Demo Data from EDBT 2026 / reviewers in the wild / expert
Petr Prokhorenkov
dblp:175/4806
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—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.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 50% Web and social media mining · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › document retrieval
temporal information retrieval |
0.2 | 1 | 2016 | Publication Date Prediction through Reverse Engineering of the Web · WSDM 2016 |
Methods — techniques the papers use, named apart from their topics
probabilistic web graph models · 0.2link-based dating · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Publication Date Prediction through Reverse Engineering of the WebabstractIn this paper, we focus on one of the most challenging tasks in temporal information retrieval: detection of a web page publication date. The natural approach to this problem is to find the publication date in the HTML body of a page. However, there are two fundamental problems with this approach. First, not all web pages contain the publication dates in their texts. Second, it is hard to distinguish the publication date among all the dates found in the page's text. The approach we suggest in this paper supplements methods of date extraction from the page's text with novel link-based methods of dating. Some of our link-based methods are based on a probabilistic model of the Web graph structure evolution, which relies on the publication dates of web pages as on its parameters. We use this model to estimate the publication dates of web pages: based only on the link structure currently observed, we perform a ``reverse engineering'' to reveal the whole process of the Web's evolution. Liudmila Ostroumova, Petr Prokhorenkov, Egor Samosvat, Pavel Serdyukov |
WSDM | 2 |