Ranking Web Services Using

Centralities and Social Indicators

Tilo Zemke*

José I. Fernández-Villamor**

Carlos Á. Iglesias**

ENASE 2012, Wroclaw

29th June 2012

* Chemnitz University of Technology

** Technical University of Madrid

Ranking Web Services Using Centralities

and Social Indicators

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Ranking Web Services Using Centralities

and Social Indicators

Logo sources: ProgrammableWeb.com API Directory 3

Ranking Web Services Using Centralities

and Social Indicators

Logo sources: ProgrammableWeb.com API Directory 4

Ranking Web Services Using Centralities

and Social Indicators

Logo sources: ProgrammableWeb.com API Directory 5

http://blog.programmableweb.com/2012/05/22/6000-apis-its-business-its-social-and-its-happening-quickly/

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How can we bring order to the set of

web services?

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Ranking Web Services Using Centralities

and Social Indicators

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• Relative importance of a vertex within

a graph

Ranking Web Services Using Centralities

and Social Indicators

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• Relative importance of a vertex within

a graph

• Degree Centrality (CD)

• Betwenness Centrality (CB)

• Closeness Centrality (CC)

• Eigenvector Centrality (CE)

Ranking Web Services Using Centralities

and Social Indicators

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Graph representation

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Graph representation (simplified)

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Degree Centrality (CD)

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Betweenness Centrality (CB)

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Closeness Centrality (CC)

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Eigenvector Centrality (CE)

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Ranking Web Services Using Centralities

and Social Indicators

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Ranking Web Services Using Centralities

and Social Indicators

• ProgrammableWeb User Rating (PUR)

• Hits on Stackoverflow.com (GSO)

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ProgrammableWeb User Rating (PUR)

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Hits on Stackoverflow.com (GSO)

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Ranking!

„Twitter API site:stackoverflow.com“

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How can we measure the quality?

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•Subsets of Dataset

•„image“ (32), „voice“ (19), „twitter“ (18)

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•Subsets of Dataset

•„image“ (32), „voice“ (19), „twitter“ (18)

• Expert relevance judges rated each service in each set individually

• Afterwards agreed on a uniform rating for each web service

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•Subsets of Dataset

•„image“ (32), „voice“ (19), „twitter“ (18)

• Expert relevance judges rated each service in each set individually

• Afterwards agreed on a uniform rating for each web service

• Ratings served as gain quantifications for the normalized Discounted Cumulated Gain (nDCG) calculations

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Results for query „voice“

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Results for query „twitter“

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Results for query „image“

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Part of this research was funded by the

European Commission under the

project ICT-OMELETTE (FP7-ICT-2009-5):

European Project on Open Platform for

Telco Mashups

www.ict-omelette.eu

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OMELETTE Mashup Registry

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Thank you! Tilo Zemke

zeti@hrz.tu-chemnitz.de

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