A Taxonomy-based Model for ExpertiseExtrapolation

Delroy Cameron, Amit P. ShethOhio Center for Excellence in Knowledge-enabled Computing (Kno.e.sis)

Wright State University, Dayton OH

Boanerges Aleman-MezaDepartment of Biochemistry and Cell Biology

Rice University, Houston TX

I. Budak Arpinar, Sheron L. DeckerLSDIS Lab, Department of Computer Science

University of Georgia, Athens GA

48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010.

BACKGROUND

Realm of Finding Experts o Propagation Method

o Human-Centered Information Diffusiono prima facie

o Issueso Inconsistent Human Perceptionso Strong vs. Weak ties

Aftefactso Curricula Vitariumo Version Control Systems, Patents & Research Grantso Citation Linkage

2

Citation Sentiment Detection

Pied Piper Effect

Expertise Granularity

Adage: The publications of a Researcher is indicative of

her expertise.

CONTRIBUTIONS

Structured Datao Taxonomy of Topics

o Extrapolation

o Bibliographic Datao Collaboration Networks

Co-authorship Grapho Prevent Collaboration Stagnation

3

Search Algorithms

Page Rank

subtopic_of

DFS, BFS

Seman

tic Ass

ociat

ions

Topic Hierarchy

sEXPERTISE MODEL

4

ai

B = {b1, b2, …, bn} P = {p1, p2,…,pn} T = {t1, t2, …, tm}

b1 λ1p1

b2 p2

b3 p3b4 p4

bn pn

t1

t2

t3

tm

λ2λ3

λ4

λn

Expertise Profile

author

EXPERTISE PROFILES

5

#Semantic_Web

p49p73 p70

p17

p40

p37

p68

p13

p36

p9

p20

p29

#A.I.

p5

#Reasoning

#OWL

#Know. Acq

#Know. Man.

#XML

#Semantics

#Languages

#Content

p50

p8

p42

p53

#Web

#RDF

ai - 81 publications12 - Semantic Web

COMPUTING EXPERTISE

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#A.I.

p5

#Reasoning

#OWL

e(#Semantic_Web) = ((p5(OWL) v p5(Reasoning) v p5(A.I.)) λecai

e(p5) = (1 v 0 v 0) 0.69 = 0.69

COMPUTING EXPERTISE

8

#Semantic_Web

p49p73 p70

p17

p40

p37

p68

p13

p36

p9

p20

p29

#A.I.

p5

#Reasoning

#OWL

#Know. Acq

#Know. Man.

#XML

#Semantics

#Languages

#Content

p50

p8

p42

p53

#Web

#RDF

e(p5) = λecai = 0.69

e(p8) = λekaw = 0.55

e(p42) = λwww = 1.54

e(p50) = λewimt = 0.1

e(p53) = λekaw= 0.55

e‘’ = 0.69+0.55+1.54+0.1+0.55=3.43

e’ = 10.40

e = 10.40+3.43=13.43

DATASET

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Papers-to-Topics Dataseto 476,299 papers o 676,569 relationships to topicso Focus Crawl DBLP

Taxonomy of CS Topicso Manually (320 Topics)o Conference Names (60)o Session Names (216)o Index Terms & Yahoo! Term Extractor (128)o O`Comma Taxonomy (50)

Publication Impact Factorso Citeseer (>1200 Proceedings)

DEMO

10

http://knoesis1.wright.edu/expert_finder

EVALUATION

11

GEODESIC

Geodesic - Shortest path between two vertices in a directed graph

12

ba

Geodesic Level Description w.r.t. PC Chair(s) Degree of SeparationSTRONG co-authors One

MEDIUM common coauthors Two

WEAK published in same proceedings Unspecified

coauthors w/ common coauthors Two

coauthor related to editor Three

EXTREMELY WEAK coauthors in same proceedings Three

UNKNOWN no relationship in dataset Unknown

EVALUATION

13

C-Net

C-Net – Measure of collaboration strength within expert subgroups

14

vm=14.80

v1=0.73 v2=0.73

v3=0.73

v4=1.810.5

0.5 0.5

1.0

M. E. J. Newman, “Coauthorship networks and patterns of scientific collaboration,” in Proceedings of the National Academy of Sciences, 2004

LIMITATIONS

Taxonomy of Topics Semantic Association in Large RDF Graphs Entity Disambiguation Paper-to-Topics Mappings

15

CONCLUSION

Semantic Expert Findero Taxonomy of Topicso Publication Impact Factorso Expertise Profiles

Collaboration Network Analysiso Co-Authorship Grapho Semantic Associations

16

ACKNOWLEDGEMENTS

People Wenbo Wang Ajith Ranabahu Boanerges Aleman-Meza

National Science Foundation Award SemDis (Discovering Complex Relationships in the Semantic Web) No. 071441 Wright State University No. IIS-0325464 to University of Georgia

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