H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 77 Citations 21,355 406 World Ranking 537 National Ranking 21

Research.com Recognitions

Awards & Achievements

2010 - Member of Academia Europaea

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • The Internet
  • World Wide Web

His primary scientific interests are in World Wide Web, Information retrieval, Semantic Web, Data mining and Metadata. In the subject of general World Wide Web, his work in Personalization, Web navigation, Social media and User profile is often linked to Email spoofing, thereby combining diverse domains of study. His Information retrieval research incorporates elements of Annotation and Web page.

His studies in Semantic Web integrate themes in fields like Ontology, Access control, Knowledge management and Data Web. His Data mining study combines topics from a wide range of disciplines, such as Machine learning, Cluster analysis, Artificial intelligence and Set. His Metadata research is multidisciplinary, incorporating perspectives in Semantics, The Internet, RDF and PageRank.

His most cited work include:

  • EDUTELLA: a P2P networking infrastructure based on RDF (765 citations)
  • The Adaptive Web (617 citations)
  • The adaptive web: methods and strategies of web personalization (513 citations)

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

The scientist’s investigation covers issues in World Wide Web, Information retrieval, Metadata, Artificial intelligence and Data mining. His research on World Wide Web frequently links to adjacent areas such as Multimedia. Many of his studies on Information retrieval involve topics that are commonly interrelated, such as Web page.

Wolfgang Nejdl has researched Metadata in several fields, including XML and RDF. He combines subjects such as Machine learning and Natural language processing with his study of Artificial intelligence. His study looks at the relationship between Semantic Web and fields such as Data Web, as well as how they intersect with chemical problems.

He most often published in these fields:

  • World Wide Web (34.14%)
  • Information retrieval (27.12%)
  • Metadata (15.50%)

What were the highlights of his more recent work (between 2012-2021)?

  • World Wide Web (34.14%)
  • Information retrieval (27.12%)
  • Artificial intelligence (12.11%)

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

Wolfgang Nejdl spends much of his time researching World Wide Web, Information retrieval, Artificial intelligence, Data mining and Machine learning. World Wide Web is often connected to WAR in his work. His Information retrieval study combines topics in areas such as Ranking and Web page.

His studies deal with areas such as Pattern recognition and Natural language processing as well as Artificial intelligence. His Data mining research includes themes of Graph, Recommender system, Set, Cluster analysis and Exploit. The concepts of his Machine learning study are interwoven with issues in Crowdsourcing and Task.

Between 2012 and 2021, his most popular works were:

  • Introduction to the special section on twitter and microblogging services (138 citations)
  • A Blocking Framework for Entity Resolution in Highly Heterogeneous Information Spaces (92 citations)
  • Meta-Blocking: Taking Entity Resolutionto the Next Level (80 citations)

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

  • Artificial intelligence
  • The Internet
  • World Wide Web

Wolfgang Nejdl focuses on World Wide Web, Information retrieval, Data mining, Artificial intelligence and Graph. His World Wide Web research includes elements of WAR and Workflow. His research integrates issues of Ranking and Event in his study of Information retrieval.

His study in Data mining is interdisciplinary in nature, drawing from both Set and Data set. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Focus. His Graph study incorporates themes from Web service, Personalization, Categorization, Crowds and Proof of concept.

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

EDUTELLA: a P2P networking infrastructure based on RDF

Wolfgang Nejdl;Boris Wolf;Changtao Qu;Stefan Decker.
the web conference (2002)

1212 Citations

The adaptive web: methods and strategies of web personalization

Peter Brusilovsky;Alfred Kobsa;Wolfgang Nejdl.
(2007)

870 Citations

The Adaptive Web

Peter Brusilovsky;Alfred Kobsa;Wolfgang Nejdl.
(2008)

599 Citations

Boilerplate detection using shallow text features

Christian Kohlschütter;Peter Fankhauser;Wolfgang Nejdl.
web search and data mining (2010)

564 Citations

Latent dirichlet allocation for tag recommendation

Ralf Krestel;Peter Fankhauser;Wolfgang Nejdl.
conference on recommender systems (2009)

541 Citations

Super-peer-based routing and clustering strategies for RDF-based peer-to-peer networks

Wolfgang Nejdl;Martin Wolpers;Wolf Siberski;Christoph Schmitz.
the web conference (2003)

460 Citations

Can all tags be used for search

Kerstin Bischoff;Claudiu S. Firan;Wolfgang Nejdl;Raluca Paiu.
conference on information and knowledge management (2008)

426 Citations

Personalization in distributed e-learning environments

Peter Dolog;Nicola Henze;Wolfgang Nejdl;Michael Sintek.
the web conference (2004)

424 Citations

Using ODP metadata to personalize search

Paul Alexandru Chirita;Wolfgang Nejdl;Raluca Paiu;Christian Kohlschütter.
international acm sigir conference on research and development in information retrieval (2005)

388 Citations

Personalized query expansion for the web

Paul Alexandru Chirita;Claudiu S. Firan;Wolfgang Nejdl.
international acm sigir conference on research and development in information retrieval (2007)

382 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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