H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 32 Citations 11,266 104 World Ranking 7408 National Ranking 368

Overview

What is he best known for?

The fields of study he is best known for:

  • Programming language
  • Artificial intelligence
  • Database

Georg Lausen spends much of his time researching Information retrieval, SPARQL, Programming language, Theoretical computer science and Database. The Recommender system research Georg Lausen does as part of his general Information retrieval study is frequently linked to other disciplines of science, such as Order, Metric, User satisfaction and Diversification, therefore creating a link between diverse domains of science. SPARQL is a subfield of RDF that Georg Lausen investigates.

As part of one scientific family, he deals mainly with the area of RDF, narrowing it down to issues related to the RDF query language, and often SQL. Many of his research projects under Programming language are closely connected to F-logic with F-logic, tying the diverse disciplines of science together. The Object-oriented programming study combines topics in areas such as Information integration, Programming style, Fifth-generation programming language, Programming language theory and Semantics.

His most cited work include:

  • Logical foundations of object-oriented and frame-based languages (1363 citations)
  • Improving recommendation lists through topic diversification (1332 citations)
  • F-logic: a higher-order language for reasoning about objects, inheritance, and scheme (352 citations)

What are the main themes of his work throughout his whole career to date?

Georg Lausen mainly focuses on Information retrieval, SPARQL, RDF, Programming language and Theoretical computer science. His Information retrieval research is mostly focused on the topic Recommender system. His SPARQL research includes themes of Scalability, SQL, Database and Benchmark.

The RDF study which covers Relational database that intersects with Data integrity. The concepts of his Programming language study are interwoven with issues in Query language and Semantics. In his works, Georg Lausen conducts interdisciplinary research on Theoretical computer science and F-logic.

He most often published in these fields:

  • Information retrieval (29.73%)
  • SPARQL (20.95%)
  • RDF (20.95%)

What were the highlights of his more recent work (between 2011-2019)?

  • RDF (20.95%)
  • SPARQL (20.95%)
  • Information retrieval (29.73%)

In recent papers he was focusing on the following fields of study:

Georg Lausen mostly deals with RDF, SPARQL, Information retrieval, Recommender system and RDF Schema. He has researched RDF in several fields, including Linked data, Relational database, Theoretical computer science and Computer cluster. His research integrates issues of Conjunctive query and Graph in his study of Theoretical computer science.

His SPARQL research is multidisciplinary, incorporating elements of Scalability, SQL, Database and RDF query language. His Information retrieval research incorporates elements of Syntax and World Wide Web. His Recommender system study combines topics from a wide range of disciplines, such as Domain and Operations research.

Between 2011 and 2019, his most popular works were:

  • S2RDF: RDF querying with SPARQL on spark (105 citations)
  • Sempala: Interactive SPARQL Query Processing on Hadoop (48 citations)
  • S2X: Graph-Parallel Querying of RDF with GraphX (40 citations)

In his most recent research, the most cited papers focused on:

  • Programming language
  • Database
  • Artificial intelligence

His primary areas of investigation include SPARQL, RDF, Database, RDF Schema and Scalability. His studies deal with areas such as Recommender system and MovieLens as well as SPARQL. His RDF research is multidisciplinary, relying on both Linked data, Theoretical computer science and Data model.

His research in Database intersects with topics in Rdf graph, RDF query language and Big data. His study on RDF Schema is covered under Information retrieval. The study of Information retrieval is intertwined with the study of World Wide Web in a number of ways.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Logical foundations of object-oriented and frame-based languages

Michael Kifer;Georg Lausen;James Wu.
Journal of the ACM (1995)

2489 Citations

Improving recommendation lists through topic diversification

Cai-Nicolas Ziegler;Sean M. McNee;Joseph A. Konstan;Georg Lausen.
the web conference (2005)

1951 Citations

F-logic: a higher-order language for reasoning about objects, inheritance, and scheme

Michael Kifer;Georg Lausen.
international conference on management of data (1989)

641 Citations

SP^2Bench: A SPARQL Performance Benchmark

Michael Schmidt;Thomas Hornung;Georg Lausen;Christoph Pinkel.
international conference on data engineering (2009)

551 Citations

Propagation Models for Trust and Distrust in Social Networks

Cai-Nicolas Ziegler;Georg Lausen.
Information Systems Frontiers (2005)

463 Citations

Spreading activation models for trust propagation

C.-N. Ziegler;G. Lausen.
ieee international conference on e technology e commerce and e service (2004)

413 Citations

Foundations of SPARQL query optimization

Michael Schmidt;Michael Meier;Georg Lausen.
international conference on database theory (2010)

361 Citations

ViPER: augmenting automatic information extraction with visual perceptions

Kai Simon;Georg Lausen.
conference on information and knowledge management (2005)

297 Citations

Managing semistructured data with florid: a deductive object-oriented perspective

Bertram Ludäscher;Rainer Himmeröder;Georg Lausen;Wolfgang May.
Information Systems (1998)

291 Citations

Taxonomy-driven computation of product recommendations

Cai-Nicolas Ziegler;Georg Lausen;Lars Schmidt-Thieme.
conference on information and knowledge management (2004)

268 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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