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Displaying 1-20 out of 20 total
Semantic Traffic-Aware Routing Using the LarKC Platform
Found in: IEEE Internet Computing
By Emanuele Della Valle,Irene Celino,Daniele Dell'Aglio,Ralph Grothmann,Florian Steinke,Volker Tresp
Issue Date:November 2011
pp. 15-23
The popularity of location-based services and automotive navigation systems calls for a new generation of intelligent solutions to support users in mobility. This article presents a traffic-aware semantic routing service for mobile users based on the Large...
 
Modeling and Learning Context-Aware Recommendation Scenarios Using Tensor Decomposition
Found in: Social Network Analysis and Mining, International Conference on Advances in
By Hendrik Wermser, Achim Rettinger, Volker Tresp
Issue Date:July 2011
pp. 137-144
The task of recommending items, like movies, to users is a core feature of many social networks. Standard approaches either use item or user similarity to suggest the next items users might be interested in. Recently, multivariate models like matrix factor...
 
Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics
Found in: IEEE Intelligent Systems
By Davide Barbieri,Daniele Braga,Stefano Ceri,Emanuele Della Valle,Yi Huang,Volker Tresp,Achim Rettinger,Hendrik Wermser
Issue Date:November 2010
pp. 32-41
A combined approach of deductive and inductive reasoning can leverage the clear separation between the evolving (streaming) and static parts of online knowledge at conceptual and technological levels.
 
Hierarchical Bayesian Models for Collaborative Tagging Systems
Found in: Data Mining, IEEE International Conference on
By Markus Bundschus, Shipeng Yu, Volker Tresp, Achim Rettinger, Mathaeus Dejori, Hans-Peter Kriegel
Issue Date:December 2009
pp. 728-733
Collaborative tagging systems with user generated content have become a fundamental element of websites such as Delicious, Flickr or CiteULike. By sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for d...
 
Towards LarKC: A Platform for Web-Scale Reasoning
Found in: International Conference on Semantic Computing
By Dieter Fensel, Frank van Harmelen, Bo Andersson, Paul Brennan, Hamish Cunningham, Emanuele Della Valle, Florian Fischer, Zhisheng Huang, Atanas Kiryakov, Tony Kyung-il Lee, Lael Schooler, Volker Tresp, Stefan Wesner, Michael Witbrock, Ning Zhong
Issue Date:August 2008
pp. 524-529
Current Semantic Web reasoning systems do not scale to the requirements of their hottest applications, such as analyzing data from millions of mobile devices, dealing with terabytes of scientific data, and content management in enterprises with thousands o...
 
Multi-Output Regularized Feature Projection
Found in: IEEE Transactions on Knowledge and Data Engineering
By Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel
Issue Date:December 2006
pp. 1600-1613
Dimensionality reduction by feature projection is widely used in pattern recognition, information retrieval, and statistics. When there are some outputs available (e.g., regression values or classification results), it is often beneficial to consider super...
 
Hierarchy-Regularized Latent Semantic Indexing
Found in: Data Mining, IEEE International Conference on
By Yi Huang, Kai Yu, Matthias Schubert, Shipeng Yu, Volker Tresp, Hans-Peter Kriegel
Issue Date:November 2005
pp. 178-185
Organizing textual documents into a hierarchical taxonomy is a common practice in knowledge management. Beside textual features, the hierarchical structure of directories reflect additional and important knowledge annotated by experts. It is generally desi...
 
Multi-Output Regularized Projection
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Kai Yu, Shipeng Yu, Volker Tresp
Issue Date:June 2005
pp. 597-602
Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is ...
 
Probabilistic Memory-Based Collaborative Filtering
Found in: IEEE Transactions on Knowledge and Data Engineering
By Kai Yu, Anton Schwaighofer, Volker Tresp, Xiaowei Xu, Hans-Peter Kriegel
Issue Date:January 2004
pp. 56-69
<p><b>Abstract</b>—Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic fr...
 
Factorizing YAGO: scalable machine learning for linked data
Found in: Proceedings of the 21st international conference on World Wide Web (WWW '12)
By Hans-Peter Kriegel, Maximilian Nickel, Volker Tresp
Issue Date:April 2012
pp. 271-280
Vast amounts of structured information have been published in the Semantic Web's Linked Open Data (LOD) cloud and their size is still growing rapidly. Yet, access to this information via reasoning and querying is sometimes difficult, due to LOD's size, par...
     
Digging for knowledge with information extraction: a case study on human gene-disease associations
Found in: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM '10)
By Anna Bauer-Mehren, Hans-Peter Kriegel, Laura Furlong, Markus Bundschus, Volker Tresp
Issue Date:October 2010
pp. 1845-1848
We present the information extraction system Text2SemRel. The system (semi-) automatically constructs knowledge bases from textual data consisting of facts about entities using semantic relations. An integral part of the system is a graph-based interactive...
     
Tutorial summary: Learning with dependencies between several response variables
Found in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09)
By Kai Yu, Volker Tresp
Issue Date:June 2009
pp. 1-1
Previous studies of Non-Parametric Kernel (NPK) learning usually reduce to solving some Semi-Definite Programming (SDP) problem by a standard SDP solver. However, time complexity of standard interior-point SDP solvers could be as high as O(n6.5). Such inte...
     
Supervised probabilistic principal component analysis
Found in: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '06)
By Hans-Peter Kriegel, Kai Yu, Mingrui Wu, Shipeng Yu, Volker Tresp
Issue Date:August 2006
pp. 464-473
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When labels of data are available, e.g., in a classification or regression task, PCA is...
     
Collaborative ordinal regression
Found in: Proceedings of the 23rd international conference on Machine learning (ICML '06)
By Hans-Peter Kriegel, Kai Yu, Shipeng Yu, Volker Tresp
Issue Date:June 2006
pp. 1089-1096
Ordinal regression has become an effective way of learning user preferences, but most research focuses on single regression problems. In this paper we introduce collaborative ordinal regression, where multiple ordinal regression tasks are handled simultane...
     
Active learning via transductive experimental design
Found in: Proceedings of the 23rd international conference on Machine learning (ICML '06)
By Jinbo Bi, Kai Yu, Volker Tresp
Issue Date:June 2006
pp. 1081-1088
This paper considers the problem of selecting the most informative experiments x to get measurements y for learning a regression model y = f(x). We propose a novel and simple concept for active learning, transductive experimental design, that explores avai...
     
Learning Gaussian processes from multiple tasks
Found in: Proceedings of the 22nd international conference on Machine learning (ICML '05)
By Anton Schwaighofer, Kai Yu, Volker Tresp
Issue Date:August 2005
pp. 1012-1019
We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equivalence between parametric linear models and nonparametric Gaussian processes (G...
     
Dirichlet enhanced relational learning
Found in: Proceedings of the 22nd international conference on Machine learning (ICML '05)
By Hans-Peter Kriegel, Kai Yu, Shipeng Yu, Volker Tresp, Zhao Xu
Issue Date:August 2005
pp. 1004-1011
We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned by entities or relationships, and are coupled via a common prior distribution. Flexibili...
     
A nonparametric hierarchical bayesian framework for information filtering
Found in: Proceedings of the 27th annual international conference on Research and development in information retrieval (SIGIR '04)
By Kai Yu, Shipeng Yu, Volker Tresp
Issue Date:July 2004
pp. 353-360
Information filtering has made considerable progress in recent years. The predominant approaches are content-based methods and collaborative methods. Researchers have largely concentrated on either of the two approaches since a principled unifying framewor...
     
Removing redundancy and inconsistency in memory-based collaborative filtering
Found in: Proceedings of the eleventh international conference on Information and knowledge management (CIKM '02)
By Anton Schwaighofer, Hans-Peter Kriegel, Kai Yu, Volker Tresp, Xiaowei Xu
Issue Date:November 2002
pp. 52-59
The application range of memory-based collaborative filtering (CF) is limited due to CF's high memory consumption and long runtime. The approach presented in this paper removes redundant and inconsistent instances (users) from the data. This paper aims to ...
     
The generalized Bayesian committee machine
Found in: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '00)
By Volker Tresp
Issue Date:August 2000
pp. 130-139
With over 800 million pages covering most areas of human endeavor, the World-wide Web is a fertile ground for data mining research to make a difference to the effectiveness of information search. Today, Web surfers access the Web through two dominant inter...
     
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