CompanyName2Vec: Company Entity Matching Based on Job Ads
This addresses the data integration challenge for organizations dealing with disparate sources, though it is incremental as it applies existing entity matching techniques to a specific domain.
The paper tackles the problem of company entity matching by introducing CompanyName2Vec, a neural network model that learns company name semantics from job ad data, achieving an average success rate of 89.3% and outperforming other methods.
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%.