Demo Data from EDBT 2026 / reviewers in the wild / expert
Klara A. Weiand
dblp:59/6484
· DBLP profile ↗
6ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 4 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 3Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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 |
Information retrieval · 61% Query processing and optimization · 30% Data models and query languages · 9% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge management |
0.1 | 1 | 2010 | visKQWL, a visual renderer for a semantic web query language · WWW 2010 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology › semantic web
semantic wiki |
0.1 | 1 | 2010 | visKQWL, a visual renderer for a semantic web query language · WWW 2010 |
Information retrieval › search engines › web crawling
focused crawling |
0.1 | 1 | 2010 | Not so creepy crawler: easy crawler generation with standard xml queries · WWW 2010 |
Information retrieval › search engines
web crawling |
0.1 | 1 | 2010 | Not so creepy crawler: easy crawler generation with standard xml queries · WWW 2010 |
Query processing and optimization
XML query processing |
0.1 | 1 | 2010 | Not so creepy crawler: easy crawler generation with standard xml queries · WWW 2010 |
Methods — techniques the papers use, named apart from their topics
visual rendering · 0.2XML queries · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | pest: Fast approximate keyword search in semantic data using eigenvector-based term propagation
Klara A. Weiand, Fabian Kneißl, Wojciech lobacz, Tim Furche, François Bry |
Inf. Syst. | 1 |
| 2010 | Flavors of KWQL, a Keyword Query Language for a Semantic Wiki
François Bry, Klara A. Weiand |
SOFSEM | 2 |
| 2010 | Not so creepy crawler: easy crawler generation with standard xml queriesabstractWeb crawlers are increasingly used for focused tasks such as the extraction of data from Wikipedia or the analysis of social networks like last.fm. In these cases, pages are far more uniformly structured than in the general Web and thus crawlers can use the structure of Web pages for more precise data extraction and more expressive analysis. Franziska von dem Bussche, Klara A. Weiand, Benedikt Linse, Tim Furche, François Bry |
WWW | 2 |
| 2010 | visKQWL, a visual renderer for a semantic web query languageabstractKiWi is a semantic Wiki that combines the Wiki philosophy of collaborative content creation with the methods of the Semantic Web in order to enable effective knowledge management. Andreas Hartl, Klara A. Weiand, François Bry |
WWW | 2 |
| 2009 | Web Queries: From a Web of Data to a Semantic Web
François Bry, Tim Furche, Klara A. Weiand |
WISE | 3 |
| 2007 | Formal modelling of L1 and L2 perceptual learning: computational linguistics versus machine learningabstractAbstract ∗ In this paper, we evaluate the adequacy of two widely used machine learning algorithms and a computational linguistic proposal to model L2 perceptual development. The three proposals are, in order, Nearest Neighbor, Naive Bayesian and Stochastic OT and the Gradual Learning Algorithm. We compared the three models ’ outputs to those of Spanish learners of Dutch who were asked to categorize synthetic stimuli as one of the 12 Dutch vowels. The empirical results of the human learners show that L2 learners differ significantly from native listeners, but also that their perceptual spaces tend to become more native-like with L2 proficiency. The results of the simulations show that all three algorithms are able to model listeners ’ data to a certain extent but that Stochastic OT and the Gradual Learning Algorithm, i.e. the linguistic model, best reproduces L1 and L2 data. 1. Paola Escudero, Jelle Kastelein, Klara A. Weiand, R. J. J. H. van Son |
INTERSPEECH | 3 |