Ulf Leser focuses on Artificial intelligence, Information retrieval, Data mining, Text mining and Query language. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Natural language processing. In the subject of general Information retrieval, his work in Semantic search is often linked to Scientific literature, thereby combining diverse domains of study.
Ulf Leser has researched Data mining in several fields, including Functional annotation, Protein function prediction, UniProt and Protein–protein interaction. His studies deal with areas such as Information extraction, World Wide Web, Programming paradigm and Data warehouse as well as Text mining. His Query language research includes elements of Query expansion, Web query classification, RDF query language, Sargable and Query optimization.
Ulf Leser spends much of his time researching Artificial intelligence, Information retrieval, Data mining, Natural language processing and Data science. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning, Named-entity recognition and Pattern recognition. His Information retrieval research is multidisciplinary, relying on both Annotation and Software.
Ulf Leser works mostly in the field of Data mining, limiting it down to concerns involving Set and, occasionally, Theoretical computer science. His Natural language processing research incorporates themes from Query language and Normalization. He has included themes like Domain, Data integration and Workflow in his Data science study.
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Artificial neural network, Cancer research and Named-entity recognition. He studies Artificial intelligence, namely Deep learning. As part of the same scientific family, Ulf Leser usually focuses on Natural language processing, concentrating on German and intersecting with Clef, Information extraction and Consistency.
His Named-entity recognition research is multidisciplinary, incorporating perspectives in Cover, Annotation and State. Ulf Leser interconnects Computation, Data mining, Constraint and Heuristic in the investigation of issues within Set. Biomedical information and Information retrieval are two areas of study in which Ulf Leser engages in interdisciplinary work.
His primary areas of study are Artificial intelligence, Natural language processing, Medical physics, Precision oncology and Named-entity recognition. The Artificial intelligence study combines topics in areas such as Batch processing, Machine learning and Scheduling. His research in Natural language processing intersects with topics in Artificial neural network, Clef, German and Ambiguity.
His Medical physics research is multidisciplinary, incorporating elements of Document retrieval, Document classification, Search engine and MEDLINE. His work in Named-entity recognition tackles topics such as Language model which are related to areas like Set. His research investigates the connection with Set and areas like Algorithm which intersect with concerns in Use case.
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Querying distributed RDF data sources with SPARQL
Bastian Quilitz;Ulf Leser.
european semantic web conference (2008)
The Stratosphere platform for big data analytics
Alexander Alexandrov;Rico Bergmann;Stephan Ewen;Johann-Christoph Freytag.
very large data bases (2014)
Deep learning with word embeddings improves biomedical named entity recognition.
Maryam Habibi;Leon Weber;Mariana L. Neves;David Luis Wiegandt.
Fast and practical indexing and querying of very large graphs
Silke Trißl;Ulf Leser.
international conference on management of data (2007)
Quality-driven Integration of Heterogenous Information Systems
Felix Naumann;Ulf Leser;Johann Christoph Freytag.
very large data bases (1999)
ChemSpot: a hybrid system for chemical named entity recognition
Tim Rocktäschel;Michael Weidlich;Ulf Leser.
Federated Information Systems: Concepts, Terminology and Architectures
Susanne Busse;Ralf-Detlef Kutsche;Ulf Leser;Herbert Weber.
What makes a gene name? Named entity recognition in the biomedical literature.
Ulf Leser;Jörg Hakenberg.
Briefings in Bioinformatics (2005)
A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature
Domonkos Tikk;Philippe E. Thomas;Peter Palaga;Jörg Hakenberg.
PLOS Computational Biology (2010)
Completeness of integrated information sources
Felix Naumann;Johann-Christoph Freytag;Ulf Leser.
cooperative information systems (2004)
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