# Presentation: 648672

## Mapping Metadata Across Genres and Languages

**XDMR Project Meeting, July 12, 2005**
**Fredric C. Gey, ****University of California, Berkeley**
**Colleagues:  ****Michael Buckland, Aitao Chen, Ray Larson**
**Programmers and Students: ****Barbara Norgard, Natalia Perelman **
**Work performed under **
**DARPA research contract : *****Search support for unfamiliar metadata vocabularies (1997-2001)***
**DARPA TIDES research grant: ****Translingual Vocabulary Mappings using Domain Ontologies (1999-2004)**
**Institute for Museum and Library Services Grant: ***** Seamless search of textual and numeric databases (1999-2001), others (2002-2006)***

- Fredric C. Gey

## HETEROGENEOUS DIGITAL INFORMATION SEARCHCurrent Search Technology (multiple independent searches without search aids)

**Bibliography**
**Full Text**

**Maps and other Geospatial data**

**Music and other media**

**QUERY**

- Mapping Metadata Across Genres and Languages

## SEARCHING UNFAMILIAR METADATA: PROBLEM STATEMENT

**SEARCHING UNFAMILIAR METADATA: ****PROBLEM STATEMENT**

**Numerous databases are indexed by structured metadata classifications**
**Classification schemes are highly specialized**
**As digital libraries multiply in size and diversity, there is a need for search engines for non-specialists**
**Search engines should translate from ordinary language to specialized classifications**

**“****computer”**

**In U.S. Import-Export Database**

**No result found**

- Mapping Metadata Across Genres and Languages

## Searching Statistical Information: Problem statement

**Numeric statistical information is often thinly documented or documented with specialized vocabularies**
**Census:  ****welfare -->**** ****public assistance**
**Foreign trade harmonized commodity classification:  ****computer --> digital adp machine**
**Standardized Industrial Classification (SIC codes): ****automobile -->  motor vehicle**
**In search, the user’s ordinary language term is unlikely to match the limited technical vocabulary used to document the statistical resource**
**Can we remedy this situation? **

- Fredric C. Gey

- Mapping Metadata Across Genres and Languages

## Searching Statistical Information: Evidence Poor Data

**Compared to searching textual databases, numeric statistical information and its terminology is both ****evidence-poor**** and highly technical**
**Statistical databases ****lack**** the rich set of ****textual clues**** which can identify items of numeric information**
**However, if we can find a textual resource associated with the numeric metadata, we may be able to ****mine the text**** to improve numeric search.**
**This is possible for some numeric classification schemes**

- Fredric C. Gey

- Mapping Metadata Across Genres and Languages

## SEARCHING  UNFAMILIAR METADATA: EXAMPLES FROM FOREIGN TRADE IMPORTS-EXPORTS

**SEARCHING  UNFAMILIAR METADATA: ****EXAMPLES FROM FOREIGN TRADE IMPORTS-EXPORTS**

**U.S. Foreign Trade Imports-Exports**
**Two CD-ROMS/month**
**Classified by 16,000 numeric commodity codes in 12 digit hierarchy**
**Search for ‘automobile’ or ‘computer’ yields no result**
**Search for ‘Car’ yields “Railway or Tramway Stock, Etc.”**
**Need to know classifications “Passenger Motor Vehicle” or “Digital ADP Machine w/CPU”**

**“****Car”**

**In U.S. Import-Export Database**

**“****Railway or Tramway Stock”**

- Mapping Metadata Across Genres and Languages

## SEARCHING  UNFAMILIAR METADATA: U.S. STANDARD INDUSTRIAL CLASSIFICATION SYSTEM

**SEARCHING  UNFAMILIAR METADATA: ****U.S. STANDARD INDUSTRIAL CLASSIFICATION SYSTEM**

**U.S. Standard Industrial Classification System (SIC)**
**Used to classify and aggregate industrial activity in the U.S. **
**Codes defined by Office of Management and Budget**
**County Business Patterns reports annual employment, payroll, firm size by county, SIC code**

**“****Lobster”**

**In U.S. SIC System**

**“****Nothing found”**

- Mapping Metadata Across Genres and Languages

## Searching Statistical Information: Economic Classification Codes

**Standard Industrial Classification (****SIC****) and North American Industrial Classification (****NAICS****) codes ****have been used to index trade magazines **
**This provides a ****textual resource**** of hundreds of thousands of documents and millions of words ****associated with the numeric data****.**
**Thus mappings can be made ****between the words**** from the magazines abstracts ****and the classifications**
**User queries can be matched against words and phrases ****most closely associated**** with the particular numeric data classification**
**A ranked list of classifications can be displayed to the user in order to improve the search**
**Harmonized commodity classifications**** can be searched using SIC codes as a search proxy**

- Fredric C. Gey

- Mapping Metadata Across Genres and Languages

## SEARCHING  UNFAMILIAR METADATASOLUTION -- ENTRY VOCABULARY TECHNOLOGY

**Maps between ordinary language and specialized classifications**
**Implemented using text categorization techniques**
**Requires training collections which have been manually indexed**
**Preserves and leverages investment in creation of complex classification structures**
**Can also be applied to the task of multi-lingual information access**

**For: “Automobile”**

**In U.S. Patent Database**

**Try: 180/280**

- Mapping Metadata Across Genres and Languages

## UNFAMILIAR METADATA PROJECT : OVERVIEW

**Unfamiliar**
**Metadata**

**UNFAMILIAR METADATA PROJECT : **_**OVERVIEW**_

**Provides entry vocabulary modules for searching unfamiliar metadata (text, Patents, Standard Industrial Classification)**
**Fundamental method uses Bayesian inference based upon training data (documents) to map between ordinary language and complex hierarchical metadata structures**
**Technique can be applied to searching poorly documented statistical information**

- Mapping Metadata Across Genres and Languages

## SEARCHING  UNFAMILIAR METADATA: ENTRY VOCABULARIES CONSTRUCTED

**Ordinary language to U.S. Patent Classification**
**Ordinary language to INSPEC thesaurus terms**
**Ordinary language to Library of Congress classification codes**
**Ordinary language to Standard Industrial Classification system**
**Experience in entry vocabulary technology (training set size, data cleaning, natural language)**
**Ordinary Language to NAICS with link to 1997 Economic Census**

**For: “Lobster”**

**In U.S. SIC classification**

**Try: “Shellfish”**

- Mapping Metadata Across Genres and Languages

## Translingual Vocabulary Mappings Using Digital Library Catalogs(Berkeley work with Aitao Chen, Ray Larson, Michael Buckland)

**Applies  digital  library multilingual catalogs  (books, maps, pictures) ****in 400+ languages ****to develop multilingual vocabulary lexicons**
**8,000 Afghan(Pushto) words mapped to English in 30 minutes**
**20,000 word corpus of  Afghan(Pushto)  text in 3 hours**
**Records contain ****one or more sentences**** from a particular language**
**Each is ****categorized by human indexers**** into standardized schemes**
**Library of Congress Subject Heading (****Islamic Fundamentalism****)**
**International standard ****(MARC)**** dataset formatting**
**Prototype implemented using 12 million records from the University of California electronic catalog (****http://melvyl.ucop.edu****)**

- Mapping Metadata Across Genres and Languages

## Search: SUBJECT “Islamic Fundamentalism” and LANGUAGE “Arabic”

**Yield: ****119 Arabic language samples on topic  “Islamic Fundamentalism”**

## BACKGROUND ON DIGITAL LIBRARY CATALOGS

**Library catalogs are being automated at a furious pace worldwide**
**Library objects (books, maps, pictures) ****in 400+ languages**
**Records contain ****one or more sentences**** from a particular language**
**All ****categorized by specialists**** into standardized schemes**
**Library of Congress Subject Heading (****Islamic Fundamentalism****)**
**Library of Congress Classification (****BP60, BP63, KF27****)**
**Dewey Decimal Classification (****297.2, 306.6, 320.5****)**
**International standard**** (MARC) ****dataset formatting**
**1000+ (est.) remotely searchable catalogs worldwide accessible using the ****international**** ****search/retrieve**** ****protocol Z39.50**

- Mapping Metadata Across Genres and Languages

## WHAT CAN DIGITAL LIBRARY CATALOGS  PROVIDE?

**Millions of sentence fragments (titles).  ****Many languages**
**Sentences with precise ****topical content identified**
**From ****150,000**** Library of Congress ****Subject Headings**
**Subject headings form transfer point (interlingua) between English topics and words in other languages**
**Can be used for:**
**Query translation**** in cross-language information retrieval**
**Creation of ****bilingual dictionaries**

- Mapping Metadata Across Genres and Languages

## BERKELEY’S TECHNOLOGY – VOCABULARY INDEXES

**Takes large training sets of digital objects (book/article titles)**
**Software/algorithms perform ****maximum likelihood mappings**** between words/phrases and subject headings**
**Search software accepts words and returns subject headings and vice versa**
**Prototype search software maps from/to English and**
**Arabic, Chinese, French, German**
**Italian, Japanese, Russian, Spanish**
**currently uses romanized search for non-roman scripts**
**currently phrases in English only**
**Language-specific and/or domain-specific **
**Our prototype for nine languages available at:**
- _[http://otlet.sims.berkeley.edu/mulevm2.html](http://otlet.sims.berkeley.edu/mulevm2.html)_

- Mapping Metadata Across Genres and Languages

## BERKELEY TRAINING SET AND PROTOTYPE

**University of California digital library catalog**
**Private copy, ****10 million+ records (5 million non-English)**
**Records in over 100 languages**
**Obtained in MARC database standard format**
**Foreign language titles use Library of Congress transliteration (Romanization) standard**
**Prototype search software maps from/to English and**
**Arabic, Chinese, French, German**
**Italian, Japanese, Russian, Spanish**

- Mapping Metadata Across Genres and Languages

## Example: Library of Congress Subject Heading  “Islamic Fundamentalism”  yields most closely associated words in multiple languages

**Example: Library of Congress Subject Heading  “Islamic Fundamentalism”  yields most closely associated words in multiple languages**

- Mapping Metadata Across Genres and Languages

## LIBRARY RESOURCES vs. WEB RESOURCES

_**LIBRARY CATALOGS**_
**Databases in standard formats**
**Data tagged with rich metadata**
**Database size limited**
**Data content identified according to well-defined rules**

_**WEB PAGE MINING**_
**Data in quite varied formats**
**Limited or non-existent metadata**
**Database size potentially massive**
**Content rarely identified**

- Mapping Metadata Across Genres and Languages

## Foreign search words are mapped to English subject headings

**Russian-German bilingual dictionary entries:**
** ****Perevod – Ubersetzung  |  Ubersetzung --  Perevod **

- Mapping Metadata Across Genres and Languages

## Translingual Vocabulary Indexes, Place Names, Stemming & CLIR for the TIDES Surprise Language  Exercise Fredric Gey, Aitao Chen, Vivien Petras (University of California, Berkeley)

**Library catalog records**:
**Hindi  ****	****72,967 records (****44,366 ****	****	****	****Berkeley****, 28,601 UK)**
**Urdu****	****30,206**
**Bengali ****	****23,430**
**Tamil ****	****20,232**
**Cebuano****	****16 ****(300 est. worldwide)**
***Day 1******:***** *****English-Hindi vocabulary index***** **_[http://metadata.sims.berkeley.edu/Evms/cdl2/hindi2/weblcsh_hindi.html](http://metadata.sims.berkeley.edu/Evms/cdl2/hindi2/weblcsh_hindi.html)_
**Terrorism --****atankavada** – आतंकवाद
**Day 5:**** 10,839 English words mapped to (transliterated)  Hindi  (only 492 unique)**
**Unable to resolve LOC back transliteration**
***Day 2:***** 178 *****city names***** from Indian Census, hand translated to Hindi by ISI, lat/lon by  Beth Sundheim of SPAWAR. **
***Day 8:*****   5,161**** city and village names**
***Day 3:***** *****Hindi Monolingual UTF-8 Retrieval***
***Day 6:***** added ****ITRANS transliteration**
***Day 18******:***** *****English-Hindi CLIR***** system**

***Day 24******:***** Berkeley’s***** statistical stemmer***** clusters Hindi words to English stems**
**– ****Derived from the IBM/ISI lexicons**
***CLIR evaluation***** participation**
**10 runs submitted, all automatic**
**Results very satisfactory**
**Also: *****Geographic visualization***** for stories associated with cities**
_***Lessons learned:***_
**World-wide**** library ****retrieval**** (using Z39.50 protocol) ****can increase coverage**
**Data limited by language density, timeliness**
**Back-transliteration ****of scripted languages ****needs further research**
**Geographic visualization useful**

- Mapping Metadata Across Genres and Languages

## HETEROGENEOUS DIGITAL INFORMATION SEARCHEnhanced Search (augmented with Entry Vocabulary Module (EVM) Technology)

**QUERY**

**HETEROGENEOUS DIGITAL INFORMATION SEARCH****Enhanced Search (augmented with Entry Vocabulary Module (EVM) Technology)**

**EVMp**

**EVMm**

**EVMs**

**EVMt**

**EVMg**

**QUERYplus**

- Mapping Metadata Across Genres and Languages

## HETEROGENEOUS DIGITAL INFORMATION SEARCHDirect Mappings and Search Between  Multiple Information Types

**QUERY**

**HETEROGENEOUS DIGITAL INFORMATION SEARCH****Direct Mappings and Search Between  Multiple Information Types**

**EVMs**

**EVMm**

**EVMp**

**EVMt**

**EVMg**

**QUERYplus**

- Mapping Metadata Across Genres and Languages

## Geographic Visualization of Hindi Stories(Electronic Cultural Atlas Initiative, University of California)

- Mapping Metadata Across Genres and Languages

## TWO RELEVANT STANDARDS under development

**SDMX**** – Statistical Data and Metadata Exchange **
**EC, **
**ISO Standard, Technical Committee 154**
**ISO/TS 17369:2005 SDMX**
- _[www.sdmx.org](http://www.sdmx.org/)__ _
**DDI**** – Data Documentation Initiative**
**XML standard for documenting statistical dataset for archiving**
**International committee of statistical data archivists**
**Coordinated by national archive at University of Michigan**
**Version 2.0 approved May**
**Working on V 3.0 to include detailed geography**
- _[www.icpsr.umich.edu/DDI](http://www.icpsr.umich.edu/DDI)__ _

- Mapping Metadata Across Genres and Languages

## SEARCHING  UNFAMILIAR METADATA: Web Site and PI Contact Information

**www.sims.berkeley.edu/research/metadata**
**www.sims.berkeley.edu/research/seamless**
**Michael Buckland and Ray Larson (buckland, ray @sims.berkeley.edu)**
**Fredric Gey (gey@berkeley.edu)**

- Mapping Metadata Across Genres and Languages