H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 4,758 144 World Ranking 8878 National Ranking 426

Overview

What is he best known for?

The fields of study he is best known for:

  • Database
  • Artificial intelligence
  • XML

Ralf Schenkel mainly investigates Information retrieval, XML, Data mining, Ranking and Query expansion. His work investigates the relationship between Information retrieval and topics such as XPath that intersect with problems in Dynamic query, XML retrieval, Document Structure Description and Ontology. His XML study combines topics in areas such as Ontology, WordNet and Annotation.

His research in Data mining intersects with topics in Probabilistic logic, Terabyte, Scheduling and Pruning. His Ranking research is multidisciplinary, relying on both Ranking, World Wide Web and Graph. His work focuses on many connections between Query expansion and other disciplines, such as Web query classification, that overlap with his field of interest in Query optimization, Sargable and Query language.

His most cited work include:

  • FedX: optimization techniques for federated query processing on linked data (254 citations)
  • Top-k query evaluation with probabilistic guarantees (231 citations)
  • Efficient top-k querying over social-tagging networks (141 citations)

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

His primary areas of investigation include Information retrieval, World Wide Web, XML, Query expansion and Database. His Information retrieval research is multidisciplinary, incorporating elements of XML retrieval and Task. He focuses mostly in the field of XML, narrowing it down to matters related to Index and, in some cases, Path expression.

His research integrates issues of Query language, Data mining, Query optimization and Web search query, Web query classification in his study of Query expansion. His studies in Query optimization integrate themes in fields like RDF query language and Sargable. His work in Ranking covers topics such as Ranking which are related to areas like Search engine indexing.

He most often published in these fields:

  • Information retrieval (63.38%)
  • World Wide Web (18.78%)
  • XML (16.90%)

What were the highlights of his more recent work (between 2015-2021)?

  • Information retrieval (63.38%)
  • Ranking (12.21%)
  • Digital library (4.69%)

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

Information retrieval, Ranking, Digital library, Argument and Metadata are his primary areas of study. His Information retrieval research incorporates elements of Web API and Task. Ralf Schenkel has included themes like Argument quality, Query by Example and Search engine indexing in his Ranking study.

His Argument study also includes

  • Similarity which connect with Weak entity and Entity linking,
  • Field that intertwine with fields like Persuasion, Probabilistic logic and Cluster analysis,
  • Premise that intertwine with fields like Annotation and Term. His Metadata study incorporates themes from Query language, Scheduling, SQL and Information access. Ralf Schenkel interconnects Graphical user interface and World Wide Web in the investigation of issues within SQL.

Between 2015 and 2021, his most popular works were:

  • QBEES: query-by-example entity search in semantic knowledge graphs based on maximal aspects, diversity-awareness and relaxation (11 citations)
  • OXpath-based data acquisition for dblp (8 citations)
  • A Systematic Comparison of Methods for Finding Good Premises for Claims (7 citations)

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

  • Database
  • Artificial intelligence
  • Operating system

His primary scientific interests are in Information retrieval, Premise, Metadata, Digital library and Argument. Ralf Schenkel studied Information retrieval and Task that intersect with Artificial intelligence. His work carried out in the field of Premise brings together such families of science as German, Annotation, Knowledge representation and reasoning, Argumentative and Natural language.

His studies deal with areas such as Scheduling and Maintenance engineering as well as Metadata. His Argument research is multidisciplinary, incorporating perspectives in Field, Probabilistic logic, Persuasion and Cluster analysis. His research in Persuasion focuses on subjects like Similarity, which are connected to Natural language processing, Query by Example, Entity linking and Weak entity.

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.

Best Publications

FedX: optimization techniques for federated query processing on linked data

Andreas Schwarte;Peter Haase;Katja Hose;Ralf Schenkel.
international semantic web conference (2011)

387 Citations

Top-k query evaluation with probabilistic guarantees

Martin Theobald;Gerhard Weikum;Ralf Schenkel.
very large data bases (2004)

312 Citations

Efficient top-k querying over social-tagging networks

Ralf Schenkel;Tom Crecelius;Mouna Kacimi;Sebastian Michel.
international acm sigir conference on research and development in information retrieval (2008)

205 Citations

An efficient and versatile query engine for TopX search

Martin Theobald;Ralf Schenkel;Gerhard Weikum.
very large data bases (2005)

191 Citations

HOPI: An Efficient Connection Index for Complex XML Document Collections

Ralf Schenkel;Anja Theobald;Gerhard Weikum.
extending database technology (2004)

190 Citations

IO-Top-k: index-access optimized top-k query processing

Holger Bast;Debapriyo Majumdar;Ralf Schenkel;Martin Theobald.
very large data bases (2006)

158 Citations

The SphereSearch engine for unified ranked retrieval of heterogeneous XML and web documents

Jens Graupmann;Ralf Schenkel;Gerhard Weikum.
very large data bases (2005)

153 Citations

YAWN: A Semantically Annotated Wikipedia XML Corpus

Ralf Schenkel;Fabian M. Suchanek;Gjergji Kasneci.
Untitled Event (2007)

143 Citations

TopX: efficient and versatile top-k query processing for semistructured data

Martin Theobald;Holger Bast;Debapriyo Majumdar;Ralf Schenkel.
very large data bases (2008)

134 Citations

RankReduce - processing K-nearest Neighbor Queries on Top of MapReduce

Aleksandar Stupar;Sebastian Michel;Ralf Schenkel.
large scale distributed systems for information retrieval (2010)

132 Citations

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