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EDBT 2026 Demo / reviewers in the wild / expert

Lars Littig

dblp:84/6306 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 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
Web and social media mining · 50% Data mining · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › text mining › text classification
web content classification		 0.112007
Classifying web sites · WWW 2007

Methods — techniques the papers use, named apart from their topics

text classification · 0.1structural feature extraction · 0.1
YearPublicationVenuePosition
2007 Classifying web sites
abstract
In this paper, we present a novel method for the classification of Web sites. This method exploits both structure and content of Web sites in order to discern their functionality. It allows for distinguishing between eight of the most relevant functional classes of Web sites. We show that a pre-classification of Web sites utilizing structural properties considerably improves a subsequent textual classification with standard techniques. We evaluate this approach on a dataset comprising more than 16,000 Web sites with about 20 million crawled and 100 million known Web pages. Our approach achieves an accuracy of 92% for the coarse-grained classification of these Web sites.
Christoph Lindemann, Lars Littig
 WWW2