Knowledge Discovery, Machine Learning, and Social Mining Klausurtagung SFB-"Früherkennung von Stress und Stresstoleranz", Bonn, July 23, 2010 Stefan Wrobel Christian Bauckhage Thomas Gärtner Kristian Kersting

@University of Bonn Basic Research Groups: ~ 25 people Focus on Machine Learning, Data Mining (, and AI) Editorial Boards: MLJ, JMLR, JAIR, … PC Chairs: ICML, SRL, MLG, StarAI, … Regularly PC members of major AI, ML, and DM conferences ICML, IJCAI, ECML-PKDD, AAAI, RSS, … International Tutorials: ICML, ECML-PKDD, AAAI, ICAPS, … Awards: ECML, ICDM, MLG, ECAI Dissertation Award, ATTRACT Fellowship, … International Collabs: MIT, Cornell, Toronto, Google, … Institute Director of Fraunhofer IAIS

@Fraunhofer - IAIS Explores and develops innovative systems to analyze data and to make information available 260 people: scientists, project engineers, technical and administrative staff Concentrates the competences and scientific qualities of all engineering disciplines especially informatics, and mathematics, natural sciences, business economics, geo and social sciences Profound industry expertise Joint research groups and cooperations with the University of Bonn

„Stress und Stresstoleranz“ 4 Years: Methods/models at individual levels Visual Analytics Social Network Analysis Frequent Itemsets Subgroup Discovery Graphical Models Kernel Methods Experimental Design Gaussian Processes Web-Scale Matrix Factorization

Example: Hyperspectral Images Matrices with millions/ billions of entries

8-12 Years Mission: Stress is complex and uncertain Let‘s deal with uncertainty and structure (objects and relations) jointly Natural domain modeling: objects, properties, relations Compact, natural models Properties of entities can depend on properties of related entities Generalization over a variety of situations … Robotics CV Search Planning SAT Probability Statistics Logic Graphs Trees Learning Ongoing “revolution” within AI, ML, and DM

Information Extraction Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains” (AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence.

Information Extraction Paper Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains” (AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence.

Segmentation Author Title Paper Venue Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains” (AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence.

Entity Resolution Author Title Paper Venue Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains” (AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence.

Entity Resolution Author Title Paper Venue Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains” (AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence. w1 : Author(bc1,a1) ^ Author(bc2,a2) ^ SameAuthor(a1,a2) => SameBib(bc1,bc2) w2 : Title(bc1,t1) ^ Title(bc2,t2) ^ SameTitle(t1,t2) => SameBib(bc1,bc2) w3 : Venue(bc1,v1) ^ Venue(bc2,v2) ^ SameVenue(v1,v2) => SameBib(bc1,bc2) w4 : Author(bc1,a1) ^ Author(bc2,a2) ^ SameBib(bc1,bc2) => SameAuthor(a1,a2) w5 : itle(bc1,t1) ^ Title(bc2,t2) ^ SameBib(bc1,bc2) => SameTitle(t1,t2) w6 : Venue(bc1,v1) ^ Venue(bc2,v2) ^ SameBib(bc1,bc2) => SameVenue(v1,v2)

“Stress und Stresstoleranz“ Author “Stress und Stresstoleranz“ Title Paper Venue Parag Singla and Pedro Domingos, “Memory-Efficient Inference in Relational Domains” (AAAI-06). Singla, P., & Domingos, P. (2006). Memory-efficent inference in relatonal domains. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (pp. 500-505). Boston, MA: AAAI Press. H. Poon & P. Domingos, Sound and Efficient Inference with Probabilistic and Deterministic Dependencies”, in Proc. AAAI-06, Boston, MA, 2006. P. Hoifung (2006). Efficent inference. In Proceedings of the Twenty-First National Conference on Artificial Intelligence. w1 : Author(bc1,a1) ^ Author(bc2,a2) ^ SameAuthor(a1,a2) => SameBib(bc1,bc2) w2 : Title(bc1,t1) ^ Title(bc2,t2) ^ SameTitle(t1,t2) => SameBib(bc1,bc2) w3 : Venue(bc1,v1) ^ Venue(bc2,v2) ^ SameVenue(v1,v2) => SameBib(bc1,bc2) w4 : Author(bc1,a1) ^ Author(bc2,a2) ^ SameBib(bc1,bc2) => SameAuthor(a1,a2) w5 : itle(bc1,t1) ^ Title(bc2,t2) ^ SameBib(bc1,bc2) => SameTitle(t1,t2) w6 : Venue(bc1,v1) ^ Venue(bc2,v2) ^ SameBib(bc1,bc2) => SameVenue(v1,v2) 8-12 Years Vision: Knowledge-rich, holistic statistical models/methods across different levels

Conclusions Thanks for your attention Basic Research + Industry Structure (Objects & Relations, Graphs, Tress) + Probabilities / Kernels + (Massive) Machine Learning and Data Mining 4 Years Goal: Models at different levels 8-12 Years Vision: Joint models across levels Lifted inference, Dynamic Models, Continuous values, Massive Data Analysis Tools Thanks for your attention