1. 2007.03.29 SLIDE 1ISGC 2007 - Taipei, Taiwan Grid-based Search and Data Mining Using Cheshire3 In collaboration with Robert Sanderson University of Liverpool Department of Computer Science Presented by Ray R. Larson University of California, Berkeley School of Information

2. 2007.03.29 SLIDE 2ISGC 2007 - Taipei, Taiwan Overview Introduction Context Architecture Grid Text Mining Data Mining Applications Future Plans and Applications Questions?

3. 2007.03.29 SLIDE 3ISGC 2007 - Taipei, Taiwan Introduction Cheshire History: –Developed at UC Berkeley originally –Solution for library data (C1), then SGML (C2), then XML –Monolithic applications for indexing and retrieval server in C + TCL scripting Cheshire3: –Developed at Liverpool, plus Berkeley –XML, Unicode, Grid scalable: Standards based –Object Oriented Framework –Easy to develop and extend in Python

4. 2007.03.29 SLIDE 4ISGC 2007 - Taipei, Taiwan Introduction Today: –Version 0.9.4 –Mostly stable, but needs thorough QA and docs –Grid, NLP and Classification algorithms integrated Near Future: –June: Version 1.0 Further DM/TM integration, docs, unit tests, stability –December: Version 1.1 Grid out-of-the-box, configuration GUI

5. 2007.03.29 SLIDE 5ISGC 2007 - Taipei, Taiwan Context Environmental Requirements: –Very Large scale information systems Terabyte scale (Data Grid) Computationally expensive processes (Comp. Grid) Digital Preservation Analysis of data, not just retrieval (Data/Text Mining) Ease of Extensibility, Customizability (Python) Open Source Integrate not Re-implement "Web 2.0" – interactivity and dynamic interfaces

6. 2007.03.29 SLIDE 6ISGC 2007 - Taipei, Taiwan Context Data Grid Layer Data Grid SRB iRODS Digital Library Layer Application Layer Web Browser Multivalent Dedicated Client User Interface Apache+ Mod_Python+ Cheshire3 Protocol Handler Process Management Kepler Cheshire3 Query Results Query Results ExportParse Document Parsers Multivalent,... Natural Language Processing Information Extraction Text Mining Tools Tsujii Labs,... Classification Clustering Data Mining Tools Orange, Weka,... Query Results Search / Retrieve Index / Store Information System Cheshire3 User Interface MySRB PAWN Process Management Kepler iRODS rules Term Management Termine WordNet... Store

7. 2007.03.29 SLIDE 7ISGC 2007 - Taipei, Taiwan Cheshire3 Object Model UserStore User ConfigStore Object Database Query Record Transformer Records Protocol Handler Normaliser IndexStore Terms Server Document Group Ingest Process Documents Index RecordStore Parser Document Query ResultSet DocumentStore Document PreParser Extracter

8. 2007.03.29 SLIDE 8ISGC 2007 - Taipei, Taiwan Object Configuration One XML 'record' per non-data object Very simple base schema, with extensions as needed Identifiers for objects unique within a context (e.g., unique at individual database level, but not necessarily between all databases) Allows workflows to reference by identifier but act appropriately within different contexts. Allows multiple administrators to define objects without reference to each other

9. 2007.03.29 SLIDE 9ISGC 2007 - Taipei, Taiwan Grid Focus on ingest, not discovery (yet) Instantiate architecture on every node Assign one node as master, rest as slaves. Master then divides the processing as appropriate. Calls between slaves possible Calls as small, simple as possible: (objectIdentifier, functionName, *arguments) Typically: ('workflow-id', 'process', 'document-id')

10. 2007.03.29 SLIDE 10ISGC 2007 - Taipei, Taiwan Grid Architecture Master Task Slave Task 1 Slave Task N Data Grid GPFS Temporary Storage (workflow, process, document) fetch document document extracted data

11. 2007.03.29 SLIDE 11ISGC 2007 - Taipei, Taiwan Grid Architecture - Phase 2 Master Task Slave Task 1 Slave Task N Data Grid GPFS Temporary Storage (index, load) store index fetch extracted data

12. 2007.03.29 SLIDE 12ISGC 2007 - Taipei, Taiwan Workflow Objects Written as XML within the configuration record. Rewrites and compiles to Python code on object instantiation Current instructions: –object –assign –fork –for-each –break/continue –try/except/raise –return –log (= send text to default logger object) Yes, no if!

13. 2007.03.29 SLIDE 13ISGC 2007 - Taipei, Taiwan Workflow example workflow.SimpleWorkflow Unparsable Record ”Loaded Record:” + input.id

14. 2007.03.29 SLIDE 14ISGC 2007 - Taipei, Taiwan Text Mining Integration of Natural Language Processing tools Including: –Part of Speech taggers (noun, verb, adjective,...) –Phrase Extraction –Deep Parsing (subject, verb, object, preposition,...) –Linguistic Stemming (is/be fairy/fairy vs is/is fairy/fairi) Planned: Information Extraction tools

15. 2007.03.29 SLIDE 15ISGC 2007 - Taipei, Taiwan Data Mining Integration of toolkits difficult unless they support sparse vectors as input - text is high dimensional, but has lots of zeroes Focus on automatic classification for predefined categories rather than clustering Algorithms integrated/implemented: –Perceptron, Neural Network (pure python) –Naïve Bayes (pure python) –SVM (libsvm integrated with python wrapper) –Classification Association Rule Mining (Java)

16. 2007.03.29 SLIDE 16ISGC 2007 - Taipei, Taiwan Data Mining Modelled as multi-stage PreParser object (training phase, prediction phase) Plus need for AccumulatingDocumentFactory to merge document vectors together into single output for training some algorithms (e.g., SVM) Prediction phase attaches metadata (predicted class) to document object, which can be stored in DocumentStore Document vectors generated per index per document, so integrated NLP document normalization for free

17. 2007.03.29 SLIDE 17ISGC 2007 - Taipei, Taiwan Data Mining + Text Mining Testing integrated environment with 500,000 medline abstracts, using various NLP tools, classification algorithms, and evaluation strategies. Computational grid for distributing expensive NLP analysis Results show better accuracy with fewer attributes:

18. 2007.03.29 SLIDE 18ISGC 2007 - Taipei, Taiwan Applications (1) Automated Collection Strength Analysis Primary aim: Test if data mining techniques could be used to develop a coverage map of items available in the London libraries. The strengths within the library collections were automatically determined through enrichment and analysis of bibliographic level metadata records. This involved very large scale processing of records to: –Deduplicate millions of records –Enrich deduplicated records against database of 45 million –Automatically reclassify enriched records using machine learning processes (Naïve Bayes)

19. 2007.03.29 SLIDE 19ISGC 2007 - Taipei, Taiwan Applications (1) Data mining enhances collection mapping strategies by making a larger proportion of the data usable, by discovering hidden relationships between textual subjects and hierarchically based classification systems. The graph shows the comparison of numbers of books classified in the domain of Psychology originally and after enhancement using data mining

20. 2007.03.29 SLIDE 20ISGC 2007 - Taipei, Taiwan Applications (2) Assessing the Grade Level of NSDL Education Material The National Science Digital Library has assembled a collection of URLs that point to educational material for scientific disciplines for all grade levels. These are harvested into the SRB data grid. Working with SDSC we assessed the grade-level relevance by examining the vocabulary used in the material present at each registered URL. We determined the vocabulary-based grade-level with the Flesch-Kincaid grade level assessment. The domain of each website was then determined using data mining techniques (TF-IDF derived fast domain classifier). This processing was done on the Teragrid cluster at SDSC.

21. 2007.03.29 SLIDE 21ISGC 2007 - Taipei, Taiwan Applications (2) The formula for the Flesch Reading Ease Score: FRES = 206.835 –1.015 ((total words)/(total sentences)) – 84.6 ((total syllables)/(total words)) The Flesch-Kincaid Grade Level Formula: FKGLF = 0.39 * ((total words)/(total sentences)) + 11.8 * ((total syllables)/(total words)) –15.59 The Domain was determined by: –Domains used were based upon the AAAS Benchmarks –Taking in samples from each of the domain areas being examined and produces scored and ranked lists of vocabularies for each domain. –Each token in a document is passed through a lookup function against this table and tallies are calculated for the entire document. –These tallies are then used to rank the order of likelihood of the document being about each topic and a statistical pass of the results returns only those topics that are above in certain threshold.

22. 2007.03.29 SLIDE 22ISGC 2007 - Taipei, Taiwan Future Plans IR Testing and Optimization –Work with the OCA Book collection as part of INEX 2007 –TREC, CLEF, and INEX Benchmarking Integration of Geographic Information Retrieval methods from Cheshire II –GIR Ranking and Gazetteer-based text retrieval using NLP methods Pattern-driven text mining methods for extracting biographical information from texts –IMLS-funded “Bringing Lives to Light” project

23. 2007.03.29 SLIDE 23ISGC 2007 - Taipei, Taiwan Overview Bringing Lives to Light –Focusing on the Who in Who, What, Where and When –Examining and extending of various types of Biographical Markup –Mining biographical data from available information resources to fill our extended markup databases

24. 2007.03.29 SLIDE 24ISGC 2007 - Taipei, Taiwan WHEN, WHERE and WHO Catalog records found from a time period search commonly include names of persons important at that time. Their names can be forwarded to, e.g., biographies in the Wikipedia encyclopedia.

25. 2007.03.29 SLIDE 25ISGC 2007 - Taipei, Taiwan Place and time are broadly important across numerous tools and genres including, e.g. Language atlases, Library catalogs, Biographical dictionaries, Bibliographies, Archival finding aids, Museum records, etc., etc. Biographical dictionaries are also heavy on place and time: Emanuel Goldberg, Born Moscow 1881. PhD under Wilhelm Ostwald, Univ. of Leipzig, 1906. Director, Zeiss Ikon, Dresden, 1926-33. Moved to Palestine 1937. Died Tel Aviv, 1970. Life as a series of episodes involving Activity (WHAT), WHERE, WHEN, and WHO else.

26. 2007.03.29 SLIDE 26ISGC 2007 - Taipei, Taiwan A new form of biographical dictionary would link to all Texts Numeric datasets Thesaurus/ Ontology GazetteerscaptionsMaps/ Geo Data EVI Time Period Directory Time lines, Chronologies Biographical Dictionary

27. 2007.03.29 SLIDE 27ISGC 2007 - Taipei, Taiwan “Lives” Projected Work Develop XML markup for Biographical Events Most likely to be adaptation and extension of existing biographical event markup –Example: EAC/EAD Harvest biographical resources –Wikipedia, etc. Integrate as next generation of current interface

28. 2007.03.29 SLIDE 28ISGC 2007 - Taipei, Taiwan EAC/EAD Biographical Note 1892, May 7 Born, Glencoe, Ill. 1915 A.B., Yale University, New Haven, Conn. 1916 Married Ada Hitchcock 1917-1919 Served in United States Army

29. 2007.03.29 SLIDE 29ISGC 2007 - Taipei, Taiwan Wikipedia data Life events metadata WHAT: Actions prisoner WHERE: Places Holstein WHEN: Times 1261-1262 WHO: People Margaret Sambiria Need external links

30. 2007.03.29 SLIDE 30ISGC 2007 - Taipei, Taiwan

31. 2007.03.29 SLIDE 31ISGC 2007 - Taipei, Taiwan A Metadata Infrastructure CATALOGS Achives Historical Societies Libraries Museums Public Television Publishers Booksellers Audio Images Numeric Data Objects Texts Virtual Reality Webpages RESOURCES INTERMEDIA INFRASTRUCTURE Biographical DictionaryWHO TimelinesTime Period DirectoryWHEN MapsGazetteer WHERE Syndetic StructureThesaurusWHAT Special Display ToolsAuthority ControlFacet Learners Dossiers

32. 2007.03.29 SLIDE 32ISGC 2007 - Taipei, Taiwan “Lives” Acknowledgements Electronic Cultural Atlas Initiative project This work is being supported supported by the Institute of Museum and Library Services through a National Leadership Grant for Libraries Contact: ray@ischool.berkeley.edu

33. 2007.03.29 SLIDE 33ISGC 2007 - Taipei, Taiwan Thank you! Available via http://www.cheshire3.org