 
-# Search for an organization
+```bash
+# Search organizations
 corp-extractor db search "Microsoft"
 
+# Search people
+corp-extractor db search-people "Tim Cook"
+
+# Search roles
+corp-extractor db search-roles "CEO"
+
+# Search locations
+corp-extractor db search-locations "California"
+
 # Check database status
 corp-extractor db status
 ```
@@ -104,46 +242,141 @@ corp-extractor db status
 ### Python API
 
 ```python
-from statement_extractor.database import OrganizationDatabase, CompanyEmbedder
+from statement_extractor.database import OrganizationDatabase, PersonDatabase
+
+# Search organizations
+org_db = OrganizationDatabase()
+matches = org_db.search_by_name("Microsoft Corporation", top_k=5)
+for match in matches:
+    print(f"{match.company.name} ({match.company.source}:{match.company.source_id})")
+    print(f"  Similarity: {match.similarity_score:.3f}")
+
+# Search people
+person_db = PersonDatabase()
+matches = person_db.search_by_name("Tim Cook", top_k=5)
+for match in matches:
+    print(f"{match.person.name} - {match.person.known_for_role} at {match.person.known_for_org}")
+```
+
+### Use in Pipeline
 
-# Load database
-database = OrganizationDatabase()
-embedder = CompanyEmbedder()
+```python
+from statement_extractor.pipeline import ExtractionPipeline
 
-# Search by embedding similarity
-query_embedding = embedder.embed("Microsoft Corporation")
-results = database.search(query_embedding, top_k=5)
+pipeline = ExtractionPipeline()
+ctx = pipeline.process("Microsoft CEO Satya Nadella announced new AI features.")
 
-for record, similarity in results:
-    print(f"{record.name} ({record.source}:{record.source_id}) - {similarity:.3f}")
+for stmt in ctx.labeled_statements:
+    print(f"{stmt.subject_fqn} --[{stmt.statement.predicate}]--> {stmt.object_fqn}")
 ```
 
-## Building Your Own Database
+## Technical Details
+
+### Vector Search Performance
+
+| Database Size | Search Time | Memory |
+|---------------|-------------|--------|
+| 100K records | ~50ms | ~500MB |
+| 1M records | ~200ms | ~3GB |
+| 8M records | ~500ms | ~20GB |
+
+### Similarity Thresholds
+
+| Score | Interpretation |
+|-------|----------------|
+| > 0.85 | Strong match (likely same entity) |
+| 0.70 - 0.85 | Good match (probable same entity) |
+| 0.55 - 0.70 | Moderate match (may need verification) |
+| < 0.55 | Weak match (likely different entity) |
+
+### Canonical ID Format
+
+| Source | Prefix | Example |
+|--------|--------|---------|
+| GLEIF | `LEI` | `LEI:INR2EJN1ERAN0W5ZP974` |
+| SEC Edgar | `SEC-CIK` | `SEC-CIK:0000789019` |
+| Companies House | `UK-CH` | `UK-CH:00445790` |
+| Wikidata | `WIKIDATA` | `WIKIDATA:Q2283` |
+
+## Building from Source
 
 ```bash
-# Import from authoritative sources
+# Import data sources
 corp-extractor db import-gleif --download
 corp-extractor db import-sec --download
 corp-extractor db import-companies-house --download
-corp-extractor db import-wikidata --limit 50000
+corp-extractor db import-wikidata --limit 100000
+corp-extractor db import-people --all --limit 50000
+
+# Link equivalent records
+corp-extractor db canonicalize
+
+# Generate scalar embeddings (75% smaller)
+corp-extractor db backfill-scalar
+
+# Create lite version for deployment
+corp-extractor db create-lite ~/.cache/corp-extractor/entities.db
+```
+
+### Wikidata Dump Import (Recommended for Large Imports)
+
+```bash
+# Download and import from Wikidata dump (~100GB)
+corp-extractor db import-wikidata-dump --download --limit 50000
+
+# Import only people
+corp-extractor db import-wikidata-dump --download --people --no-orgs
 
-# Upload to HuggingFace
-export HF_TOKEN="hf_..."
-corp-extractor db upload
+# Import only locations
+corp-extractor db import-wikidata-dump --dump dump.json.bz2 --locations --no-people --no-orgs
+
+# Resume interrupted import
+corp-extractor db import-wikidata-dump --dump dump.bz2 --resume
 ```
 
+## Considerations for Using the Data
+
+### Social Impact
+
+This dataset enables entity linking for NLP applications. Users should be aware that:
+- Organization and people records may be incomplete or outdated
+- Historic people (deceased) are included with `death_date` field
+- Not all notable entities are covered
+
+### Biases
+
+- Coverage is weighted toward English-speaking countries (US, UK) due to source availability
+- Wikidata coverage depends on Wikipedia notability criteria
+- SEC and Companies House data is limited to their respective jurisdictions
+
+### Limitations
+
+- The database does not automatically deduplicate across sources
+- Embedding similarity is not perfect for entity disambiguation
+- Updates require re-importing from source data
+
 ## License
 
-MIT License - the database structure and embedding generation code are MIT licensed.
+Apache 2.0
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{entity_references_2024,
+  title = {Entity References Database},
+  author = {Corp-o-Rate-Community},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/Corp-o-Rate-Community/entity-references}
+}
+```
+
+## Dataset Card Authors
 
-Individual data sources have their own licenses:
-- GLEIF: Open license for LEI data
-- SEC Edgar: Public domain (US government)
-- Companies House: Open Government Licence
-- Wikidata: CC0 (public domain)
+Corp-o-Rate-Community
 
-## Links
+## Dataset Card Contact
 
-- [Corp-Extractor on PyPI](https://pypi.org/project/corp-extractor/)
-- [Corp-Extractor GitHub](https://github.com/corp-o-rate/statement-extractor)
-- [Statement Extractor Model](https://huggingface.co/Corp-o-Rate-Community/statement-extractor)
+Open an issue on the [GitHub repository](https://github.com/corp-o-rate/statement-extractor) for questions or feedback.