Hanmin Jung(KISTI)

1. Generating Researcher Networks
with Identified Persons
on a Semantic Service Platform
15 Sep. 2009
Hanmin Jung
KISTI
BlogTalk2009 1 Copyright © 2004-2009, KISTI

2. Agenda
Research networks would be useful for finding
Collaborators
Speakers (Key persons of a researcher group)
Issues
Getting sources
Resolving identities
Finding experts
Generating networks
BlogTalk2009 2 Copyright © 2004-2009, KISTI

3. Getting sources …
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4. Sources
Identified Entities
Papers: 453,124
Elsevier international journal papers with full-texts and metadata
Persons: 1,352,220
Topics: 339,947
Institutions: 91,514
Locations: 409,575 (with GPS coordinate)
RDF Triples: 283,087,518 (2008.11)
BlogTalk2009 4 Copyright © 2004-2009, KISTI

5. Resolving identities … How to resolve identities?
How to merge different identifiers as one?
BlogTalk2009 5 Copyright © 2004-2009, KISTI

6. OntoFrame
OntoFrame 2008 Service
WS API WS API/SPARQL
XML
Ontology Search Engine
Ontologies XML
Schemata
OntoReasoner®
OntoURI®
WS API
Triple
Legacy DB Table Field Listener Generator
SQL/
Information Expanded Triples
…
WS API Answers
Ontology
Field Instances
Information
Legacy DB Table
DB Tables
RDF Triple Store
BlogTalk2009 6 Copyright © 2004-2009, KISTI

7. Ontology
Reference and Academic Knowledge Ontologies
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8. OntoFrame
Syntactic-to-Semantic Process
Design Ontology Model
Design Ontology Model Edit URI Generation Rules
Edit URI Generation Rules
Modeling-Time Select Database & Ontology Edit Identity Resolution Rules
Select Database & Ontology Edit Identity Resolution Rules
Process
Edit Mapping Rules
Edit Mapping Rules Test Mapping Process
Test Mapping Process
Normalize Field Values
Normalize Field Values Crawl Database
Crawl Database
Apply Identity Resolution Rules
Apply Identity Resolution Rules Refer Authority Data
Refer Authority Data
Indexing-Time
Resolve Identities
Resolve Identities Extract Topics
Extract Topics
Process
Assign URIs
Assign URIs Apply Mapping Rules
Apply Mapping Rules
Apply URI Generation Rules
Apply URI Generation Rules Generate RDF Triples
Generate RDF Triples
Run-Time
Process Apply sameAs Relations
Apply sameAs Relations
BlogTalk2009 8 Copyright © 2004-2009, KISTI

9. Identity Resolution
case 1 case 2
Barry G.T. Barry Christian Christian
Lowden Lowden Becker Becker
BlogTalk2009 9 Copyright © 2004-2009, KISTI

10. Identity Resolution
Rules for Resolving Personal Identities
Class Resource Kind Match Relation Source Weight
Person Order 1
Person Name Pivot Exact Single OntoURI
Person hasInstitution Feature Exact Single OntoURI 2
Person Email Feature Number Single 4
Person hasCoauthor Feature Number Multiple OntoReasoner 1
Person hasTopic threshold 0.8
BlogTalk2009 10 Copyright © 2004-2009, KISTI

11. Identity Resolution
Authority Data
Normalized Form Variant Form Kind Class
IBM International Business Machines Corporation Abbreviation Institution
Microsoft MS Abbreviation Institution
Microsoft 마이크로소프트 Korean Institution
London 런던 Korean Location
Academic Inc. Academic Press Inc, LTD Alternative Publication
BlogTalk2009 11 Copyright © 2004-2009, KISTI

12. Identity Resolution
sameAs
Authorization
∅
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13. Identity Resolution
sameAs
Candidates
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14. ReSIST (2006 ~ 2008)
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15. ReSIST (2006 ~ 2008)
Resilience Knowledge Base
"Deliverable D31: Final Workshop report" by ReSIST
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16. LOD Project
Linking Open Data Community Project
Available in RDF and SVG (Scalable Vector Graphics) versions
KISTI
http://richard.cyganiak.de/2007/10/lod/
BlogTalk2009 16 Copyright © 2004-2009, KISTI

17. Finding experts … How to extract topics?
How to determine topics of a researcher?
BlogTalk2009 17 Copyright © 2004-2009, KISTI

18. Topic Extraction
System Architecture
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19. Topic Propagation
Propagating Topics of Entities
Article Person
BlogTalk2009 19 Copyright © 2004-2009, KISTI

20. Experts Finding
Process
Knowledge expansion
Making direct relations for shorter access path
Experts retrieval
Querying with SPARQL for a given topic
Converting SPARQL-to-SQL
Using backward chaining path
Post-processing
Grouping and counting retrieved authors
Ranking by names or the number of achievements
Making an XML document as the result
BlogTalk2009 20 Copyright © 2004-2009, KISTI

21. Knowledge Expansion
Inference Rule
@prefix isrl: <http://www.kisti.re.kr/isrl/ResearchRefOntology#>
(?x isrl:hasCreatorInfo ?y) (?y isrl:hasCreator ?z) ->
(?x isrl:createdByPerson ?z)
Article
hasCreatorInfo
CreatorInfo
createdByPerson
hasCreator
Person ……
BlogTalk2009 21 Copyright © 2004-2009, KISTI

22. Experts Retrieval
Backward Chaining Path
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23. Generating networks … How to find a researcher group?
How about similar researchers?
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24. OntoFrame 2008
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25. Researcher Networks (T, P)
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26. Researcher Networks (T, P)
Process
Getting co-author pairs for a target topic (T)
SELECT DISTINCT ?person1 ?person2
WHERE {
?article aca:yearOfAccomplishment ?year .
FILTER(?year>=startYear && ?year<=endYear) .
?article aca:hasTopicOfArticle <topURI> .
?article aca:createdByPerson ?person1 .
?article aca:createdByPerson ?person2 .
FILTER(?person1 < ?person2) .
}
Selecting a target researcher (P) in the pairs
Tracing group members connected with him (seed)
BlogTalk2009 26 Copyright © 2004-2009, KISTI

27. Researcher Networks (P)
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28. Researcher Networks (P)
Process
Getting co-author pairs including a target researcher (P)
SELECT ?per1 ?per2
WHERE {
?article aca:yearOfAccomplishment ?year .
FILTER(?year>=startYear && ?year<=endYear) .
?article aca:createdByPerson ?per1 .
?article aca:createdByPerson ?per2 .
FILTER(?per1 < ?per2) .
FILTER(?per1=<perURI> || ?per2=<perURI>) .
}
Ranking them with the frequency of co-authorship
BlogTalk2009 28 Copyright © 2004-2009, KISTI

29. Similar Researchers
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30. Similar Researchers (P)
Process (1/2)
Getting topics of a target researcher (P)
SELECT ?per1 ?topic
WHERE {
?article aca:createdByPerson ?per1 .
?article aca:hasTopicArea ?topicArea .
?topicArea aca:hasTopicOfTopicArea ?topic .
FILTER(?per1=<perURI>) .
}
Ranking and selecting top n topics for him
BlogTalk2009 30 Copyright © 2004-2009, KISTI

31. Similar Researchers
Process (2/2)
Getting researchers who largely share topics with him
SELECT DISTINCT ?per2
WHERE {
?per2 aca:hasTopicOfPerson ?topic1 .
?per2 aca:hasTopicOfPerson ?topic2 .
?per2 aca:hasTopicOfPerson ?topic3 .
?per2 aca:hasTopicOfPerson ?topic4 .
FILTER(?per2!=<perURI>) .
FILTER(?topic1 < ?topic2 && ?topic2 < ?topic3 && ?topic3 < ?topic4) .
{
FILTER(?topic1=<topic[0]> || ?topic1=<topic[1]> || ?topic1=<topic[2]>
|| ?topic1=<topic[3]> || ?topic1=<topic[4]>) .
FILTER(?topic2=<topic[0]> || ?topic2=<topic[1]> || ?topic2=<topic[2]>
|| ?topic2=<topic[3]> || ?topic2=<topic[4]>) .
FILTER(?topic3=<topic[0]> || ?topic3=<topic[1]> || ?topic3=<topic[2]>
|| ?topic3=<topic[3]> || ?topic3=<topic[4]>) .
FILTER(?topic4=<topic[0]> || ?topic4=<topic[1]> || ?topic4=<topic[2]>
|| ?topic4=<topic[3]> || ?topic4=<topic[4]>) .
}
BlogTalk2009 31 Copyright © 2004-2009, KISTI

32. Conclusions
Processes to Generate Researcher Networks
Getting sources: Papers
Resolving identities: Rules, Authority data, sameAs
Finding experts: Topics, Reasoning
Generating networks: Topic-, Person-constrained
Next Research Topic
Service mashup to get researcher networks directly
BlogTalk2009 32 Copyright © 2004-2009, KISTI

33. “A lot of times, people don’t know what they want until you show it to them.”
by Steve Jobs
Thank you
jhm@kisti.re.kr
BlogTalk2009 33 Copyright © 2004-2009, KISTI