1. 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

2. @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

3. @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

4. „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

5. Example: Hyperspectral Images
Matrices with millions/
billions of entries

6. 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

7. 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 ). 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.

8. 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 ). 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.

9. 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 ). 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.

10. 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 ). 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.

11. 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 ). 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)
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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)
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w6 : Venue(bc1,v1) ^ Venue(bc2,v2) ^ SameBib(bc1,bc2) => SameVenue(v1,v2)

12. “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 ). 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

13. 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