What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Information retrieval
His scientific interests lie mostly in Information retrieval, Probabilistic logic, Ranking, Artificial intelligence and Search engine indexing.
His study on Information retrieval is mostly dedicated to connecting different topics, such as XML retrieval.
His Divergence-from-randomness model study in the realm of Probabilistic logic interacts with subjects such as String metric.
The study incorporates disciplines such as XML, Data mining and Pattern recognition in addition to Ranking.
His Artificial intelligence research includes themes of Machine learning and Natural language processing.
The various areas that Norbert Fuhr examines in his Search engine indexing study include Rule-based system and Relevance feedback.
His most cited work include:
- A probabilistic relational algebra for the integration of information retrieval and database systems (376 citations)
- XIRQL: a query language for information retrieval in XML documents (312 citations)
- Probabilistic models in information retrieval (285 citations)
What are the main themes of his work throughout his whole career to date?
Norbert Fuhr focuses on Information retrieval, Probabilistic logic, World Wide Web, Relevance and XML.
His research on Information retrieval often connects related topics like XML retrieval.
His Probabilistic logic research incorporates elements of Datalog and Data mining.
His World Wide Web research is multidisciplinary, relying on both User interface and Multimedia.
His Relevance research is multidisciplinary, incorporating perspectives in Document retrieval and Vector space model.
His studies deal with areas such as Test and Relevance feedback as well as XML.
He most often published in these fields:
- Information retrieval (57.03%)
- Probabilistic logic (18.25%)
- World Wide Web (15.97%)
What were the highlights of his more recent work (between 2014-2021)?
- Information retrieval (57.03%)
- Reproducibility (3.04%)
- Artificial intelligence (11.79%)
In recent papers he was focusing on the following fields of study:
Norbert Fuhr mainly focuses on Information retrieval, Reproducibility, Artificial intelligence, Natural language processing and Data science.
He is interested in Ranking, which is a field of Information retrieval.
Norbert Fuhr works mostly in the field of Reproducibility, limiting it down to topics relating to Data mining and, in certain cases, Face and Field, as a part of the same area of interest.
His research integrates issues of Machine learning and Markov model in his study of Artificial intelligence.
His research investigates the connection between Natural language processing and topics such as Recommender system that intersect with problems in Predictive modelling.
The study incorporates disciplines such as Test and Context in addition to Data science.
Between 2014 and 2021, his most popular works were:
- Some Common Mistakes In IR Evaluation, And How They Can Be Avoided (56 citations)
- Increasing Reproducibility in IR: Findings from the Dagstuhl Seminar on "Reproducibility of Data-Oriented Experiments in e-Science" (51 citations)
- Can Rumour Stance Alone Predict Veracity (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Statistics
The scientist’s investigation covers issues in Reproducibility, Information retrieval, Information system, Data science and Recommender system.
He has researched Information retrieval in several fields, including Session, Information needs and Result set.
Norbert Fuhr undertakes interdisciplinary study in the fields of Data science and e-Science through his research.
His Recommender system research includes elements of Predictive modelling and Performance prediction.
His work carried out in the field of Predictive modelling brings together such families of science as Artificial intelligence and Natural language processing.
Markov chain connects with themes related to Data mining in his study.
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