a taxonomy-based model for expertise extrapolation delroy cameron, amit p. sheth ohio center for...
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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 A
ssoc
iation
s
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
7
#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
9
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)
GEODESIC
Geodesic - Shortest path between two vertices in a directed graph
12
b
a
Geodesic Level Description w.r.t. PC Chair(s) Degree of Separation
STRONG 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
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=1.81
0.5
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