1. Where does this new information belong? From developing mining algorithms to supporting knowledge discovery Bettina Berendt – thanks for joint work with and support from Ilija Subasić Mathias Verbeke Siegfried Nijssen Luc De Raedt K.U. Leuven

2. Yes we can! The problem

3. The solution? Automatic topic dectection Period 1Period 2Period 3Period 4 Healthcare agenda Green energy plan Opposition to healthcare reform Another healthcare vote Climate agenda A healthcare vote Peace Nobel Prize Cophenhagen climate summit Health0.017 Care0.015 Insurance0.013 American0.013 Uninsured0.009 Families0.008 Working0.005

4. Same event/document; different interpretations & categorisations Visionary president Party-politics (right and left) Obama‘s overall agenda Damp-rag president Rhetorics

5. Similar problems in science and learning Topic detection in time-indexed corpora of news texts Conference programme ! Text miningStream miningMedia studies

6. Music collections, multimedia collections: see Andreas Nürnberger‘s talk at SML 2010 Similar problems in other areas

7. The solution? Context-aware systems / personalisation Female Has problems with anger management You probably do / should think about it this way:... Political activist

8. is (nearly) green What users want leftright squares / circles green / not green... to structure the world how they see it... to re-use their categories (that they worked so hard to find)... to be able to see through their eyes  interactivity... to acknowledge that others see the world differently  semantics  Social similarity / diversity  perspective- taking... to provide data mining methods to do all that!

9.  Research agenda  interactivity  semantics  Social similarity / diversity  perspective- taking... to provide data mining methods to do all that! automatic topic dectection support sense-making = provide methods / tools for Knowledge Disovery (in the full sense) The problem

10.  Research agenda  interactivity  semantics  Social similarity / diversity  perspective- taking... to provide data mining methods to do all that! Our solution approach The problem automatic topic dectection support sense-making = provide methods / tools for Knowledge Disovery (in the full sense)

11. STORIES: functionality basics

13. STORIES: mining basics (1) Graphical summarisation of multiple text documents Document / text pre-processing Document summarization strategy Template recognition Multi-document named entities Stopword removal, lemmatization “fact (assertion) recognition” no topics, but salient concepts & relations time window; word-span window Selection approach for concepts concepts = words or named entities salient concept = high TF & involved in a salient relation, time-indexed Similarity measure to determine salient relations bursty co-occurrence Burstiness measure time relevance, a “temporal co-occurrence lift”

14. Aim: highlight subgraphs that represent an event Topological properties Change: Subgraph new in this period STORIES: mining basics (2) Graph analysis for query recommendation

15. STORIES: evaluation 4.Comparison with other temporal text mining methods nNew (and only) framework for cross-method comparison nRecall-&precision-style metrics  different method rankings 3.Learning effectiveness nDocument search with story graphs leads to averages of n67-75% accuracy on judgments of story fact truth non average, 1.3-4.7 queries with 3.4-5.2 nodes/words per query 1.Information retrieval quality Edges – events: up to 80% recall, ca. 30% precision 2.Search quality Subgraphs index coherent document clusters

16. Damilicious: functionality basics Apply my grouping rfid (Security/privacy, Group 2,...) to the following new search result: * Show users and how similarly they group * Apply U4‘s grouping to my new search result:

17. Damilicious: mining basics (1) Methods and process 1.Query 2.Automatic clustering 3.Manual regrouping 4.Re-use 1.Learn classifier & present way(s) of grouping 2.Transfer the constructed concepts Features/methods for the conceptual/predictive clustering: Lingo phrases, Lingo clustering, Ripper co-citation, bibliometric coupling, word or LSA similarity, combinations; k-means, hierarchical

18. “How similarly do two users group documents?“ For each query q, consider their groupings gr: For several queries: aggregate Damilicious: mining basics (2) Measures of grouping and user diversity Diversity = 1 – similarity = 1 - Normalized mutual information (entropy-based measure) NMI = 0 “How similarly do two users group documents?“ For each query q, consider their groupings gr: For several queries: aggregate

19. Damilicious: evaluation Clustering: Does it generate meaningful document groups? –yes (tradition in bibliometrics) – but: data? –Small expert evaluation of CiteseerCluster Choosing the clustering and classification methods for conceptual clustering –Experiments: different features, clustering methods, classification methods  quality of reconstruction and extension-over-time (NMI) Technology acceptance –End-user experiment (clustering & regrouping) –5-person formative user study (transfer of own results)

20. Sense-making involves –Extracting information from texts –Extracting structural information between entities –Creating, using and modifying categories –Interacting with external representations –Acknowledging diversity and perspective-taking –... Appropriate mining methods, measures,...? More/better evaluation methods and frameworks? Use cases? KD approach Text mining Graph mining Semantics Interactivity Usage mining and “model-processing“ (conceptual / predictive clustering) Conclusions and (some) questions Sense-making involves –Extracting information from texts –Extracting structural information between entities –Creating, using and modifying categories –Interacting with external representations –Acknowledging diversity and perspective-taking –...

21. Questions ?you !Thank

22. Subašić, I. & Berendt, B. (2009). Discovery of interactive graphs for understanding and searching time-indexed corpora. Knowledge and Information Systems. DOI - 10.1007/s10115-009-0227-x (PDF)PDF Berendt, B. & Subašić, I. (2009). STORIES in time: a graph-based interface for news tracking and discovery. n N. Cristianini & M. Turchi (Eds.), Proceedings of Intelligent Analysis and Processing of Web News Content (IAPWNC) at The 2009 IEEE /WIC / ACM International Conferences Web Intelligence (WI'09) / Intelligent Agent Technology (IAT'09). 15 September 2009, Milan, Italy. (Proceedings of WI-IAT.2009, DOI 10.1109/WI-IAT.2009.342, pp. 531-534) (PDF)PDF Verbeke, M., Berendt, B., & Nijssen, S. (2009). Data mining, interactive semantic structuring, and collaboration: A diversity-aware method for sense-making in search. In G. Boato & C. Niederee (Eds.), Proceedings of First International Workshop on Living Web, collocated with the 8th International Semantic Web Conference (ISWC- 2009), Washington D.C., USA, October 26, 2009. CEUR Workshop Proceedings Vol- 515. (PDF)PDF Berendt, B. (2010). Diversity in search: what, how, and what for? Talk at Barcelona Media / Yahoo! Research and UPF, 4 March 2010. (PPT)PPT Berendt, B., Krause, B., & Kolbe-Nusser, S. (2010). Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks. networks. Information Processing & Management, 46(1), 1-10. (PDF)PDF To Read