“Towards Multi-Step Expert Advice for Cognitive Computing” - Dr. Achim Rettinger, Karlsruhe Institute of Technology

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Dr. Achim Rettinger from Karlsruhe Institute of Technology presented this today as part of the Cognitive Systems Institute Speaker Series on October 13, 2016

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“Towards Multi-Step Expert Advice for Cognitive Computing” - Dr. Achim Rettinger, Karlsruhe Institute of Technology

  1. 1. KIT – Karlsruhe Institute of Technology INSTITUTE OF APPLIED INFORMATICS ANDFORMAL DESCRIPTION METHODS (AIFB) www.kit.edu Towards Multi-Step Expert Advice for Cognitive Computing Achim Rettinger (rettinger@kit.edu) Cognitive Systems Institute Speaker Series, October/13/2016
  2. 2. Institute of Applied Informatics and Formal Description Methods 2 My Research Group Media Channel Analytics Healthcare Analytics KIT • Former University of Karlsruhe, Germany • 24.800 students • 9.500 employees AIFB • Research Group Web Science and Knowledge Managment • Prof. Studer and Prof. Sure-Vetter KSRI • Industry-on- campus model • Prof. Satzger
  3. 3. Institute of Applied Informatics and Formal Description Methods 3 Our Research Cross-Lingual Technologies Cross-Modal Technologies Language A Language B DiCaprio appeare d in Titanic DiCaprio spielt in Titanic (Mogadala et al. 2015) instances of modalities present in the documents. To reduce the c we assume a multi-modal document Di = (T ext, Media) to contai media item either an image, video or audio embedded with a text desc collection Cj = {D1, D2...Di...Dn} of these documents in different lang {LC1 , LC2 ...LCj ...LCm } are spread across web. Formally, our research to find a cross-modal semantically similar document across language LCo using unsupervised similarity measures on low-dimension correla representation. Figure 2 shows broad visualization of the approach. Fig. 2. Correlated Space Retrieval (Zhang et al. 2014)
  4. 4. Institute of Applied Informatics and Formal Description Methods 4 Our Research Semantic Search Entity Summarization Fig. 1. Automatically annotated excerpt of a Wikipedia article9 and the summaClient knowledge panel with a summary by LinkSUM. that can be enabled at the top of each page. Other proprietary solutions include the Bing Knowledge Widget6 and Ontotext’s Now7 . Most of the proprietary solutions are highly customized and the annotation and knowledge panel parts are often strongly connected. 4 Summary With ELES, we propose loose coupling between automatic entity linking and en- tity summarization systems via ITS 2.0. We exemplify the lightweight integration approach with the applications DBpedia Spotlight and the qSUM method of the SUMMA entity summarization interface. Filter for Multiple Entities Constan t Stream (Zhang et at. 2016) (Thalhammer et al. 2016)
  5. 5. Institute of Applied Informatics and Formal Description Methods 5 Our Innovation Projects LiMexLiMe – crossLingual crossMedia knowledge extraction http://xlime.eu Augment with related content from news and social media Semantic Search across content in channels Supported by
  6. 6. Institute of Applied Informatics and Formal Description Methods 6 “Watson Seminar” supported by IBM Academic Initiative Our Teaching ▪ Create a system that identifies the relationship between two randomly given characters Expectations to final solution
  7. 7. Institute of Applied Informatics and Formal Description Methods 7 TOWARDS MULTI-STEP EXPERT ADVICE FOR COGNITIVE COMPUTING Joint work with Patrick Philipp
  8. 8. Institute of Applied Informatics and Formal Description Methods 8 Many tasks comprise multiple steps … Step 1 Step 2 Step n…
  9. 9. Institute of Applied Informatics and Formal Description Methods 9 Medical Assistance Brain Stripping Brain Registration Robust Brain Normalization Normal Brain Normalization Tumor Segmentation Map Generation Tumor Prediction Tumor Progression Mapping (Philipp et al. 2015)
  10. 10. Institute of Applied Informatics and Formal Description Methods 10 Natural Language Processing Named Entity Recognition Named Entity Linking Entity Disambiguation WebofDocuments WebofThings
  11. 11. Institute of Applied Informatics and Formal Description Methods 11 Multiple “experts“ might be available … Step 1 Step 2 Step n… Expert 1 Expert 2 Expert m Expert 1 Expert 2 Expert m Expert 1 Expert 2 Expert m … … …
  12. 12. Institute of Applied Informatics and Formal Description Methods 12 Natural Language Processing Named Entity Recognition Named Entity Linking Entity Disambiguation - Example FOX Stanford Tagger X-LISA POS Rules … AGDISTIS AIDA X-LISA Disambiguator …
  13. 13. Institute of Applied Informatics and Formal Description Methods 13 Develop robust approaches given various data distributions NLP: News articles, social media, blogs, … Medical Assistance: Patients of different departments, scans taken with different machines by different people à Many Machine Learning techniques oversimplify as they assume data to be independent and identically distributed (i.i.d.) Multiple interpretation steps render brute force approaches impractical Number of possible alternatives grow fast over multiple steps Potential (continuous-) parameters have to be set Different kinds of additional constraints might be set Execution / query budgets: Not all experts can be asked Time budgets: A solution has to be found in a predefined time frame à Learn behavior of experts with as few training samples as possible and transfer knowledge among different training datasets Various Challenges
  14. 14. Institute of Applied Informatics and Formal Description Methods 14 Natural Language Processing Can be applied to natural language processing tasks E.g. named entity recognition and –disambiguation pipeline Hypothesis generation and evaluation Score outputs of experts Adapt weight over time Dynamic learning Learn weights for each expert given a specific context Adapt expert choices given a specific context Incrementally improves with experience Connection to IBM Watson‘s Cognitive Computing Capabilities
  15. 15. Institute of Applied Informatics and Formal Description Methods 15 (Budgeted-) Decision Making with Expert Advice (Cesa-Bianchi et al. 1997, Amin et al. 2015) Adversarial (non i.i.d.) setting with potential budgets Best expert / subset of experts need to be found (Contextual-) Bandits (e.g. Auer et al. 2002) Approaches for adversarial and i.i.d. settings available Only one action can be played, no feedback for the rest A high-dimensional context might be given to generalize (Contextual-) Markov Decision Processes (Puterman 1996, Krishnamurthy et al. 2016 ) for Reinforcement Learning Multi-stage contextual bandit with different context spaces Only intractable solutions with good theoretical performance guarantees exist Connection to Decision Making Theory
  16. 16. Institute of Applied Informatics and Formal Description Methods 16 Problem Formalization – Entity Disambiguation Example ! "! ! Michael Jordan basket ball $! ! $% ! ! "! ! $! % $% % ! "! ! ! "! !! "! !Michael Jordan à NE basketball à NE Michael Jordan à NE basket ball à NIL ! "! !Michael Jordan à dbpedia: Michael_J ordan basket ball à NIL +1 Michael Jordan à NE basket ball à NIL basket ball à NIL Michael Jordan à dbpedia: Michael_J ordan
  17. 17. Institute of Applied Informatics and Formal Description Methods 17 Probabilistic Soft Logic (PSL) PSL (Kimmig et al. 2012) is a template language to instantiate a Hinge Loss Markov Random Field (HL-MRF) (Bach et al. 2012) 0.3: *+,$-. /, 1 ∧ 345$"64+ 1, 7 ≫ 345$"64+(/, 7), 0.8: "<4="$ /, 1 ∧ 345$"64+ 1, 7 ≫ 345$"64+(/, 7) Given such PSL rules and observations (data), we can infer the unknown truth values (atoms) Our Idea: Certain sequences of experts perform better on certain decision candidates Introduce a set of PSL rules that describes the dependencies between experts and decision candidates in a specific state Collect observations of executions of the pipeline Probabilistic inference will give you the weights telling you how to execute experts in each state
  18. 18. Institute of Applied Informatics and Formal Description Methods 18 PSL Rules for Multi-Step Learning >! ?@! >% ?@! >! ? >% ? >A ? ! B! ?@! % B! ?@! ! B! ?
  19. 19. Institute of Applied Informatics and Formal Description Methods 19 PSL Rules for Multi-Step Learning >! ?@! >% ?@! >! ? >% ? >A ? ! B! ?@! % B! ?@! ! B! ? Hypothesis / Locality / Weight / Value
  20. 20. Institute of Applied Informatics and Formal Description Methods 20 PSL Rules for Multi-Step Learning >! ?@! >% ?@! >! ? >% ? >A ? ! B! ?@! % B! ?@! % B! ? Hypothesis / Locality / Weight / Value C!.!: D4EFG,5H >, B => K$,Lℎ5(>, B) C1.2: K$,Lℎ5(>, B!) ∧ PH<45ℎ$"," >, B!, B% => QFG=$(B%)
  21. 21. Institute of Applied Informatics and Formal Description Methods 21 PSL Rules for Multi-Step Learning >! ?@! >% ?@! >! ? >% ? >A ? ! B! ?@! % B! ?@! ! B! ? Independence
  22. 22. Institute of Applied Informatics and Formal Description Methods 22 PSL Rules for Multi-Step Learning >! ?@! >% ?@! >! ? >% ? >A ? ! B! ?@! % B! ?@! ! B! ? Independence / Combination C2: R-.$<$-.$-5 >!, >%, B => K$,Lℎ5(>!, B)
  23. 23. Institute of Applied Informatics and Formal Description Methods 23 PSL Rules for Multi-Step Learning >! ?@! >% ?@! >! ? >% ? >A ? ! B! ?@! % B! ?@! ! B! ? Robustness / Future Reward
  24. 24. Institute of Applied Informatics and Formal Description Methods 24 PSL Rules for Multi-Step Learning >! ?@! >% ?@! >! ? >% ? >A ? ! B! ?@! % B! ?@! ! B! ? Robustness / Future Reward C3: S4T="5 >!, >%, B => K$,Lℎ5(>!, B)
  25. 25. Institute of Applied Informatics and Formal Description Methods 25 Task: Named Entity Recognition + Named Entity Disambiguation (Entity Linking) for tweets and news articles Scenario 1 (individual steps): Predict the performance on NER and NED of experts for Tweets, left out from training set Articles, trained on tweets only Scenario 2 (full pipeline): Given a process for collecting samples (e,s) (i.e. expert performance on tweet or article), select best outcomes to improve overall performance Empirical Evaluation
  26. 26. Institute of Applied Informatics and Formal Description Methods 26 1. NER 1. NED 2. Preliminary Results
  27. 27. Institute of Applied Informatics and Formal Description Methods 27 Heuristic similarity measures such as text length or number of extra characters yield good results The relational learning approach (PSL) seems to allow for knowledge transfer but further evaluations are needed PSL scales well for thousands of tweets and articles if meta- dependencies are precomputed Lessons learnt
  28. 28. Institute of Applied Informatics and Formal Description Methods 28 PSL approach beats State-of-the-Art for heterogeneous textual data Our approach needs to be embedded into contextual bandit / reinforcement learning techniques. No exploration / exploitation strategy implemented so far. Conclusion & Future Work
  29. 29. Institute of Applied Informatics and Formal Description Methods 29 (Amin et at. 2015) (Auer et al. 2002) (Krishnamurthy et al. 2016) (Puterman 1994) (Bach et al. 2012) (Kimmig et al. 2012) Amin, K., Kale, S., Tesauro, G., and Turaga, D. S. (2015). Budgeted prediction with expert advice. In AAAI, pages 2490–2496. Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E. (2002). The nonstochastic multiarmed bandit problem. SIAM J. Comput., 32(1):48–77. Krishnamurthy, A., Agarwal, A., and Langford, J. (2016). Contextual-mdps for pac-reinforcement learning with rich observations. CoRR, abs/1602.02722. Puterman, M.L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. WileyInterscience, New York. Bach, S. H., Broecheler, M., Getoor, L., and O’Leary, D. P. (2012). Scaling MPE inference for constrained continuous markov random fields with consensus optimization. In NIPS, pages 2663–2671. Kimmig, A., Bach, S., Broecheler, M., Huang, B., and Getoor, L. (2012). A short introduction to probabilistic soft logic. In NIPS Workshop on Probabilistic Programming: Foundations and Applications, pages 1–4. References
  30. 30. Institute of Applied Informatics and Formal Description Methods 30 (Zhang et al. 2016) (Thalhammer et al. 2016) (Philipp et al. 2015) (Mogadala et al. 2015) (Zhang et al. 2014) Lei Zhang, Michael Färber, Achim Rettinger; XKnowSearch! Exploiting Knowledge Bases for Entity-based Cross-lingual Information Retrieval; The 25th ACM International on Conference on Information and Knowledge Management (CIKM), ACM, Oktober, 2016 Andreas Thalhammer, Nelia Lasierra, Achim Rettinger; LinkSUM: Using Link Analysis to Summarize Entity Data; In Bozzon, Alessandro and Cudré-Mauroux, Philippe and Pautasso, Cesare, Web Engineering, 16th International Conference, ICWE 2016, Lugano, Switzerland, June 6-9, 2016. Proceedings, Seiten: 244-261, Springer International Publishing, Lecture Notes in Computer Science, 9671, Cham, Juni, 2016 Patrick Philipp, Maria Maleshkova, Darko Katic, Christian Weber, Michael Goetz, Achim Rettinger, Stefanie Speidel, Benedikt Kämpgen, Marco Nolden, Anna-Laura Wekerle, Rüdiger Dillmann, Hannes Kenngott, Beat Müller, Rudi Studer; Toward Cognitive Pipelines of Medical Assistance Algorithms; International Journal of Computer Assisted Radiology and Surgery, November, 2015 Aditya Mogadala, Achim Rettinger; Multi-Modal Correlated Centroid Space for Multi-Lingual Cross-Modal Retrieval; In Hanbury, Allan and Kazai, Gabriella and Rauber, Andreas and Fuhr, Norbert, Advances in Information Retrieval: 37th European Conference on IR Research (ECIR), Vienna, Austria., Seiten: http://people.aifb.kit.edu/amo/ecir2015/, Springer International Publishing, Cham, Germany, April, 2015 Lei Zhang, Achim Rettinger; X-LiSA: Cross-lingual Semantic Annotation; Proceedings of the VLDB Endowment (PVLDB), the 40th International Conference on Very Large Data Bases (VLDB), 7, (13), Seiten 1693-1696, September, 2014 Own Publications
  31. 31. Institute of Applied Informatics and Formal Description Methods 31 rettinger@kit.edu http://www.aifb.kit.edu/web/Achim_Rettinger/en concerning Research Discussions Innovation Ideas about Expert Processes Cross-Lingual Technologies Cross-Modal Technologies Semantic Search Entity Summarization Thank you & feel free to contact me

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