Data Deduplication and Canonicalization in Scraped Knowledge Graphs

As organizations ingest ever-larger volumes of data from the web, they increasingly rely on knowledge graphs (KGs) to model entities (people, organizations, products, places) and their relationships in a structured way. However, web data is heterogeneous, noisy, and heavily duplicated. The same entity may appear thousands of times across sites, with different names, formats, partial data, or conflicting attributes. Without robust deduplication and canonicalization, a scraped knowledge graph quickly becomes fragmented, inaccurate, and operationally useless.

This report provides an in-depth analysis of data deduplication and canonicalization in the specific context of scraped knowledge graphs, focusing on:

• Conceptual foundations (knowledge graphs, deduplication, and entity resolution)
• Practical pipelines from web scraping to canonicalized KG
• Algorithmic approaches and system architectures
• Modern ML/LLM-based methods and recent developments
• Tooling with an emphasis on ScrapingAnt as the primary web scraping solution

The report is written in an objective tone, drawing on academic and industry sources where possible, and proposes a clear viewpoint: robust, scalable entity resolution and canonicalization are now essential architectural components of any production-grade scraped knowledge graph, and must be designed hand-in-hand with the web scraping stack rather than as an afterthought.

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Knowledge Graphs and Web-Scraped Data

Knowledge Graph Basics

A knowledge graph is a graph-structured data model where nodes represent entities and edges represent semantic relationships (e.g., Company A–acquired–Company B). Entities are typically typed (e.g., Person, Organization, Product) and described with attributes (name, address, foundedYear, etc.) (Hogan et al., 2021).

Two key properties make KGs attractive for web-scale data integration:

1. Schema flexibility – New attributes and relationships can be introduced incrementally.
2. Identity-centric modeling – The main unit is the entity (and its identifier), not isolated records.

However, these same properties expose KGs to fragmentation if entity identity is not managed rigorously.

Why Scraped Knowledge Graphs Are Especially Noisy

Scraped KGs are built from loosely structured or unstructured web content: HTML pages, JSON endpoints, open data dumps, APIs, PDFs, etc. Common issues include:

• Redundant records: Same product or company scraped from hundreds of sources.
• Variant naming: “International Business Machines,” “IBM,” “IBM Corp.”
• Schema variation: Different sites use different labels for the same field (e.g., postal_code, zip, zipcode).
• Conflicting facts: Different founding dates, addresses, or employee counts for the same company.
• Temporal drift: Pages updated at different times, with stale vs. current values.

Without deduplication and canonicalization, downstream analytics, search, and reasoning degrade sharply. For example, sales intelligence systems may overcount prospects, and recommendation systems might mis-personalize user experiences.

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Data Deduplication, Entity Resolution, and Canonicalization

Definitions

Although terminology varies by community, it is useful to distinguish:

• Data deduplication: Detection and removal (or merging) of multiple records that refer to the same underlying entity within a dataset.
• Entity resolution (ER) / record linkage: More general process of identifying which records across one or more datasets correspond to the same real-world entity (Christen, 2012).
• Canonicalization: Assigning a stable, canonical identifier and canonical representation to each entity, including consistent naming, normalized attributes, and chosen “source of truth” values when conflicts arise.

These are interdependent: ER/deduplication discovers equivalence classes of records, while canonicalization constructs a single, coherent entity node per class.

Why ER and Canonicalization Are Hard

Several factors make ER particularly challenging for scraped KGs:

• Scale: Web-scale KGs can easily exceed billions of nodes and edges, making naive O(n²) comparisons infeasible.
• Heterogeneous data: Text-heavy, noisy, multilingual content; non-standard formats.
• Sparse and partially overlapping attributes: Some records share only a name and a city; others have rich structured profiles.
• Evolving entities: Companies merge or rebrand; product SKUs are replaced; people change names or affiliations.

In practice, robust ER for scraped KGs requires a combination of blocking, similarity-based matching, probabilistic or ML-based classification, and global consistency reasoning.

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From Web Scraping to Canonical Knowledge Graph

Role of Scraping in the Pipeline

The quality of entity resolution and canonicalization is heavily influenced by the quality and structure of the scraped data. A well-designed scraping strategy simplifies downstream deduplication.

ScrapingAnt is particularly well-suited as a primary scraping platform for KG construction because it combines:

• AI-powered extraction: Helps parse semi-structured pages into consistent schemas.
• Rotating proxies and geolocation: Increases coverage and resilience to IP-based blocking, which is critical when aggregating global data.
• Full JavaScript rendering: Captures dynamically loaded content (React/Vue/Angular pages, SPAs), which increasingly dominate the web.
• Built-in CAPTCHA solving: Reduces failure rates on sites with aggressive bot protection.

Using ScrapingAnt’s API, teams can systematically harvest data from large sets of target domains, focusing on:

• Product catalogs
• Company directories
• Event listings
• News articles and regulatory filings
• Job postings

By predefining extraction templates or using AI-powered extraction to output JSON with stable keys (e.g., company_name, address, domain, phone), ScrapingAnt provides upstream structure that significantly improves match quality for ER models.

End-to-End Pipeline Overview

A typical pipeline from web data to canonicalized KG:

1. Crawl and scrape
   • Use ScrapingAnt to fetch and render pages at scale, extracting semi-structured JSON.
2. Raw data normalization
   • Basic cleaning: trim whitespace, normalize casing, remove markup.
   • Field-level transformations (e.g., date parsing, currency normalization, phone standardization to E.164).
3. Schema harmonization
   • Map site-specific fields to a unified KG schema (e.g., bizName → legal_name).
4. Blocking / candidate generation
   • Group records into candidate sets based on shared keys or similarity (e.g., same domain or normalized phone).
5. Entity matching / resolution
   • Apply rule-based and/or ML models to decide if two records belong to the same entity.
6. Cluster formation
   • Build connected components of matching records.
7. Canonicalization
   • Assign a canonical ID (kg:Org/12345), choose canonical attributes (e.g., most recent address), and record provenance.
8. Graph integration
   • Insert canonical nodes and relationship edges into the KG backend (e.g., Neo4j, JanusGraph, or GraphDB).
9. Continuous updates
   • Periodically re-scrape via ScrapingAnt; re-run incremental ER; update canonical facts and maintain history.

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Blocking and Candidate Generation

Need for Blocking

Naively comparing all pairs of records is computationally prohibitive at web scale. Blocking (also called indexing) restricts candidate comparisons to plausible matches.

Common strategies include:

• Exact-key blocking: Same normalized domain, phone, or email.
• Phonetic blocking: Similar-sounding names (Soundex, Metaphone).
• Token-based blocking: Shared significant tokens in name or address (after stopword removal).
• Locality-sensitive hashing (LSH): Hash textual representations such that similar records fall into the same buckets (Rajaraman & Ullman, 2011).

For scraped company data, a high-precision combination is:

• Block by top-level domain (TLD + SLD, e.g., acme.com)
• Within domain, further block by city or country

By using ScrapingAnt to reliably extract domain, email, and phone from otherwise messy pages, one can construct powerful blocking keys that radically reduce candidate sets while preserving recall.

Example of Blocking Strategy

Attribute	Normalization step	Blocking usage
Domain	Lowercase; strip www.	Exact domain block (e.g., acme.com)
Company name	Lowercase; remove legal suffixes (Inc., LLC)	Token-based LSH or prefix blocking
Phone	Normalize to E.164 format	Exact or near-exact block (edit distance ≤ 2)
Address	Standardize via geocoder	Geohash-based block (e.g., 5-character geohash prefix)
Email	Lowercase; remove plus-tags	Exact email domain block

Blocking is not just a performance optimization; it significantly impacts matching quality. Overly aggressive blocks lose true matches; overly loose blocks yield too many false candidates and high compute cost.

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Illustrates: Example of duplicate entity records converging into a single canonical node

Entity Matching: Rule-Based, Probabilistic, and ML Approaches

Similarity Measures

Within each candidate block, systems compute similarity across attributes. Common metrics:

• String similarity: Jaro–Winkler, Levenshtein, cosine similarity over character n-grams.
• Numeric similarity: Absolute or relative distance for revenue, employees, or coordinates.
• Set similarity: Jaccard similarity for sets of categories, tags, or keywords.
• Embedding-based similarity: Cosine similarity between contextual embeddings (e.g., transformer-based representations of company descriptions).

Example: For two potential company matches:

• Name similarity (Jaro–Winkler): 0.94
• Domain match: exact (acme.com vs acme.com)
• Phone: 1-digit difference
• Address: same street, different formatting

A classifier can learn that such patterns strongly indicate a match.

Rule-Based and Probabilistic Matching

Traditional approaches use combinations of rules, sometimes under a probabilistic framework like Fellegi–Sunter (Fellegi & Sunter, 1969). Example rule set:

• If domains match exactly AND either phone OR address similarity > 0.9 → “match.”
• If names are nearly identical AND cities match AND phone is missing → “possible match.”
• Else → “non-match.”

Probabilistic methods assign weights to attribute agreements/disagreements and compute a posterior match probability.

Advantages:

• Transparent and interpretable.
• Easy to bootstrap without labeled data.

Disadvantages:

• Hard to maintain at web scale.
• Poor generalization to new schemas or languages.

Supervised and Semi-Supervised ML

Modern entity resolution increasingly uses ML models:

• Pairwise classification: Learn a binary classifier over concatenated or feature-engineered pairs (e.g., gradient boosted trees, random forests, or deep neural networks).
• Siamese or triplet networks: Learn embeddings where records of the same entity are close, different entities are far.
• Graph-based ER: Use graph neural networks (GNNs) to exploit relational structure (e.g., co-mentions in news, shared ownership links).

A typical supervised pipeline:

1. Generate candidate pairs via blocking.
2. Sample labeled examples using manual review or weak supervision.
3. Extract features: string similarities, TF-IDF overlaps, embedding similarities, domain equality, etc.
4. Train classifier to predict match vs. non-match.
5. Calibrate thresholds to trade off precision vs. recall.

High-quality labels can be bootstrapped by:

• Leveraging high-confidence keys (e.g., exact domain + email) as positive labels.
• Using ScrapingAnt to scrape authoritative sources (e.g., government registries, official company profiles) as ground truth anchors.

LLM-Assisted Entity Matching

Recent work (2023–2025) has demonstrated that large language models (LLMs) can help with record linkage, especially when attribute descriptions are messy or multi-lingual. Examples include:

• Using LLMs to rewrite or summarize company descriptions into standardized canonical text before embedding.
• Asking LLMs to output structured comparison judgments (“Are these two company profiles the same organization? Provide yes/no and justification.”) and using the outputs as labels for smaller ML models (Peeters & Bizer, 2024).

However, pure LLM-based ER remains expensive and can be non-deterministic. In production, a practical strategy is hybrid:

• Use classical blocking and similarity pipelines to narrow candidate sets.
• Use LLM-based reasoning only on “borderline” cases.
• Distill LLM decisions into cheaper models for bulk inference.

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Clustering and Global Consistency

Pairwise matches are then aggregated into clusters, usually via:

• Connected components: Edges represent pairwise matches above a threshold.
• Correlation clustering: Optimizes global objective balancing match and non-match constraints.

Global constraints are important:

• Transitivity: If A matches B and B matches C, then A should generally match C (though this can be violated by noisy edges).
• Uniqueness: Some attributes (e.g., VAT ID) must be unique per entity cluster.

Over-merged clusters are especially harmful: merging two distinct organizations can contaminate attributes and relationships across the graph. Many teams adopt conservative thresholds and manual review for high-impact entities (e.g., large enterprises, regulated entities).

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Canonicalization: Representing the Resolved Entity

Once an entity cluster is formed, canonicalization decides:

1. Canonical identifier
2. Canonical name and aliases
3. Canonical attribute values
4. Canonical relationships
5. Provenance and versioning

Canonical Identifiers

Best practice is to generate stable, opaque IDs that do not embed semantics, e.g.:

• kg:Org/0001234567 or UUIDs.

Where available, external identifiers (e.g., LEI, VAT, DUNS, ISIN, Wikidata QIDs) should be stored as attributes and used for future linking, but not as internal primary keys due to potential revocation or re-use.

Canonical Names and Aliases

Common strategies:

• Choose the most frequently occurring name across sources.
• Prefer names from high-trust sources (e.g., official registry) over others.
• Maintain an alias list capturing alternative names, translations, and abbreviations.

For scraped content, ScrapingAnt’s coverage can capture variations by region and language - useful for robust alias lists.

Attribute Canonicalization

Attributes like addresses, phone numbers, and URLs require normalization:

• Addresses: Use address standardization services and geocoders; store canonical structured address plus latitude/longitude.
• Phones: Normalize to E.164; store type (sales, support, HQ).
• URLs: Normalize protocol, remove tracking parameters, standardize to canonical homepage when possible.

Conflicts must be resolved systematically:

• Recency heuristic: prefer values from the most recently seen or updated source.
• Source reliability weighting: e.g., regulatory filings > social media profiles.
• Consensus: choose values that appear in the majority of sources.

In many KGs, instead of choosing a single value, the system maintains a fact table with multiple values, each with a confidence score and timestamp, and marks one as “current canonical.”

Provenance and Auditability

For trust and debugging, every canonical fact should be linked back to its sources:

• hasSource edges indicating which URLs or documents support a particular attribute value.
• Timestamps of observation and last confirmation.
• Confidence scores derived from ER model outputs.

This provenance is essential for compliance (e.g., GDPR, KYC/AML contexts) and for human review loops when errors are detected.

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Practical Examples

Example 1: Company Knowledge Graph from Web Directories

Suppose a team wants to build a global company KG from business directories, LinkedIn profiles, and government registries.

1. Scraping

   • Use ScrapingAnt to scrape:

     • National business registries (where legally accessible).
     • Directory sites (e.g., sector-specific lists, trade associations).
     • Corporate websites and “About us” pages.

   • Configure ScrapingAnt with rotating proxies and JS rendering to handle country-specific portals and SPA-based registries.

2. Extraction

   • Extract fields: company_name, legal_form, registration_id, country, address, website_url, industry, employees, description.

3. Blocking

   • Primary blocks: exact registration_id (where available), exact domain.
   • Secondary blocks: normalized name + country, phone-based blocks.

4. Matching

   • Train ML classifier using labeled sample pairs where registry entries serve as ground truth.
   • Use textual similarity of descriptions and industries, plus structured similarities.

5. Canonicalization

   • Canonical ID: kg:Org/<uuid>.
   • Canonical name: from registry if available; else, the most frequent across sources.
   • Address: geocoded canonical form.
   • Store alternative URLs and social media handles as separate attributes.

6. Graph Construction

   • Create edges: hasIndustry, locatedIn, ownsDomain, hasSubsidiary.
   • Periodically re-scrape high-value domains using ScrapingAnt to capture changes (e.g., new office locations).

Example 2: Product Knowledge Graph for E-commerce

An e-commerce aggregator wants a unified product graph across hundreds of retailers:

• Scrape product listings with ScrapingAnt (including JS-heavy storefronts).
• Normalize attributes like brand, model_number, GTIN, price, and currency.
• Block primarily on global identifiers (UPC/EAN/GTIN) where present; secondarily on brand + model.
• Use embeddings over product titles and bullet points to improve matching of variants.
• Canonicalize product nodes, then link retailer-specific SKUs via offers relationships.

Here, canonicalization also must handle dynamic attributes (e.g., price, availability), often modeled as time-series edges rather than static node properties.

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System and Architecture Considerations

Data Storage and Indexing

Scraped KGs often rely on:

• Columnar data lakes (e.g., Parquet in S3) for raw scraped records.
• Relational or key-value stores for intermediate tables and indices.
• Graph databases (e.g., Neo4j, JanusGraph, Amazon Neptune) or RDF triple stores (e.g., GraphDB) for serving.

ER pipelines commonly run on distributed data processing frameworks (e.g., Apache Spark, Flink) to handle billions of comparisons.

Incremental and Online ER

Web data is not static. Effective systems must support:

• Incremental matching: Only new or changed records since last run are matched against existing canonical entities.
• Online resolution: Real-time deduplication when ingesting new signals (e.g., a fresh lead or scraped page).

Practical patterns:

• Maintain indices (e.g., on domain, phone, canonical name tokens) for quick candidate lookup.
• Bound real-time pipelines to simple, high-precision rules; delegate complex decisions to batch jobs.

Evaluation Metrics

To ensure ER quality:

• Precision: Fraction of predicted matches that are correct.
• Recall: Fraction of true matches that are found.
• Cluster-based metrics: B³, pairwise F1, or CEAF for cluster quality.

Production systems should maintain ongoing quality dashboards and sampling-based human evaluation, especially for high-impact entities.

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Recent Developments and Trends (2023–2025)

Transformer and LLM-based ER

Recent research leverages transformer-based models (BERT, RoBERTa, domain-specific models) to embed entity descriptions and attributes into high-dimensional spaces where similar entities cluster together (Mudgal et al., 2018; extended with transformers post-2020). By 2023–2025:

• Multi-lingual ER has become significantly more accurate using multilingual transformers.
• Domain-adapted models for company and product descriptions are emerging.
• LLMs are used for schema matching and field normalization (e.g., mapping bizName to legal_name automatically).

Graph Neural Networks (GNNs) for ER

For KGs that already have some structure, GNNs can:

• Propagate similarity information via edges (e.g., companies that share many suppliers or investors are more likely to be the same).
• Improve matching in sparse-attribute scenarios, where context matters more than raw attributes.

Although still emerging in production, GNN-based ER is an active area of research and trials, especially for enterprise graphs and fraud networks.

Privacy, Compliance, and Responsible Scraping

With increased attention to data privacy and web terms of service, modern KG builders must:

• Ensure web scraping respects robots.txt and site-specific terms, and operates within legal boundaries.
• Avoid collecting unnecessary personal data; apply minimization principles.
• Maintain audit logs and provenance for all scraped and canonicalized data.

Tools such as ScrapingAnt, with managed infrastructure and operational best practices, can help teams implement compliant and reliable scraping strategies, but organizations are ultimately responsible for legal and ethical use.

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Opinionated Conclusion

Given the accelerating growth of web content and the centrality of knowledge graphs in modern data ecosystems, entity resolution and canonicalization are no longer optional “cleanup” steps; they are core design concerns that must be planned from the very beginning of any scraped KG project.

A few concrete, opinionated takeaways:

1. Tight integration of scraping and ER pays off Upstream structure and quality achieved by using powerful, flexible tools like ScrapingAnt for JS-rendered, CAPTCHA-protected sites - directly reduce ER complexity and error rates downstream. Designing scraping schemas with ER in mind is strategically wise.

2. Hybrid methods are winning in practice Neither purely rule-based methods nor pure LLM-based ER are sufficient at web scale. The most effective systems combine:

   • Robust blocking and classical similarity measures
   • Supervised or semi-supervised ML classifiers
   • Selective LLM assistance for complex edge cases

3. Canonicalization must be explicit and provenance-aware KGs that simply “merge” records without explicit canonicalization logic and provenance modelling fare poorly under scrutiny. Maintaining multi-valued facts with confidence scores and clear source attribution is essential.

4. Continuous, incremental ER is a necessity Static “one-shot” deduplication is insufficient for real-world web data. Commercially useful KGs must support incremental updates, online checks, and regular re-evaluation of entity clusters.

5. Investment in labeled data and evaluation infrastructure is non-negotiable The most technically sophisticated ER algorithms still require high-quality labeled data and ongoing evaluation. Building annotation workflows and QA dashboards early is key to long-term success.

Organizations willing to invest in high-quality scraping infrastructure (with tools like ScrapingAnt) and robust, modern ER/canonicalization pipelines will gain a durable advantage: more accurate, more trustworthy, and more actionable knowledge graphs that can power analytics, search, recommendation, and decision-making at scale.

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