CompanyName2Vec: Company Entity Matching Based on Job Ads

TL;DR

CompanyName2Vec is a neural network-based method that effectively matches company entities from job ads by learning company name semantics, achieving high accuracy without additional company information.

Contribution

The paper introduces CompanyName2Vec, a novel neural network algorithm for company entity matching that outperforms existing methods using only company names from job ads.

Findings

Achieves an average success rate of 89.3% in company entity matching.

Outperforms other evaluated methods on real-world data.

Demonstrates effectiveness of name semantics learning for entity matching.

Abstract

Entity Matching is an essential part of all real-world systems that take in structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and integration processes require completion before any data analytics, or further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights, supports data visualization, and guides further analytic approaches to the data. Here, we focus on organization entity matching. We introduce CompanyName2Vec, a novel algorithm to solve company entity matching (CEM) using a neural network model to learn company name semantics from a job ad corpus, without relying on any information on the matched company besides its name. Based on a real-world data, we show that CompanyName2Vec outperforms other evaluated methods and solves the CEM challenge with an average success rate of 89.3%.