Searching for authors named "Henrik Nottelmann" – sorted by Relevance.
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MIND: An architecture for multimedia information retrieval in federated digital libraries
- libraries Henrik Nottelmann, Norbert Fuhr Department of Computer Science University of Dortmund, Germany
- Cited by 4 (0 self) – Add To MetaCart
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From uncertain inference to probability of relevance for advanced IR applications
- , Rijsbergen [18] introduced a new paradigm for probabilistic information retrieval, namely 2 Henrik Nottelmann
- Cited by 7 (4 self) – Add To MetaCart
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From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications
- From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications Henrik
- Cited by 5 (2 self) – Add To MetaCart
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Combining CORI and the decision-theoretic approach for advanced resource selection
- the number of relevant documents in the complete DL, and uses 2 Henrik Nottelmann and Norbert Fuhr a recall
- Cited by 6 (0 self) – Add To MetaCart
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Comparing different architectures for query routing in peer-to-peer networks
- Comparing different architectures for query routing in peer-to-peer networks Henrik Nottelmann
- Cited by 7 (0 self) – Add To MetaCart
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A probabilistic, logic-based framework for automated Web directory alignment
- A Probabilistic, Logic-based Framework for Automated Web Directory Alignment Henrik Nottelmann 1
- Cited by 3 (1 self) – Add To MetaCart
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Predicting Retrieval Quality for Resource Selection in Distributed Information Retrieval
- Henrik Nottelmann nottelmann@(email omitted); In a federated digital library system, it is too
- Cited by 1 (1 self) – Add To MetaCart
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Learning probabilistic Datalog rules for information classification and transformation
- Henrik Nottelmann nottelmann@(email omitted); Probabilistic Datalog is a combination of classical
- Cited by 4 (2 self) – Add To MetaCart
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sPLMap: A probabilistic approach to schema matching
- sPLMap: A probabilistic approach to schema matching Henrik Nottelmann 1 and Umberto Straccia 2 1
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Information Retrieval and Machine Learning for
- ABSTRACT Information Retrieval and Machine Learning for Probabilistic Schema Matching ∗ Henrik
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