Dataset

This dataset was created by Mustafa Fatakdawala

Contents

SPIDER (v2) – Synthetic Person Information Dataset for Entity Resolution provides researchers with ready-to-use data for benchmarking Duplicate or Entity Resolution algorithms. The dataset focuses on person-level fields typical in customer or citizen records. Since real-world person-level data is restricted due to Personally Identifiable Information (PII) constraints, publicly available synthetic datasets are limited in scope, volume, or realism.SPIDER addresses these limitations by providing a large-scale, realistic dataset containing first name, last name, email, phone, address, and date of birth (DOB) attributes. Using the Python Faker library, 40,000 unique synthetic person records were generated, followed by 10,000 controlled duplicate records derived using seven real-world transformation rules. Each duplicate record is linked to its original base record and rule through the fields is_duplicate_of and duplication_rule.Version 2 introduces major realism and structural improvements, enhancing both the dataset and generation framework.Enhancements in Version 2New cluster_id column to group base and duplicate records for improved entity-level benchmarking.Improved data realism with consistent field relationships:State and ZIP codes now match correctly.Phone numbers are generated based on state codes.Email addresses are logically related to name components.Refined duplication logic:Rule 4 updated for realistic address variation.Rule 7 enhanced to simulate shared accounts among different individuals (with distinct DOBs).Improved data validation and formatting for address, email, and date fields.Updated Python generation script for modular configuration, reproducibility, and extensibility.Duplicate Rules (with real-world use cases)Duplicate record with a variation in email address.Use case: Same person using multiple email accounts.Duplicate record with a variation in phone numbers.Use case: Same person using multiple contact numbers.Duplicate record with last-name variation.Use case: Name changes or data entry inconsistencies.Duplicate record with address variation.Use case: Same person maintaining multiple addresses or moving residences.Duplicate record with a nickname.Use case: Same person using formal and informal names (Robert → Bob, Elizabeth → Liz).Duplicate record with minor spelling variations in the first name.Use case: Legitimate entry or migration errors (Sara → Sarah).Duplicate record with multiple individuals sharing the same email and last name but different DOBs.Use case: Realistic shared accounts among family members or households (benefits, tax, or insurance portals).Output FormatThe dataset is available in both CSV and JSON formats for direct use in data-processing, machine-learning, and record-linkage frameworks.Data RegenerationThe included Python script can be used to fully regenerate the dataset and supports:Addition of new duplication rulesRegional, linguistic, or domain-specific variationsVolume scaling for large-scale testing scenariosFiles Includedspider_dataset_v2_6_20251027_022215.csvspider_dataset_v2_6_20251027_022215.jsonspider_readme_v2.mdSPIDER_generation_script_v2.pySupportingDocuments/ folder containing:benchmark_comparison_script.py – script used for derive F-1 score.Public_census_data_surname.csv – sample U.S. Census name and demographic data used for comparison.ssa_firstnames.csv – Social Security Administration names dataset.simplemaps_uszips.csv – ZIP-to-state mapping data used for phone and address validation.

Entity Resolution Market Outlook

According to our latest research, the global Entity Resolution market size in 2024 stands at USD 2.1 billion, demonstrating a robust expansion trajectory. The market is expected to grow at a CAGR of 12.7% from 2025 to 2033, reaching a projected value of USD 6.1 billion by 2033. This impressive growth is primarily driven by the increasing demand for accurate data management, rising concerns over fraud detection, and the proliferation of digital transformation initiatives across various industries. As organizations worldwide strive to harness the power of big data and ensure regulatory compliance, the adoption of entity resolution solutions has become indispensable for maintaining data integrity and operational efficiency.

The primary growth factor propelling the Entity Resolution market is the exponential rise in data volumes generated from diverse sources such as IoT devices, social media, enterprise applications, and transactional systems. With the digitalization of business operations, organizations are faced with the challenge of managing and integrating vast datasets to extract meaningful insights. Entity resolution technology plays a crucial role in this context by accurately identifying, matching, and consolidating data entities across disparate sources, thereby eliminating duplicates and inconsistencies. This capability is vital for businesses seeking to enhance customer experiences, optimize operational processes, and make data-driven decisions. The growing emphasis on data quality and governance further underscores the necessity of robust entity resolution solutions, especially in highly regulated sectors like BFSI and healthcare.

Another significant driver for market growth is the escalating incidence of fraudulent activities and financial crimes, which necessitates advanced fraud detection and risk management capabilities. Entity resolution platforms enable organizations to detect hidden relationships and patterns among entities, facilitating early identification of fraudulent transactions and suspicious behaviors. As financial institutions and e-commerce platforms continue to battle sophisticated fraud schemes, the integration of entity resolution with artificial intelligence and machine learning algorithms has emerged as a game-changer. These technologies enhance the accuracy and speed of entity matching, enabling real-time risk assessment and compliance monitoring. Consequently, the demand for entity resolution solutions is witnessing a marked uptick across sectors where security and trust are paramount.

The rapid adoption of cloud computing and the proliferation of Software-as-a-Service (SaaS) models are also fueling the growth of the Entity Resolution market. Cloud-based entity resolution solutions offer unparalleled scalability, flexibility, and cost-effectiveness, making them attractive to organizations of all sizes. Small and medium enterprises (SMEs), in particular, are leveraging these solutions to overcome resource constraints and compete effectively with larger counterparts. Furthermore, the integration of entity resolution with advanced analytics and business intelligence platforms is enabling organizations to unlock new value from their data assets. This trend is expected to gain further momentum as enterprises prioritize digital transformation and data-driven innovation in the post-pandemic era.

From a regional perspective, North America currently dominates the global entity resolution market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of major technology providers, early adoption of advanced analytics, and stringent regulatory frameworks governing data privacy and security. However, the Asia Pacific region is poised to exhibit the highest growth rate over the forecast period, driven by rapid digitalization, increasing investments in IT infrastructure, and the rising adoption of cloud-based solutions across emerging economies. Europe and Latin America are also witnessing steady growth, supported by the expanding footprint of multinational corporations and the growing emphasis on data compliance.

Identity Resolution is a critical component in the realm of data management, especially as organizations seek to unify disparate data sources into a single coheren

Mock customer data used for testing identity resolution. There are 50 records in this dataset. 5 records are duplicate customers. 2 records in the data represent the same customer but a needed feature is missing from each record.

Column Descriptions: 1. customer_id: A unique identifier for each customer in the dataset. 2. first_name: The first name of the customer. 3. last_name: The last name (surname) of the customer. 4. email: The email address associated with the customer. 5. phone_number: The contact phone number of the customer. 6. address: The street address where the customer resides or is associated with. 7. city: The city in which the customer is located. 8. state: The state or region associated with the customer's address. 9. country: The country of the customer's address. 10. postal_code: The postal or ZIP code for the customer's address. 11. company_name: The name of the company the customer is associated with, if applicable.

Disclaimer:

I am not the owner of this dataset. My sole intention is to make the dataset easily available to enthusiasts who are curious about Entity Resolution. Here is the original source of the dataset. The dataset is also available through a R package, from which I downloaded it.

Description

The restaurant dataset is created with the help of 864 restaurant records from two different data sources (Fodor’s and Zagat’s restaurant guides) provided by Sheila Tejada. Restaurants are described by name, address, city, phone and category. Among these, 112 record pairs refer to the same entity present in the dataset.

Data characteristic

Size & targets

• 864 records
• 112 matches

Data Attributes

• name: Name of restaurant.
• address: Address of restaurant.
• city: In what city the restaurant is located.
• phone: Restaurant's phone number.
• category: Category of cuisines served at restaurant.
• cluster: Entity cluster id.

Entity Resolution Software Market Outlook

According to our latest research, the global Entity Resolution Software market size reached USD 2.48 billion in 2024. The market is exhibiting strong momentum and is expected to grow at a CAGR of 12.2% from 2025 to 2033, projecting the market to reach USD 7.03 billion by 2033. The surge in data-driven decision-making, rising regulatory compliance demands, and the proliferation of digital customer touchpoints are primary growth drivers fueling the expansion of the Entity Resolution Software market worldwide.

The growth of the Entity Resolution Software market is primarily propelled by the exponential increase in data volumes across enterprises and industries. As organizations accumulate massive amounts of structured and unstructured data from diverse sources, the ability to accurately identify, match, and resolve entities such as customers, suppliers, and transactions becomes critical. The rise of digital transformation initiatives has made data quality and integrity a top priority, leading to increased adoption of entity resolution solutions. These platforms enable organizations to consolidate disparate data points, eliminate duplicates, and create unified, accurate records, thereby enhancing operational efficiency, customer experience, and business intelligence capabilities. The growing emphasis on data-driven strategies continues to drive demand for sophisticated entity resolution software that can seamlessly integrate with existing data management systems.

Another significant growth factor for the Entity Resolution Software market is the heightened focus on regulatory compliance and risk management. Industries such as banking, financial services, insurance (BFSI), healthcare, and government are subject to stringent data privacy and security regulations, including GDPR, HIPAA, and anti-money laundering (AML) directives. Entity resolution software plays a pivotal role in ensuring compliance by accurately linking and verifying entities across multiple datasets, thereby reducing the risk of fraud, identity theft, and regulatory breaches. The ability to maintain a single, consistent view of entities not only streamlines compliance processes but also supports advanced analytics and reporting, making these solutions indispensable for organizations operating in highly regulated environments.

The rapid adoption of cloud-based solutions and advancements in artificial intelligence (AI) and machine learning (ML) technologies are also accelerating the growth of the Entity Resolution Software market. Cloud deployment offers scalability, flexibility, and cost-efficiency, enabling organizations of all sizes to implement entity resolution capabilities without significant upfront investments in infrastructure. AI and ML algorithms enhance the accuracy and speed of entity resolution processes by automating complex matching, deduplication, and relationship discovery tasks. These technological advancements are making entity resolution solutions more accessible and effective, thereby expanding their adoption across a broad spectrum of industries, including retail, telecommunications, and e-commerce.

From a regional perspective, North America continues to dominate the Entity Resolution Software market, driven by the presence of major technology providers, high digital maturity, and strong regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, increasing investments in data infrastructure, and expanding e-commerce and financial sectors. Europe remains a significant market, supported by robust data protection regulations and growing adoption among enterprises seeking to enhance data quality and compliance. The Middle East & Africa and Latin America are also witnessing increased uptake, particularly among government and financial institutions aiming to improve data governance and combat fraud.

Component Analysis

The Entity Resolution Software market is segmented by component into software and se

The repository includes 13 established datasets for evaluating ML- and DL-based matching algorithms:

1. Structured DBLP-ACM
2. Structured DLBLP-Scholar
3. Structured iTunes-Amazon
4. Structured Walmart-Amazon
5. Structured BeerAdvo-RateBeer
6. Structured Amazon-Google Products
7. Strucutred Fodors-Zagats
8. Dirty DBLP-ACM
9. Dirty DBLP-Scholar
10. Dirty iTunes-Amazon
11. Dirty Walmart-Amazon
12. Textual Abt-Buy
13. Textual CompanyA-CompanyB

Additionally, the repository includes five new benchmark datasets that are drawn from the following databases using a principled approach based on DeepBlocker:

1. Abt-Buy
2. Amazon-Google Products
3. IMDB-TVDB
4. TMDB-TVDB
5. Walmart-Amazon

The datasets are available in six different formats so that they can be processed by the following matching algorithms:

1. EMTransformer
2. GNEM
3. HierMatcher
4. Magellan
5. ZeroER

Dataset

This dataset was created by HitendraVaghela

Contents

Entity Resolution Software Market size was valued at USD 462.05 Million in 2023 and is projected to reach USD 993.92 Million by 2031, growing at a CAGR of 11.56% from 2024 to 2031.

Global Entity Resolution Software Market Overview

The shift toward cloud-based solutions in the entity resolution software market is a defining trend, driven by the growing demand for scalability, flexibility, and cost-efficiency in data management. Cloud-based platforms enable organizations to efficiently manage their data without requiring substantial upfront investments in hardware and infrastructure. This approach offers significant advantages, including scalability, which allows businesses to dynamically adjust resources to meet varying data processing needs, and flexibility, which supports diverse deployment models such as public, private, and hybrid clouds to accommodate specific operational and compliance requirements.

Tough Tables (2T) is a dataset designed to evaluate table annotation approaches on the CEA task.
The dataset is compliant with the data format used in SemTab2019, and it can be used as an additional dataset without any modification. Annotations are based on DBpedia 2016-10.

Note on License: This dataset includes data from the following sources. Refer to each source for license details:
- Wikipedia https://www.wikipedia.org/
- DBpedia http://dbpedia.org/
- SemTab2019 https://doi.org/10.5281/zenodo.3518539
- GeoDatos https://www.geodatos.net
- The Pudding https://pudding.cool/
- Offices.net https://offices.net
- DATA.GOV https://www.data.gov/

THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Entity Resolution for Law Enforcement Market Outlook

According to our latest research, the global market size for Entity Resolution for Law Enforcement reached USD 1.42 billion in 2024. The market is experiencing robust expansion, supported by a CAGR of 14.8% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of USD 4.32 billion. This impressive growth is primarily driven by the increasing need for advanced data analytics and identity management solutions in law enforcement to combat sophisticated criminal activities and enhance operational efficiencies.

The growth of the Entity Resolution for Law Enforcement market is underpinned by the rapid digitalization of law enforcement agencies globally. As agencies transition from traditional paper-based systems to digital platforms, the volume, variety, and velocity of data generated have grown exponentially. This transformation necessitates robust entity resolution solutions capable of accurately identifying, linking, and deduplicating entities across disparate data sources. The proliferation of smart devices, surveillance systems, and interconnected databases has further intensified the demand for advanced software that can process and analyze massive datasets in real time. The market is also benefiting from government initiatives aimed at modernizing public safety infrastructure, which often include investments in advanced data management and analytics platforms.

Another significant driver for the Entity Resolution for Law Enforcement market is the escalating complexity and sophistication of criminal activities. Criminals are increasingly leveraging technology to obscure their identities, create false records, and exploit gaps in law enforcement data systems. This has made traditional investigative methods less effective, pushing agencies to adopt entity resolution solutions that use artificial intelligence, machine learning, and natural language processing to uncover hidden connections and relationships. The integration of these advanced technologies enables law enforcement to detect fraud, analyze intelligence, and solve cases more efficiently. Furthermore, the growing emphasis on data-driven policing and predictive analytics is accelerating the adoption of entity resolution platforms to support proactive crime prevention and resource allocation.

Additionally, the rising concerns around national security, terrorism, and cross-border crimes have compelled federal and intelligence agencies to invest heavily in entity resolution technologies. These solutions are critical for consolidating fragmented data from multiple jurisdictions and sources, enabling agencies to build comprehensive profiles of suspects, organizations, and criminal networks. The ability to accurately resolve entities across complex datasets not only enhances investigative outcomes but also supports intelligence sharing and collaboration between local, national, and international agencies. As data privacy and regulatory compliance become more stringent, entity resolution platforms are evolving to incorporate robust security features and audit trails, further boosting their adoption in the law enforcement sector.

From a regional perspective, North America continues to dominate the Entity Resolution for Law Enforcement market, driven by substantial investments in public safety technologies, a high incidence of cyber and financial crimes, and the presence of leading solution providers. Europe and Asia Pacific are also witnessing significant growth, fueled by increasing government focus on digital transformation and public safety modernization. Emerging economies in Latin America and the Middle East & Africa are gradually adopting entity resolution solutions as part of broader efforts to enhance law enforcement capabilities and address rising crime rates. The regional dynamics are shaped by varying levels of technological maturity, regulatory frameworks, and law enforcement priorities, contributing to a diverse and evolving global market landscape.

Component Analysis

The Component segment of the Entity Resolution for Law Enforcement market is bifurcated into Software and Services. Software solutions represent the backbone of entity resolution, providing the algorithms, analytics engines, and user interfaces necessary for data integration, matching, and deduplication. These platforms are designed to handle

Record linkage is the task of combining records from multiple files which refer to overlapping sets of entities when there is no unique identifying field. In streaming record linkage, files arrive sequentially in time and estimates of links are updated after the arrival of each file. This problem arises in settings such as longitudinal surveys, electronic health records, and online events databases, among others. The challenge in streaming record linkage is to efficiently update parameter estimates as new data arrive. We approach the problem from a Bayesian perspective with estimates calculated from posterior samples of parameters and present methods for updating link estimates after the arrival of a new file that are faster than fitting a joint model with each new data file. In this article, we generalize a two-file Bayesian Fellegi-Sunter model to the multi-file case and propose two methods to perform streaming updates. We examine the effect of prior distribution on the resulting linkage accuracy as well as the computational tradeoffs between the methods when compared to a Gibbs sampler through simulated and real-world survey panel data. We achieve near-equivalent posterior inference at a small fraction of the compute time. Supplementary materials for this article are available online.

Entity Resolution Graph for Investigations Market Outlook

According to our latest research, the global Entity Resolution Graph for Investigations market size stood at USD 2.41 billion in 2024, underlining the sector’s robust presence in the global analytics and investigation ecosystem. The market is anticipated to expand at a compound annual growth rate (CAGR) of 18.2% from 2025 to 2033, reaching a forecasted size of USD 12.26 billion by 2033. This remarkable growth trajectory is primarily driven by the rising need for advanced data analytics, the proliferation of digital fraud, and increasing regulatory scrutiny across industries. As organizations face mounting pressure to manage complex data relationships and uncover hidden connections, the Entity Resolution Graph for Investigations market is poised for significant expansion over the coming decade.

One of the principal growth factors for the Entity Resolution Graph for Investigations market is the escalating volume and complexity of data generated by modern enterprises. As businesses digitize their operations, the data landscape has become fragmented, making it difficult to establish clear relationships between entities such as individuals, organizations, and transactions. Entity resolution graph solutions offer a sophisticated approach to integrating disparate datasets, enabling investigators to identify patterns, detect anomalies, and uncover hidden relationships. This capability is increasingly vital for sectors such as BFSI, government, and healthcare, where the accuracy of entity identification directly impacts risk management, compliance, and investigative outcomes. The integration of artificial intelligence and machine learning algorithms into these solutions further enhances their ability to deliver real-time insights, driving adoption across industries.

Another significant driver is the surge in regulatory requirements and compliance mandates globally. Financial institutions, healthcare providers, and government agencies are under unprecedented pressure to comply with anti-money laundering (AML), know your customer (KYC), and data privacy regulations. Entity resolution graph technology enables these organizations to efficiently reconcile and validate data from multiple sources, ensuring compliance while minimizing manual intervention. The technology’s ability to provide a unified view of entities across vast datasets is critical for timely and accurate reporting, audit readiness, and risk mitigation. As regulatory frameworks continue to evolve and become more stringent, demand for robust entity resolution solutions is expected to intensify, further propelling market growth.

The rise of sophisticated fraud schemes and cyber threats is also fueling demand for entity resolution graph solutions. Fraud detection and risk management applications rely heavily on the ability to correlate seemingly unrelated data points to uncover fraudulent activities. Entity resolution graphs empower organizations to visualize and analyze complex networks of relationships, making it easier to detect fraud rings, insider threats, and other malicious activities. The growing adoption of digital channels in banking, retail, and other sectors has expanded the attack surface for fraudsters, necessitating advanced investigative tools. As organizations invest in strengthening their security postures, the adoption of entity resolution graph technology is set to accelerate, underpinning the market’s sustained growth.

From a regional perspective, North America currently dominates the Entity Resolution Graph for Investigations market, driven by the early adoption of advanced analytics, a strong regulatory environment, and significant investments in digital transformation. However, Asia Pacific is emerging as a high-growth region, fueled by rapid digitization, increasing awareness of data-driven investigations, and expanding regulatory frameworks. Europe also represents a substantial share of the market, with stringent data protection laws and a mature financial services sector contributing to steady demand. As organizations across these regions continue to grapple with complex data challenges and evolving threats, the adoption of entity resolution graph solutions is expected to rise, supporting robust market growth globally.

Radio Station dataset contains around 10K entities of 256d vectors.

Merging datafiles containing information on overlapping sets of entities is a challenging task in the absence of unique identifiers, and is further complicated when some entities are duplicated in the datafiles. Most approaches to this problem have focused on linking two files assumed to be free of duplicates, or on detecting which records in a single file are duplicates. However, it is common in practice to encounter scenarios that fit somewhere in between or beyond these two settings. We propose a Bayesian approach for the general setting of multifile record linkage and duplicate detection. We use a novel partition representation to propose a structured prior for partitions that can incorporate prior information about the data collection processes of the datafiles in a flexible manner, and extend previous models for comparison data to accommodate the multifile setting. We also introduce a family of loss functions to derive Bayes estimates of partitions that allow uncertain portions of the partitions to be left unresolved. The performance of our proposed methodology is explored through extensive simulations. Supplementary materials for this article are available online.

Identity Resolution Market Outlook

According to our latest research, the global identity resolution market size stood at USD 3.6 billion in 2024, reflecting a robust demand for advanced identity management solutions across multiple sectors. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 10.7 billion by 2033. This strong growth trajectory is being fueled by the increasing need for organizations to provide seamless, personalized customer experiences while maintaining rigorous security and compliance standards.

The primary growth driver for the identity resolution market is the exponential surge in digital interactions and data generation. As businesses across industries digitize their operations, they accumulate vast amounts of customer data from diverse sources and touchpoints. This fragmented data landscape creates a pressing need for sophisticated identity resolution solutions that can unify disparate data sets, accurately match identities, and create a single customer view. The proliferation of omnichannel engagement strategies, particularly in retail, BFSI, and healthcare, is making identity resolution an indispensable component of modern customer experience management. Organizations increasingly rely on these solutions to improve personalization, enhance customer engagement, and boost loyalty, directly impacting their bottom line.

Another significant factor propelling the identity resolution market is the escalating threat landscape and the corresponding need for fraud detection and prevention. Cybercriminals are leveraging advanced tactics to exploit identity-related vulnerabilities, compelling organizations to adopt robust identity resolution technologies as a frontline defense. These solutions not only help in detecting and preventing fraudulent activities but also ensure compliance with stringent regulatory frameworks such as GDPR, CCPA, and HIPAA. The growing emphasis on risk and compliance management, especially in highly regulated sectors like BFSI and healthcare, is driving substantial investments in identity resolution platforms and services. This trend is expected to intensify as regulatory bodies worldwide continue to tighten data privacy and security mandates.

Technological advancements in artificial intelligence, machine learning, and big data analytics are fundamentally transforming the identity resolution market. Vendors are integrating these cutting-edge technologies to enhance the accuracy, scalability, and real-time capabilities of their solutions. AI-powered identity resolution platforms can analyze massive volumes of structured and unstructured data, identify complex relationships, and continuously update customer profiles with minimal manual intervention. This not only improves operational efficiency but also empowers organizations to derive actionable insights for targeted marketing, risk mitigation, and strategic decision-making. The ongoing evolution of cloud computing is further accelerating the adoption of identity resolution solutions, enabling scalable, flexible, and cost-effective deployments across organizations of all sizes.

Regionally, North America continues to dominate the identity resolution market, accounting for the largest share in 2024. The regionÂ’s leadership is attributed to early technology adoption, a mature digital ecosystem, and stringent regulatory requirements. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid digital transformation, increasing internet penetration, and a burgeoning e-commerce sector. Europe follows closely, with a strong focus on data privacy and compliance. Meanwhile, the Middle East & Africa and Latin America are witnessing steady growth, supported by rising investments in digital infrastructure and security solutions. The global identity resolution market is thus characterized by a dynamic regional landscape, with each geography presenting unique growth opportunities and challenges.

Entity Resolution Software plays a pivotal role in the identity resolution market, providing the necessary tools to aggregate, match, and unify customer data from various sources. These software solutions are designed to handle the complexities of modern data environments, offering advanced algorithms and AI capabilities that enable real-time identity matching and dedupl

148MM+ total addressable U.S. identity profiles (updated regularly). These identity profiles include full names, addresses, age / DOB, emails, phone numbers, social media urls, education, employment information and more.

This database is available for license ( either full or partial data feed) and can support a variety of B2B and B2C use-cases.

Datasets for Binary Entity Resolution

Source Page : DBLP-Source

In the VLDB 2010 paper [1] we present a first comparative evaluation on the relative match quality and runtime efficiency of entity resolution approaches using challenging real-world match tasks. The evaluation considers existing approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community a state-of-the-art commercial entity resolution implementation is considered. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from online shops are not sufficiently solved with conventional approaches based on the similarity of attribute values.

The dataset consists of 3 tables:

Two lists of academic publications: DBLP and Scholar. 1. DBLP1.csv: Contain no redundant 2. Scholar.csv: Contain messy data with redundant entities. 3. DBLP-Scholar_PerfectMapping.csv: The perfect mapping for entities between both tables.

Workflow:

Provide an approach to find the perfect mapping between entities from the DBLP1 dataset and Scholar dataset to find same documents from DBLP dataset that is in Scholar dataset or duplicated in the Scholar

This data is a benchmark for coreference resolution system evaluation on knowledge graphs. It contains the information about Cruise entities in GeoLink repository.

Identity Resolution AI Market Outlook

As per our latest research, the global Identity Resolution AI market size reached USD 2.12 billion in 2024, reflecting robust expansion driven by the increasing demand for advanced identity management solutions across diverse industries. The market is poised to grow at a healthy CAGR of 19.6% from 2025 to 2033, with the forecasted market size expected to reach USD 10.25 billion by 2033. The primary growth factor for this market is the rapid adoption of artificial intelligence (AI) and machine learning technologies for fraud detection, customer experience enhancement, and regulatory compliance in a landscape marked by escalating digital interactions and cyber threats.

A significant growth driver for the Identity Resolution AI market is the surge in digital transformation initiatives across sectors such as BFSI, healthcare, retail, and government. As organizations accelerate their digital strategies, the volume and complexity of data generated by multiple touchpoints are increasing exponentially. This proliferation of disparate data sources creates challenges in accurately identifying and verifying individuals across platforms. Identity Resolution AI leverages sophisticated algorithms to unify fragmented data, enabling businesses to establish a single, accurate customer view. This capability is critical for preventing identity fraud, streamlining customer onboarding, and delivering personalized experiences, thereby making AI-powered identity resolution an essential investment for organizations aiming to stay competitive in the digital age.

Another key factor fueling market growth is the rising sophistication and frequency of cyberattacks, which have made traditional identity management systems inadequate. Organizations are increasingly recognizing the need for advanced solutions capable of real-time identity verification and anomaly detection. Identity Resolution AI systems utilize machine learning models to detect subtle patterns, flag suspicious activities, and adapt to evolving threat landscapes. This proactive approach to security is particularly vital in sectors like banking and healthcare, where the stakes of data breaches are exceptionally high. Consequently, regulatory bodies are also mandating stricter compliance standards, further propelling the adoption of AI-driven identity resolution solutions that can ensure robust risk management and regulatory adherence.

The expanding scope of personalized marketing and customer engagement strategies is also a pivotal growth catalyst for the Identity Resolution AI market. Enterprises are leveraging AI-powered identity resolution to create holistic customer profiles, enabling targeted marketing, improved customer service, and enhanced loyalty programs. By accurately resolving identities across devices and channels, organizations can deliver seamless omnichannel experiences while maintaining privacy and data security. This ability to balance personalization with compliance is becoming a critical differentiator, especially as consumer expectations for tailored interactions continue to rise alongside concerns about data privacy.

From a regional perspective, North America currently leads the global Identity Resolution AI market, accounting for the largest revenue share in 2024. The region’s dominance is attributed to the early adoption of advanced technologies, a strong presence of key market players, and stringent regulatory frameworks. Europe and Asia Pacific are also witnessing significant growth, with increasing investments in digital infrastructure and heightened awareness of cybersecurity risks. While North America continues to set the pace, Asia Pacific is projected to register the highest CAGR during the forecast period, driven by rapid digitalization in emerging economies and the proliferation of online services.

Component Analysis

The Identity Resolution AI market is segmented by component into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment dominates the market, capturing a substantial share in 2024 due to the increasing deployment of AI-powered platforms that automate and streamline identity resolution processes. These software solutions are designed to ingest, process, and correlate large volumes of data from disparate sources, leveraging advanced algorithms to establish accurate identity matches. The growing demand for scalable, flexible, and easy-to-integrate software solutions is driving continuous innovation among