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Fabian Müller 0002

dblp:71/5071-2 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1Theory of computation · 1 · 1 first-authorApplied, 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
Information retrieval · 50% Web and social media mining · 50%

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

TopicWeightPapersLastEvidence papers
Information retrieval › document retrieval › domain-specific retrieval
mathematical information retrieval		 0.412020
Discovering Mathematical Objects of Interest - A Study of Mathematical Notations · WWW 2020

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

corpus analysis · 0.4
YearPublicationVenuePosition
2020 Discovering Mathematical Objects of Interest - A Study of Mathematical Notations
abstract
Mathematical notation, i.e., the writing system used to communicate concepts in mathematics, encodes valuable information for a variety of information search and retrieval systems. Yet, mathematical notations remain mostly unutilized by today’s systems. In this paper, we present the first in-depth study on the distributions of mathematical notation in two large scientific corpora: the open access arXiv (2.5B mathematical objects) and the mathematical reviewing service for pure and applied mathematics zbMATH (61M mathematical objects). Our study lays a foundation for future research projects on mathematical information retrieval for large scientific corpora. Further, we demonstrate the relevance of our results to a variety of use-cases. For example, to assist semantic extraction systems, to improve scientific search engines, and to facilitate specialized math recommendation systems.
André Greiner-Petter, Moritz Schubotz, Fabian Müller 0002, Corinna Breitinger, Howard S. Cohl, Akiko Aizawa, Bela Gipp
 WWW3
2016 Progress of Self-Archiving Within the DML Corpus, with a View Toward Community Dynamics
Fabian Müller 0002, Olaf Teschke
 CICM1