EDBT 2026 Demo / reviewers in the wild / expert
Thomas Mahoney
dblp:150/2354
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1Theory of computation · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
2 papers |
Data mining · 64% Knowledge graphs · 19% Web and social media mining · 17% | |
| Artificial intelligence
1 paper |
Information extraction and text analysis · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs
knowledge base linking |
0.1 | 1 | 2018 | Lessons Learned from Developing and Deploying a Large-Scale Employer Name Normalization System for Online Recruitment · KDD 2018 |
Web and social media mining
online recruitment |
0.1 | 1 | 2017 | Large-Scale Occupational Skills Normalization for Online Recruitment · AAAI 2017 |
Methods — techniques the papers use, named apart from their topics
machine learning · 0.3entity linking · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Lessons Learned from Developing and Deploying a Large-Scale Employer Name Normalization System for Online RecruitmentabstractEmployer name normalization, or linking employer names in job postings or resumes to entities in an employer knowledge base (KB), is important for many downstream applications in the online recruitment domain. Key challenges for employer name normalization include handling employer names from both job postings and resumes, leveraging the corresponding location and URL context, and handling name variations and duplicates in the KB. In this paper, we describe the CompanyDepot system developed at CareerBuilder, which uses machine learning techniques to address these challenges. We discuss the main challenges and share our lessons learned in deployment, maintenance, and utilization of the system over the past two years. We also share several examples of how the system has been used in applications at CareerBuilder to deliver value to end customers. Qiaoling Liu, Josh Chao, Thomas Mahoney, Alan Chern, Chris Min, Faizan Javed, Valentin Jijkoun |
KDD | 3 |
| 2018 | Dynamic coloring parameters for graphs with given genus
Sarah Loeb, Thomas Mahoney, Benjamin Reiniger, Jennifer Wise |
Discret. Appl. Math. | 2 |
| 2017 | Large-Scale Occupational Skills Normalization for Online Recruitment
Faizan Javed, Phuong Hoang, Thomas Mahoney, Matt McNair |
AAAI | 3 |