SLIDE 1ISGC 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

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

SLIDE 3ISGC 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

SLIDE 4ISGC Taipei, Taiwan Introduction Today: –Version –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

SLIDE 5ISGC 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

SLIDE 6ISGC 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

SLIDE 7ISGC 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

SLIDE 8ISGC 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

SLIDE 9ISGC 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')

SLIDE 10ISGC 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

SLIDE 11ISGC 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

SLIDE 12ISGC 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!

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

SLIDE 14ISGC 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

SLIDE 15ISGC 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)

SLIDE 16ISGC 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

SLIDE 17ISGC 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:

SLIDE 18ISGC 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)

SLIDE 19ISGC 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

SLIDE 20ISGC 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.

SLIDE 21ISGC Taipei, Taiwan Applications (2) The formula for the Flesch Reading Ease Score: FRES = –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)) * ((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.

SLIDE 22ISGC 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

SLIDE 23ISGC 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

SLIDE 24ISGC 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.

SLIDE 25ISGC 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 PhD under Wilhelm Ostwald, Univ. of Leipzig, Director, Zeiss Ikon, Dresden, Moved to Palestine Died Tel Aviv, Life as a series of episodes involving Activity (WHAT), WHERE, WHEN, and WHO else.

SLIDE 26ISGC 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

SLIDE 27ISGC 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

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

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

SLIDE 30ISGC Taipei, Taiwan

SLIDE 31ISGC 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

SLIDE 32ISGC 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:

SLIDE 33ISGC Taipei, Taiwan Thank you! Available via