a practical ontology for the large-scale modeling of scholarly artifacts and their usage

QuickTime™ and aTIFF (LZW) decompressorare needed to see this picture.Marko A. Rodriguez, Johan Bollen, Herbert Van de Sompel

JCDL 2007, June 18-23 2007, Vancouver, Canada

A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and their Usage

Marko A. Rodriguez (1)

Johan Bollen

Herbert Van de Sompel

Digital Library Research & Prototyping Team

Los Alamos National Laboratory - Research Library

(1) [email protected]

Acknowledgements:

Lyudmila L. Balakireva (LANL), Wenzhong Zhao (LANL), Aric Hagberg (LANL)

MESUR is supported by the Andrew W. Mellon Foundation.



Overview

The MESUR project

A quick RDF/RDFS/OWL tutorial

Modeling the scholarly community

Practical applications of the model

Conclusion



What is the MESUR project?

• MEtrics from Scholarly Usage of Resourceso http://www.mesur.org

• The MESUR project is currently gathering publication, citation, and usage data from providers world-wide in order to engineer a large-scale scholarly model.

o Publication and citation data from bibliographic databases.o Usage data logged by institutions, publishers, and aggregators.



Journal and Article data

• Journal-level Bibliographic Datao Thomson Scientific JCR: > 8,000 indexed journalso Thomson Scientific JCR: > 50,000,000 journal citationso Thomson Scientific JCR: > 100,000 journal classificationso Ex Libris SFX Journal Master List: > 300,000 journals identifiers (title,

abbreviated title, ISSN, eISSN) clustered in groupso Ex Libris SFX: > 85,000 journal classifications

• Article-level Bibliographic Datao Thomson Scientific Citation Databases : > 37,500,000 articleso Thomson Scientific Citation Databases: > 550,000,000 article citations



Usage data

• Institutions (link resolvers, proxies)o Los Alamos: > 350,000 1-yearo CalState: > 3,500,000 2-yearso UTexas: > 2,500,000 5-yearso PennState: …o …

• Aggregatorso anonymous: > 2,500,000 1-yearo anonymous: > 50,000,000 1-weeko …

• Publisherso BioMed Central: > 24,000,000 2-yearso Elseviero …



The Primary Data Representation

• So how are we going to represent all this data in one model such that this model can analyzed computationally?

ANSWER: A semantic network.

• Why are we using a semantic network?o Able to represent a heterogeneous set of entities related to one

another by a heterogeneous set of relationships.o All “actors” are represented in the same substrate.o Existing technologies and standards to support the representation:

triples-stores, RDF, semantic network analysis algorithms, etc.

A BC



Example Scholarly Relationships

• <marko, wrote, practical_ontology>

• <johan, wrote, practical_ontology>

• <herbertv, wrote, practical_ontology>

• <practical_ontology, publishedIn, jcdl>

• <practical_ontology, cites, rdf_specification>

• <rdf_specifcation, downloadedBy, 127.0.0.1>

• <127.0.0.1, from, LANL>

• <LANL, contains, herbertv>

• <herbertv, coauthorsWith, marko>



What is the Purpose of an Ontology?



The MESUR Data Flow



Overview

The MESUR project




Conclusion



RDF, RDFS, OWL

• The Resource Description Frameworko A data model for representing a semantic network. URIs connected to

one another by a URI. <lanl:marko, lanl:worksWith, lanl:johan>

• The Resource Description Framework Schemao A simple ontology language for defining classes and their relationships

to one another. (provides basic class hierarchy construction)

• The Web Ontology Languageo A more advanced ontology language. (this is the ontology language

used in MESUR)



RDF, RDFS

ex:marko ex:cookie

ex:Human ex:Food

ex:isEatingrdf:type rdf:type

ex:isEating

rdfs:domainrdfs:range

ontology

instance



RDF, RDFS, OWL

ex:fluffy ex:marko

ex:Pet ex:Human

ex:hasOwnerrdf:type rdf:type

ex:hasOwner

rdfs:domain rdfs:range

ontology

instance

_:0123

rdfs:subClassOf

owl:onProperty

“1”owl:maxCardinality

ex:bobex:hasOwner

owl:Restrictionrdf:type



The Triple Store

SELECT ?a ?c WHERE ( ?a type human ) ( ?a wrote ?b ) ( ?b type article ) ( ?c wrote ?b ) ( ?c type human ) ( ?a != ?c )

• The triple store is to semantic networks what the relational database is to data tables.

• Storing and querying triples in a triple store

• SPARQL query languageo like SQL, but for triple stores



Overview

The MESUR project




Conclusion



The Problem of Scale

• High-end triple stores reasonably support 1+ billion triples.

• The MESUR solution is to not include all artifact metadata in the triple store.

• MESUR leverages relational database and triple store technology.o The triple store is for relationships.o The relational database is for metadata.



Relational Database & Triple Store



The MESUR Class Hierarchy



The Context Classes

Inspired by OntologyX: http://www.ontologyx.com



The Publishes Context



The Uses Context



Overview

The MESUR project




Conclusion



Analysis Algorithms

• ISI Impact Factor

• Usage Impact Factoro Bollen J., Van de Sompel, H., “Usage Impact Factor: The Effects of Sample Characteristics on

Usage-based Impact Metrics”, [in review], 2007.

• H-Indexo Hirsh, J.E., “An index to quantify an individual's scientific research output”, Proceedings of the

National Academy of Science, 102:46, 2005.

• Y-Factoro Bollen J., Rodriguez, M.A., Van de Sompel, H., “Journal Status”, Scientometrics, 69:3, 2006.

• Other social network metricso Eccentrity, Betweenness, Closeness, PageRank, …



Journal Citation and Usage



Calculating the 2007 Impact Factor

SELECT ?xWHERE

( ?x rdf:type mesur:Citation )( ?x mesur:hasSource ?a)( ?x mesur:hasSink urn:issn:0028-0836 )( ?x mesur:hasSourceTime ?u) AND

(?u == 2007)( ?x mesur:hasSinkTime ?t) AND

(?t > 2004 AND ?t < 2007)

SELECT ?yWHERE

( ?y rdf:type mesur:Publishes )( ?y mesur:hasGroup urn:issn:0028-0836 )( ?y mesur:hasTime ?t ) AND

(?t > 2004 AND ?t < 2007)

INSERT < _123 rdf:type mesur:ImpactFactor >INSERT < _123 mesur:hasObject urn:issn:0028-0836 >INSERT < _123 mesur:hasStartTime 2007 >INSERT < _123 mesur:hasEndTime 2007 >INSERT < _123 mesur:hasNumbericValue

(COUNT(?x) / COUNT(?y)) >

The 2007 impact factor of journal A is the total number of citations to articles published in A in 2005 and 2006 from articles published in 2007 in journal Bdivided by the total number of articles published by journal A in 2005 and 2006.



Calculating the 2007 Usage Impact Factor

SELECT ?xWHERE

( ?x rdf:type mesur:Uses ) ( ?x mesur:hasUnit ?a )( ?x mesur:hasGroup ?b )( ?b mesur:partOf urn:issn:1082-9873 )( ?x mesur:hasTime ?t ) AND

(?t == 2007)( ?y rdf:type mesur:Publishes )( ?y mesur:hasUnit ?a )( ?y mesur:hasTime ?u ) AND

(?u > 2004 AND ?u < 2007)

SELECT ?yWHERE

( ?y rdf:type mesur:Publishes )( ?y mesur:hasGroup ?a )( ?a mesur:partOf urn:issn:1082-9873 )( ?y mesur:hasTime ?t ) AND

(?t > 2004 AND ?t < 2007)

INSERT < _123 rdf:type mesur:UsageImpactFactor >INSERT < _123 mesur:hasObject urn:issn:1082-9873 >INSERT < _123 mesur:hasStartTime 2007 >INSERT < _123 mesur:hasEndTime 2007 >INSERT < _123 mesur:hasNumbericValue

(COUNT(?x) / COUNT(?y)) >

The 2007 usage impact factor of journal A is the total number of 2007 usage events of articles published in A in 2005 and 2006 divided by the total number of articles published by journal A in 2005 and 2006.



Overview

The MESUR project




Conclusion



Contributions

• Uniting the RDF/Semantic Web community technology with scholarly modeling.

o Semantic network model of the scholarly community.

• Architectural set-up supports a massive data seto Triple-store/relational database coupling.

• Open ontology for the scholarly communityo Open source ontology to represent many aspects of the scholarly

communication process including publication, citation, and usage.



Some Related Publications

Marko A. Rodriguez, Johan Bollen and Herbert Van de Sompel. A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and their Usage, In Proceedings of the Joint Conference on Digital Libraries, Vancouver, June 2007

Marko A. Rodriguez. Grammar-based random walkers in semantic networks. (LAUR-06-7791)

Marko A. Rodriguez and Jennifer H. Watkins. Grammar-based geodesics in semantic networks. (LAUR-07-4042)

Johan Bollen and Herbert Van de Sompel. Usage Impact Factor: the effects of sample characteristics on usage-based impact metrics. (arxiv.org: cs.DL/0610154)

Johan Bollen and Herbert Van de Sompel. An architecture for the aggregation and analysis of scholarly usage data. In Joint Conference on Digital Libraries (JCDL2006), pages 298-307, June 2006.

Johan Bollen and Herbert Van de Sompel. Mapping the structure of science through usage. Scientometrics, 69(2), 2006.

Johan Bollen, Marko A. Rodriguez, and Herbert Van de Sompel. Journal status. Scientometrics, 69(3), December 2006 (arxiv.org:cs.DL/0601030)

Johan Bollen, Herbert Van de Sompel, Joan Smith, and Rick Luce. Toward alternative metrics of journal impact: a comparison of download and citation data. Information Processing and Management, 41(6):1419-1440, 2005.



Questions

MESUR is at http://www.mesur.org

MESUR ontology is at http://www.mesur.org/schemas/2007-01/mesur/

Many thanks to the Andrew W. Mellon Foundation for their support

a practical ontology for the large-scale modeling of scholarly artifacts and their usage

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