URSW 2011
International Workshop on
Uncertainty Reasoning for the Semantic Web
October 23, 2011
Bonn, Germany
Workshop Agenda

Technical paper authors were assigned 25 minutes to present their work plus 5 minutes for questions. Position paper authors were assigned 10 minutes to present their ideas plus 5 minutes for questions.

Many AI inference problems arising in a wide variety of fields such as machine learning, semantic web, network communication, computer vision, and robotics can be solved using message-passing algorithms that operate on factor graphs. Often, however, we are facing inference problems with symmetries not reflected in the graph structure and, hence, not exploitable by efficient message-passing algorithms. Recently, several lifting approaches have been proposed that exploit such additional symmetries. Starting from a given factor graph, they essentially first construct a lifted factor graph of supernodes and superfactors, corresponding to sets of nodes and factors that send and receive the same messages, i.e., that are indistinguishable given the evidence. Then, they run a modified message-passing algorithm on the often smaller lifted factor gaph. In this talk, I will demonstrate that another important AI technique is lifteable too: linear programs (LPs). Intuitively, we employ a lifted variant of Gaussian belief propagation (GaBP) to solve the systems of linear equations arising when running an interior-point solver based on a Newton method. Then, we show that running lifted GaBP is not required at all. Instead we can read off a lifted but equivalent LP from it that can be solved using any off-the-shelf LP solver. This contribution significantly pushes the boundaries of lifted inference as it directly paves the way to novel sometimes even first lifted approaches for MAP inference, solving MDPs, and flow problems, among others.

This talk is based on joint works with Babak Ahmadi, Yussef El Massaoudi, Fabian Hadiji, Michael Haimes, Leslie Kaelbling, Brian Milch, Martin Mladenov, Sriraam Natarajan, Scott Sanner, and Luke Zettlemoyer.

Kristian Kersting is the head of the "statisitcal relational activity mining" (STREAM) group at Fraunhofer IAIS, Bonn, Germany, and a research fellow of the University of Bonn, Germany. He received his Ph.D. from the University of Freiburg, Germany, in 2006. After a PostDoc at the MIT, USA, he joined Fraunhofer IAIS in 2008 to build up the STREAM research group using an ATTRACT Fellowship. His main research interests are statistical relational artificial intelligence, machine learning, and data mining. He has published over 70 peer-reviewed papers, has received the ECML Best Student Paper Award in 2006 and the ECCAI Dissertation Award 2006 for the best European dissertation in the field of AI, and is an ERCIM Cor Baayen Award 2009 finalist for the "Most Promising Young Researcher In Europe in Computer Science and Applied Mathematics". He gave several tutorials at top conferences (AAAI, ECML-PKDD, ICAPS, ICML, IJCAI, ...) and (will) co-chair(ed) MLG-07, SRL-09, MLG-11, CoLISD-11, STAIRS-12, and the first workshop on Statistical Relational AI (StarAI-10). In 2013, he will co-chair the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). He (will) serve(d) as area chair for ECML (06,07), ICML (10,11,12) as Senior PC member at IJCAI-11 and AAAI-12, and on the PCs of several top conference (AAAI, ECAI, ECML PKDD, ICAPS, ICML, IJCAI, KDD, RSS, PODS, ...). He was a guest co-editor for special issues of the Annals of Mathematics and AI (AMAI), the Journal of Machine Learning Research (JMLR), and the Machine Learning Journal (MLJ). Currently, he serves as an action editor for MLJ and the Data Mining and Knowledge Discovery (DAMI) Journal as well as an associate editor for the Journal of Artifical Intelligence Research (JAIR).

09:00 - 10:00

Invited Talk

Coordinator: Claudia d'Amato

09:00 - 10:00

From Lifted Probabilistic Inference to Lifted Linear Programming

Kristian Kersting

10:00 - 10:30

Session 1: Probabilistic approaches I

Coordinator: Kristian Kersting

10:00 - 10:15

Reasoning under Uncertainty with Log-Linear Description Logics

Mathias Niepert
paper, presentation

10:15 - 10:30

Handling Uncertainty in Information Extraction

Maurice Van Keulen, Mena B. Habib
paper, presentation (pdf, pptx)

10:30 - 11:00



11:00 - 12:30

Session 2: Probabilistic approaches II

Coordinator: Claudia d'Amato

11:00 - 11:30

Semantic Link Prediction through Probabilistic Description Logics

Kate Revoredo, José Eduardo Ochoa Luna, Fabio Gagliardi Cozman
paper, presentation

11:30 - 12:00

Representing Sampling Distributions in P-SROIQ

Pavel Klinov, Bijan Parsia
paper, presentation

12:00 - 12:30

A Distribution Semantics for Probabilistic Ontologies

Elena Bellodi, Evelina Lamma, Fabrizio Riguzzi, Simone Albani
paper, presentation

12:30 - 14:00



14:30 - 16:00

Session 3: Fuzzy and Dempster-Shafer approaches

Coordinator: Claudia d'Amato

14:30 - 15:00

Building A Fuzzy Knowledge Body for Integrating Domain Ontologies

Konstantin Todorov, Peter Geibel, Céline Hudelot
paper, presentation

15:00 - 15:30

Finite Lattices Do Not Make Reasoning in ALCI Harder

Stefan Borgwardt, Rafael Peñaloza
paper, presentation

15:30 - 16:00

An Evidential Approach for Modeling and Reasoning on Uncertainty in Semantic Applications

Amandine Bellenger, Sylvain Gatepaille, Habib Abdulrab, Jean-Philippe Kotowicz
paper, presentation

16:00 - 16:30



16:30 - 17:45

Session 4: Bayesian approaches

Coordinator: Pavel Klinov

16:30 - 17:00

Estimating Uncertainty of Categorical Web Data

Davide Ceolin, Willem Robert Van Hage, Wan Fokkink, Guus Schreiber
paper, presentation

17:00 - 17:30

Learning Terminological Naive Bayesian Classifiers under Different Assumptions on Missing Knowledge

Pasquale Minervini, Claudia d'Amato, Nicola Fanizzi
paper, presentation

17:30 - 17:45

Distributed Imprecise Design Knowledge on the Semantic Web

Julian R. Eichhoff, Wolfgang Maass
paper, presentation (pdf, pptx)

17:45 - 18:00

Closing remarks

Coordinator: Claudia d'Amato

17:45 - 18:00

Final discussion

Claudia d'Amato