Helping Interdisciplinary Vocabulary Engineering (HIVE)

OCTOBER 31, 2011

Joan BooneNico CarverJane GreenbergLina HuangRobert LoseeMady MadhuraJosé Ramón Pérez AgüeraLee Richardson Ryan ScherleTodd VisionHollie WhiteCraig Willis

OverviewOverviewPart 1Introduction to HIVEUnderlying rationale A scenarioResearch and challenges

Part 2Technical overview and implementationProgress and challengesNext steps

Part 3Let you experiment

HIVE HIVE TeamTeam

Craig Willis

Bob LoseeLee Richardson

Hollie WhiteJane Greenberg

Madhura Marathe

Lina Huang

José R. P. Agüera

Ryan Scherle

4

HIVE modelHIVE model

<AMG> approach for integrating discipline CVs Model addressing C V cost, interoperability, and usability constraints (interdisciplinary environment)

5

Data underlying peer-reviewed articles in the basic and applied biosciences

• Vocabulary analysis – 600 keywords, Dryad partner journals

• Vocabularies: NBII Thesaurus, LCSH, the Getty’s TGN, ERIC Thesaurus, Gene Ontology, IT IS (10 vocabularies)

• Facets: taxon, geographic name, time period, topic, research method, genotype, phenotype…

• Results431 topical terms, exact matches– NBII Thesaurus, 25%; MeSH, 18%531 terms (topical terms, research method and taxon)– LCSH, 22% found exact matches, 25% partial

• Conclusion: Need multiple vocabularies

Vocabulary needs for Vocabulary needs for DryadDryad

1. Provide efficient, affordable, interoperable, and user friendly access to multiple vocabularies during metadata creation activities

2. Present a model and an approach that can be replicated

—> not necessarily a service

1. Building HIVEVocabulary preparationServer development

2. Sharing HIVEContinuing education (empowering information professionals)

3. Evaluating HIVEExamining HIVE in Dryad

HIVE work-planHIVE work-plan3 PhasesHIVE Goals

HIVE PartnersHIVE PartnersVocabulary

Partners Library of Congress: LCSH

the Getty Research Institute (GRI): TGN (Thesaurus of Geographic Names )

United States Geological Survey (USGS): NBII Thesaurus, Integrated Taxonomic Information System (ITIS)

National Library of Medicine and the National Agricultural Library

Advisory Board Jim Balhoff, NESCent Libby Dechman, LCSH Mike Frame, USGS Alistair Miles, Oxford, UK William Moen, University of North Texas Eva Méndez Rodríguez, University

Carlos III of Madrid Joseph Shubitowski, Getty Research

Institute Ed Summers, LCSH Barbara Tillett, Library of Congress Kathy Wisser, Simmons Lisa Zolly, USGS

WORKSHOPS HOSTS: Columbia Univ.; Univ. of California, San Diego; George Washington University; Univ. of North Texas; Universidad Carlos III de Madrid, Madrid, Spain

HIVE is for…HIVE is for…

HIVE for resource creators- w/Dryad: scientists, depositors

HIVE for information professionals: curators, professional librarians, archivists, museum catalogers

~~~~Amy~~~~Amy

• Meet Amy Zanne. She is a botanist.

• Like every good scientist, she publishes, and she deposits data in Dryad.

Amy’s dataAmy’s data

UsabilityUsability

Huang, 2010

Huang, 2010

System usability and flow System usability and flow metricsmetrics

Huang, 2010

ChallengesChallenges Building vs. doing/analysis

• Source for HIVE generation, beyond abstracts Combining many vocabularies during the indexing/term

• matching phase is difficult, time consuming, inefficient.• NLP and machine learning offer promise

Interoperability = dumbing down • ontologies

Proof-of-concept/ illustrate the differences between HIVE and other vocabulary registries (NCBO and OBO Foundry)

People wanting a service General large team logistics, and having people from

multiple disciplines (also the ++)

HIVE Technical OverviewCraig Willis (craig.willis@unc.edu)

CreditsCredits Ryan Scherle (Nescent)

José Ramón Pérez Agüera (UNC)

Lina Huang (UNC)

Duane Costa (LTER)

Alyona Medelyan & Ian Whitten (Univ. of Waikato/NZDL)

HIVE Technical OverviewHIVE Technical Overview HIVE combines several open-source technologies to provide

a framework for vocabulary services.

Java-based web services can run in any Java application server

Demonstration website (http://hive.nescent.org/)

Open-source Google Code project (http://code.google.com/p/hive-mrc/)

Source code, pre-compiled releases, documentation, mailing lists

Who’s using HIVE?Who’s using HIVE?HIVE is being evaluated by several institutions and organizations:

Long Term Ecological Research Network (LTER) Prototype for keyword suggestion for Ecological Markup Language

(EML) documents.

Library of Congress Web Archives (Minerva) Evaluating HIVE for automatic LCSH subject heading suggestion for

web archives.

Dryad Data Repository Evaluating HIVE for suggestion of controlled terms during the

submission and curation process. (Scientific name, spatial coverage, temporal coverage, keywords).

Yale University, Smithsonian Institution Archives

HIVE FunctionsHIVE Functions System for management of multiple controlled vocabularies

in SKOS format

Single interface for browsing, searching, and indexing using multiple vocabularies.

Natural language and structured (SPARQL) queries

Rich internet application (RIA) demonstration interface

Java API and REST interfaces for programmatic access

Framework for conversion of vocabularies to SKOS

HIVE ComponentsHIVE Components HIVE Core API

Java API for management of HIVE vocabularies.

HIVE Web Service

Google Web Toolkit (GWT) based interface to demonstrate the HIVE service. Includes Concept Browser and Indexer.

HIVE REST API

RESTful API developed by Duane Costa of the Long Term Ecological Research Network (LTER)

Supporting TechnologiesSupporting Technologies Sesame: Open-source triple store and framework for

storing and querying RDF data

Used for primary storage, structured queries

Lucene: Java-based full-text search engine

Used for keyword searching, autocomplete (version 2.0)

H2: Embedded relational database

Stores administrative data, fast concept index, KEA++ lookup tables.

KEA++: Algorithm and Java API for automatic indexing

ArchitectureArchitecture

Converting Vocabularies to Converting Vocabularies to SKOSSKOS

“We learned that some thesauri have complex structures for which no SKOS counterparts can be found and that for some features care is required in converting them in such a way that they are still usable for their original purpose.”

Van Assem, Mark. (2010). Converting and Integrating Vocabularies for the Semantic Web. Unpublished dissertation.

Converting Vocabularies to Converting Vocabularies to SKOSSKOS

SKOS does not fit all vocabularies/thesauri

For example, MeSH Is a MeSH descriptor a SKOS Concept?

“A Method to Convert Thesauri to SKOS” (van Assem et al) http://thesauri.cs.vu.nl/eswc06/

Or is a MeSH concept a SKOS concept? “Converting MeSH to SKOS for HIVE”

http://code.google.com/p/hive-mrc/wiki/MeshToSKOS

Either way, information is lost about the vocabulary

Converting Vocabularies to Converting Vocabularies to SKOSSKOS

Additional information http://code.google.com/p/hive-mrc/wiki/VocabularyConversion

Each vocabulary has different requirements

AGROVOC Available in SKOS

ITIS Convert from RDB (MySQL) to SKOS RDF/XML

LCSH Available in SKOS

MeSH Convert from XML to SKOS RDF/XML (SAX)

NBII Convert from XML to SKOS RDF/XML (SAX)

TGN Convert from flat-file to SKOS RDF/XML

KEA++ for Keyphrase KEA++ for Keyphrase ExtractionExtraction

Algorithm and open-source Java library for extracting keyphrases from documents using SKOS vocabularies.

Domain-independent machine learning approach with minimal training set (~50 documents).

Leverages SKOS relationships and alternate/preferred labels

Developed by Alyona Medelyan (KEA++), based on earlier work by Ian Whitten (KEA) University of Waikato, New Zealand (http://www.nzdl.org/Kea/)

(Expanded implementation in Medelyan’s MAUI)Medelyan, O. and Whitten I.A. (2008). “Domain independent automatic keyphrase indexing with small training sets.”

Journal of the American Society for Information Science and Technology, (59) 7: 1026-1040).

KEA++: Feature KEA++: Feature definitiondefinition

Term Frequency/Inverse Document Frequency: Frequency of a phrase’s occurrence in a document with frequency in general use.

Position of first occurrence: Distance from the beginning of the document. Candidates with high/low values are more likely to be valid (introduction/conclusion)

Phrase length: Analysis suggests that indexers prefer to assign two-word descriptors

Node degree: Number of relationships between the term in the CV.

(MAUI expands feature set)Medelyan, O. and Whitten I.A. (2008). “Domain independent automatic keyphrase indexing with small training sets.” Journal of the American Society for Information Science and Technology, (59) 7: 1026-1040).

Medelyan, O. (2010). Human-competitive automatic topic indexing. Unpublished dissertation.

HIVE – UpcomingHIVE – Upcoming Vocabulary synchronization

Integration of HIVE with LCSH Atom Feed (http://id.loc.gov/authorities/feed/)

Integration and evaluation of alternative algorithms As part of the Dryad/HIVE integration Questions:

What is the best algorithm for automatic term suggestion for Dryad vocabularies?

Do different algorithms perform better for title, abstract, full-text, data?

Do different algorithms perform better for a particular vocabulary/taxonomy/ontology?