using semantic annotation of web services for analyzing

Shahab Mokarizadeh , Royal Institute of Technology (KTH) , Sweden

Peep Küngas, University of Tartu (UT) , Estonia

Mihhail Matskin , Royal Institute of Technology (KTH) , Sweden

Marco Crasso, Marcelo Campo, Alejandro Zunino , UNICEN University,

Argentina

Contact: shahabm@kth.se

1

Information Diffusion in Web Services Networks

Outline

2

Background of Information Flow Analysis

Roadmap and Computational Model

Web service Annotation

Web service Categorization

Experimental Results

Discussion & Conclusion

Background – Information Diffusion

3

Information Diffusion: the communication of knowledge over

time among members of a social system

It shows intrinsic properties of real-world phenomenon.

Already studied in the context of: biosphere, microblogs,

publication citation, … where a network structure present.

Information Diffusion

among Web service Domains

4

Observation: Services published in the Web form a conceptual

ecology of knowledge where information is shared and flows

along input and output parameters of service operations.

Case-study: How Web services in different commodities have

been designed from information exchange perspective?

Introducing value-add Web services

Web service adoption spots

Roadmap

5

1 • Semantically annotation of Web services

2 • Assign Web services to respective categories

3 • Construct Web service network

4 • Compute information flow matrix

5 • Matrix Analysis

1-Web service Annotation

6

Image from : Web Services and

Security,1/17/2006 ,Marco Cova

-Only semantic annotations of basic elements of input and output

parameters of Web service Operations

-SAWSDL annotation model

-We exploit our Semi-automated ontology learning method which

relies on lexico-syntactic patterns “Ontology Learning for Cost-Effective Large-Scale Semantic Annotation

of Web Service Interfaces”. EKAW 2010:pp. 401-410

Tax and Customs Board service

7

Output message content fragment

Business Registry service

8

Input message content fragment

A Business Registry service

9 Output message content fragment

Registry of Economic Activities Service

10

Output message content fragment

2-Web service Categorization

11

A category (a.k.a. commodity) describes a general kind of a service

that is provided, for example “B2B” , “Health”, “E-Commerce”, etc.

Each Web service could belong to multiple categories !

Standard Software Taxonomy e.g. UNSPSC: http://www.unspsc.org/

We use Classifier : "AWSC: An approach to Web Service classification

based on machine learning techniques“, Inteligencia Artificial, ISSN 1137-3601, vol.

12, no. 37, pp. 25-36, Asociación Española para la Inteligencia Artificial, Valencia, España.

2008.

UNSPSC

Instant messaging Calendar and scheduling

Adventure games Mobile operator specific

Internet directory services Medical software

Music or sound editing Video conferencing software

3-Web service Network Construction

12

1- Present annotated Web services as bipartite (2-mode) graph

2- Create Semantic Network (1-mode graph)

3- Create Weighted Category Network using Semantic network

Bipartite Web Service Network

13

Bipartite Web Service Network

(categorized)

14

Propagate the categories to semantic

nodes , Cu: semantic node ,

qk: weight of node in category k

Network Transformation

15

Semantic Network Category Network

nku qqqQ .,,..1

n

i

iu

su

s

DinCoffrequency

DinCoffrequencyq

1

Ds, Dt : category nodes

Label each category edge with weights:

tvsutsvu qqDD ,,, .),(

),(

, ),(),(vuedge

tsvuts DDDDW

4-Normalizing Weights (Z-score)

16

Edge category weight W(Di,Dj) : Wi,j

Sum of all weights of all links from category i:

Sum of all weights of all links to category j:

Sum of weights of all categories:

Expected weights from category i to category j :

Normalize category weights (Z-Score):

j

jii DDWW ),(*

i

jij DDWW ),(*

ji

ji DDWW,

),(

W

WW ji **

W

WW

W

WWW

jiji

jiji

****

,, )(

Matrix of Information flow

17

nnjnn

nijii

nj

,,1,

,,1,

,1,11,1

Matrix of information flow between pair of categories:

A high proximity (Φ i j) between categories i and j reveals a strong

tendency for semantic concepts associated to category j to be resulted

from invocation of services which take semantic concepts associated to

category i.

5-Experimental Settings

18

27000 public Web services (WSDLs) (collected 2005-2011)

Semantic Annotation

Lexico-syntactic based ontology learning

Annotation accuracy: Precision= 31% , Recall= 19%

Categorization

AWSC Classifier

Training dataset: 1500 WSDLs

Categorization Accuracy: 91%

Category Category

1-Communications server 11-Network operation system

2-Instant messaging 12-Database management system

3-Adventure games 13-Analytical or scientific

4-Internet directory services 14-Portal server

5-Music or sound editing 15-Foreign language software

6-Calendar and scheduling 16-Procurement software

7-Mobile operator specific 17-Inventory management software

8-Medical software 18-Dictionary software

9-Video conferencing 19-Fax software

10-Map creation software 20-Object oriented database management

19

Excerpt of Identified Service Categories

20

Visualization of Matrix of Information Flow

Information Exchange Patterns - 1:

21

Self-Referential Pattern: A category mainly provides inputs

for its own services and consumes mostly the information

provided by itself (i.e. self contained).

Appear in diagonal of matrix

Categories: Financial Analysis Software, Web Platform Development

Software, Map Creation Software, Video Conferencing Software and

Accounting Software

The API-s exposed by these Web services exploit frequently

domain-specific concepts as input and output elements

Information Exchange Patterns - 2:

22

Outside main diagonal:

-Foreign Language category , Presentation category

-Financial Analysis category , Enterprise Resource Planning category

Least volume of information flow:

-Video Conferencing software and Financial Analysis software

Threats to Validity

23

The presented model heavily relies of accuracy of underlying semantic annotation and matching scheme !

The examined Web services account only for small proportion of existing ones on the Web!

The collection of Web services’ interface descriptions may also suffer from unintentional preference toward some specific categories.

In the absence of timing factor our analysis is rather static analysis of information flow

Conclusion and Future Work

24

The presented approach can discover information exchange

patterns.

In general our approach is applicable to any other kind of machine

understandable APIs, not just WSDLs, !

Future work:

To examine how presence of service composition or mashups

influences the information exchange pattern

Recommending value-add Web services based on identified

information exchange patterns and Web service network

properties

Thanks!

Questions Please!

25

26

tvsutsvu qqDD ,,, .),( Partial Category Weight for Edge (Ds,Dt) :

Augmented Category Weight for Edge (Ds, Dt):

),(

, ),(),(vuedge

tsvuts DDDDW

Ontology Learning for

Web service Annotation1

27

Reference Ontology

Adding Relations

Ontology Organization

Term Extraction

Syntactic Refinement

Information Elicitation

Pattern-based Semantic Analysis

Term Disambiguation

Class and Relation Determination

Ontology Discovery

Ontology Learning Input:

- Message Part names of input/output

parameters

- XML Schema leaf element names of

complex types

[1] ”Ontology Learning for Cost-Effective Large-scale Semantic

Annotation of XML Schemas and Web Service Interfaces". in Porc.

EKAW 2010, LNAI 6317,pp.401-410, 2010