Demo Data from VLDB 2026 / reviewers in the wild / expert
Pei-yun Hseuh
dblp:67/9989
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% | |
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational social science and digital humanities › scientometrics
patent analysis |
0.1 | 1 | 2011 | Latent graphical models for quantifying and predicting patent quality · KDD 2011 |
Data mining
pattern mining |
0.1 | 1 | 2011 | Latent graphical models for quantifying and predicting patent quality · KDD 2011 |
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models
latent variable graphical model |
0.0 | 1 | 2011 | Latent graphical models for quantifying and predicting patent quality · KDD 2011 |
Methods — techniques the papers use, named apart from their topics
natural language processing · 0.4latent graphical model · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Latent graphical models for quantifying and predicting patent qualityabstractThe number of patents filed each year has increased dramatically in recent years, raising concerns that patents of questionable validity are restricting the issuance of truly innovative patents. For this reason, there is a strong demand to develop an objective model to quantify patent quality and characterize the attributes that lead to higher-quality patents. In this paper, we develop a latent graphical model to infer patent quality from related measurements. In addition, we extract advanced lexical features via natural language processing techniques to capture the quality measures such as clarity of claims, originality, and importance of cited prior art. We demonstrate the effectiveness of our approach by validating its predictions with previous court decisions of litigated patents. Yan Liu 0002, Pei-yun Hseuh, Rick Lawrence, Steve Meliksetian, Claudia Perlich, Alejandro Veen |
KDD | 2 |