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March 2006NaCTeM – Ray R. Larson Prof. Ray R. Larson University of California, Berkeley School of Information Metadata as Infrastructure for Information.

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Presentation on theme: "March 2006NaCTeM – Ray R. Larson Prof. Ray R. Larson University of California, Berkeley School of Information Metadata as Infrastructure for Information."— Presentation transcript:

1 March 2006NaCTeM – Ray R. Larson Prof. Ray R. Larson University of California, Berkeley School of Information Metadata as Infrastructure for Information Retrieval and Text Mining

2 March 2006NaCTeM – Ray R. Larson Overview Metadata as Infrastructure –What, Where, When and Who? What are Entry Vocabulary Indexes? –Notion of an EVI –How are EVIs Built Time Period Directories –Mining Metadata for new metadata

3 March 2006NaCTeM – Ray R. Larson Metadata as Infrastructure The difference between memorization and understanding lies in knowing the context and relationships of whatever is of interest. When setting out to learn about a new topic, a well-tested practice is to follow the traditional 5Ws and the H: Who?, What?, When?, Where?, Why?, and How?

4 March 2006NaCTeM – Ray R. Larson Metadata as Infrastructure The reference collections of paper-based libraries provide a structured environment for resources, with encyclopedias and subject catalogs, gazetteers, chronologies, and biographical dictionaries, offering direct support for at least What, Where, When, and Who. The digital environment does not yet provide an effective, and easily exploited, infrastructure comparable to the traditional reference library.

5 March 2006NaCTeM – Ray R. Larson What? Searching texts by topic, e.g. Dewey, LCSH, any subject index, or category scheme applied to documents. Two kinds of mapping in every search: Documents are assigned to topic categories, e.g. Dewey Queries have to map to topic categories, e.g. Deweys Relativ Index from ordinary words/phrases to Decimal Classification numbers. Also mapping between topic systems, e.g. US Patent classification and International Patent Classification.

6 March 2006NaCTeM – Ray R. Larson Texts What searches involve mapping to controlled vocabularies Thesaurus/ Ontology

7 March 2006NaCTeM – Ray R. Larson Start with a collection of documents.

8 March 2006NaCTeM – Ray R. Larson Classify and index with controlled vocabulary Or use a pre- indexed collection. Index

9 March 2006NaCTeM – Ray R. Larson Problem: Controlled Vocabularies can be difficult for people to use. pass mtr veh spark ign eng Index Use: Economic Policy In Library of Congress subj For: Wirtschaftspolitik

10 March 2006NaCTeM – Ray R. Larson Solution: Entry Level Vocabulary Indexes. Index EVI pass mtr veh spark ign eng = Automobile

11 March 2006NaCTeM – Ray R. Larson What and Entry Vocabulary Indexes EVIs are a means of mapping from users vocabulary to the controlled vocabulary of a collection of documents…

12 March 2006NaCTeM – Ray R. Larson Has an Entry Vocabulary Module been built? User selects a subject domain of interest. Download a set of training data. Build associations between extracted terms & controlled vocabularies. Map users query to ranked list of controlled vocabulary terms Part of speech tagging Use an existing EVI. Extract terms (words and noun phrases) from titles and abstracts. User selects search terms from the ranked list of terms returned by the EVI. YES Building an Entry Vocabulary Module (EVI) Searching For noun phrases Internet DB indexed with a controlled vocabulary. Domains to select from: Engineering, Medicine, Biology, Social science, etc. User has question but is unfamiliar with the domain he wants to search. NO Building and Searching EVIs

13 March 2006NaCTeM – Ray R. Larson Technical Details Download a set of training data. Build associations between extracted terms & controlled vocabularies. Part of speech tagging Extract terms (words and noun phrases) from titles and abstracts. Building an Entry Vocabulary Module (EVI) For noun phrases Internet DB indexed with a controlled vocabulary.

14 March 2006NaCTeM – Ray R. Larson Association Measure C ¬C t a b ¬t c d Where t is the occurrence of a term and C is the occurrence of a class in the training set

15 March 2006NaCTeM – Ray R. Larson Association Measure Maximum Likelihood ratio W(C,t) = 2[logL(p 1,a,a+b) + logL(p 2,c,c+d) - logL(p,a,a+b) – logL(p,c,c+d)] where logL(p,n,k) = klog(p) + (n – k)log(1- p) and p 1 = p 2 = p= a a+b c c+d a+c a+b+c+d Vis. Dunning

16 March 2006NaCTeM – Ray R. Larson Alternatively Because the evidence terms in EVIs can be considered a document, you can also use IR techniques and use the top-ranked classes for classification or query expansion

17 March 2006NaCTeM – Ray R. Larson Find Plutonium In Arabic Chinese Greek Japanese Korean Russian Tamil Statistical association Digital library resources

18 March 2006NaCTeM – Ray R. Larson EVI example EVI 1 Index term: pass mtr veh spark ign eng User Query Automobile EVI 2 Index term: automobiles OR internal combustible engines

19 March 2006NaCTeM – Ray R. Larson But why stop there? Index EVI

20 March 2006NaCTeM – Ray R. Larson Which EVI do I use? Index EVI Index EVI Index EVI

21 March 2006NaCTeM – Ray R. Larson EVI to EVIs Index EVI Index EVI Index EVI EVI 2

22 March 2006NaCTeM – Ray R. Larson Find Plutonium In Arabic Chinese Greek Japanese Korean Russian Tamil Why not treat language the same way?

23 March 2006NaCTeM – Ray R. Larson Texts Numeric datasets It is also difficult to move between different media forms Thesaurus/ Ontology EVI

24 March 2006NaCTeM – Ray R. Larson Searching across data types Different media can be linked indirectly via metadata, but often (e.g. for socio-economic numeric data series) you also need to specify WHERE to get correct results

25 March 2006NaCTeM – Ray R. Larson Texts Numeric datasets But texts associated with numeric data can be mapped as well… Thesaurus/ Ontology captions EVI

26 March 2006NaCTeM – Ray R. Larson EVI to Numeric Data example EVI LCSH marcnew query search results captions numeric table numeric database online catalog search interface 1 search interface

27 March 2006NaCTeM – Ray R. Larson Texts Numeric datasets But there are also geographic dependencies… Thesaurus/ Ontology captionsMaps/ Geo Data EVI

28 March 2006NaCTeM – Ray R. Larson WHERE: Place names are problematic… Variant forms: St. Petersburg, Санкт Петербург, Saint-Pétersbourg,... Multiple names: Cluj, in Romania / Roumania / Rumania, is also called Klausenburg and Kolozsvar. Names changes: Bombay Mumbai. Homographs:Vienna, VA, and Vienna, Austria; –50 Springfields. Anachronisms: No Germany before 1870 Vague, e.g. Midwest, Silicon Valley Unstable boundaries: 19th century Poland; Balkans; USSR Use a gazetteer!

29 March 2006NaCTeM – Ray R. Larson WHERE. Geo-temporal search interface. Place names found in documents. Gazetteer provided lat. & long. Places displayed on map. Timebar

30 March 2006NaCTeM – Ray R. Larson Zoom on map. Click on place for a list of records. Click on record to display text.

31 March 2006NaCTeM – Ray R. Larson Catalogs and gazetteers should talk to each other! Geographic sort / display of catalog search result. Catalog search Gazetteer search

32 March 2006NaCTeM – Ray R. Larson Texts Numeric datasets So geographic search becomes part of the infrastructure Thesaurus/ Ontology GazetteerscaptionsMaps/ Geo Data EVI

33 March 2006NaCTeM – Ray R. Larson WHEN: Search by time is also weakly supported… Calendars are the standard for time But people use the names of events to refer to time periods Named time periods resemble place names in being: –Unstable: European War, Great War, First World War –Multiple: Second World War, Great Patriotic War –Ambiguous: Civil war in different centuries in England, USA, Spain, etc. Places have temporal aspects & periods have geographical aspects: When the Stone Age was, varies by region

34 March 2006NaCTeM – Ray R. Larson Suggests a similar solution: A gazetteer- like Time Period Directory. Gazetteer: –Place name – Type – Spatial markers (Lat & long) -- When Time Period Directory: –Period name – Type – Time markers (Calendar) – Where Note the symmetry in the connections between Where and When. Similarity between place names and period names

35 March 2006NaCTeM – Ray R. Larson Solution - Time Period Directories Initial development involved mining the Library of Congress Subject Authority file for named time periods…

36 March 2006NaCTeM – Ray R. Larson LC MARC Authorities Records sh Magdeburg (Germany) History Siege, g Sieges Germany Work cat.: : Besselmeier, S. Warhafftige history vnd beschreibung des Magdeburgischen Kriegs, Cath. encyc. (Magdeburg: besieged ( ) by the Margrave Maurice of Saxony) Ox. encyc. reformation (Magdeburg:... during the siege of Magdeburg...)

37 March 2006NaCTeM – Ray R. Larson timePeriodEntry Time Period Directory Instance Contains components described below - periodID Unique identifier - periodName Period name, can be repeated for alternative names Information about language, script, transliteration scheme Source information and notes (where was the period name mentioned) - descriptiveNotes Description of time period - dates Calendar and date format Begin & end date (exact, earliest, latest, most-likely, advocated-by- source, ongoing) Notes, sources - periodClassification Period type, e.g. Period of Conflict, Art movement Can plug in different classification schemes Can be repeated for several classifications - location Associated places with time period Contains both place name and entry to a gazetteer providing more specific place information like latitude / longitude coordinates Can plug in different location indicators (e.g. ADL gazetteer, Getty Thesaurus of Geographic names) Recently added coordinates for direct use - relatedPeriod Related time periods periodID of related periods Information about relationship type (part-of, successor etc.) Can plug in different relationship type schemes - entryMetadata Notes about creator / creation of instance Entry date Modification date

38 March 2006NaCTeM – Ray R. Larson

39 March 2006NaCTeM – Ray R. Larson Time periods by named location

40 March 2006NaCTeM – Ray R. Larson Catalog Search Result

41 March 2006NaCTeM – Ray R. Larson Web Interface - Access by map

42 March 2006NaCTeM – Ray R. Larson Zoomable interface gives access to geographically focused info…

43 March 2006NaCTeM – Ray R. Larson Link initiates search of the Library of Congress catalog for all records relating to this time period. Web Interface - Access by timeline

44 March 2006NaCTeM – Ray R. Larson WHEN and WHAT These named time periods are derived from Library of Congress catalog subject headings and so can be used for catalog searching which finds books on topics important for that time period

45 March 2006NaCTeM – Ray R. Larson Texts Numeric datasets Time period directories link via the place (or time) Thesaurus/ Ontology GazetteerscaptionsMaps/ Geo Data EVI Time Period Directory Time lines, Chronologies

46 March 2006NaCTeM – Ray R. Larson 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.

47 March 2006NaCTeM – Ray R. Larson 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 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.

48 March 2006NaCTeM – Ray R. Larson Texts Numeric datasets A new form of biographical dictionary would link to all Thesaurus/ Ontology GazetteerscaptionsMaps/ Geo Data EVI Time Period Directory Time lines, Chronologies Biographical Dictionary

49 March 2006NaCTeM – Ray R. Larson 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 Text and ImagesBiographical DictionaryWHO TimelinesTime Period DirectoryWHEN MapsGazetteer WHERE Syndetic StructureThesaurusWHAT Special Display ToolsAuthority ControlFacet Learners Dossiers

50 March 2006NaCTeM – Ray R. Larson Acknowledgements Electronic Cultural Atlas Initiative project This work was partially supported by the Institute of Museum and Library Services through a National Leadership Grant for Libraries, award number LG , Oct Sept 2006 entitled Supporting the Learner: What, Where, When and Who – See: Michael Buckland, Fred Gey, Vivien Petras, Matt Meiske, Kim Carl Contact:


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