A Semantic and Information Retrieval based Approach to

A Semantic and Information Retrieval A Semantic and Information Retrieval based Approach to based Approach to

Service Contract SelectionService Contract Selection

Silvia Calegari, Marco Comerio, Andrea Maurino, Emanuele Panzeri and Gabriella PasiEmanuele Panzeri, and Gabriella Pasi

Department of Informatics, Systems and Communication (DISCo)University of Milano-BicoccaUniversity of Milano Bicocca

{calegari,comerio,maurino,panzeri,pasi}@disco.unimib.it

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ContentsContents

Problem Definition

Motivation, Background and Contributions

The Semantic and IR based ApproachppMulti-constraint query formulationFiltering and query evaluation

Experimental Results

Conclusions and Future Works

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Service ContractService Contract

A Service Contract represents the agreement between a service provider and potential service consumers to use a service provider and potential service consumers to use a specific service under given conditions.

Beyond the description of service functionalities, a service contract is composed by contractual terms on:

Quality of Service (e.g., response time and availability);Legal Terms (e.g., limitation of liability and copyrights), Intellectual Rights (e.g., denying composition), Business Terms (e.g., payment and tax).

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For each service, multiple service contracts are available. available.

Each service contract can be offered to specific user categories; Each user category is associated with specific affiliation Each user category is associated with specific affiliation conditions.

Service Contract Selection: identify the service contracts that better fulfill the constraints on contractual terms explicitly specified by the user, and/or implicitly p y p y p yinferred from user information.

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H t t ti ll l t th b t5

How to automatically select the best among a set of functional-equivalent services?

ContentsContents

Problem Definition





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Motivation & BackgroundMotivation & Background

An approach to automatic service contract selection should cover the following characteristics: should cover the following characteristics:

expressivity as the possibility to evaluate qualitative contractual terms by means of logical expressions on ontology values, and quantitative contractual terms by mean of expressions including ranges and inequalities;

extensibility as the possibility to customize evaluation extensibility as the possibility to customize evaluation functions;

flexibility as the possibility to perform evaluation in case of incomplete specifications.

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Motivation & BackgroundMotivation & Background

In ICSOC 2009, we proposed an hybrid approach to service contract selection that

combines logic-based and algorithmic techniques; offers high levels of expressivity, extensibility and flexibility.

The approach has been implemented by the Policy Matchmaker and Ranker (PoliMaR) framework that operates on service contracts defined according to the Policy Centered Metamodel (PCM).

Beyond performance problems, the approach and the f k d h f ll i li i i framework presented the following limitations:

no support for the formulation of user requests; no support for the evaluation of user category affiliations.

PoliMaR is available at: http://sourceforge.net/projects/polimar/8

ContributionContribution

A new approach to service contract selection based on:

the exploitation of preferences explicitly specified by the user, and implicitly inferred from user information;

th f b th ti b d d i f ti the use of both semantic-based and information retrieval (IR) techniques to filter and rank service contracts.

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ContentsContents

Problem Definition





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The Proposed ApproachThe Proposed Approach

Set-up time

Run time

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RegistrationRegistration

At set-up time, the user:selects a pre-defined profile providing information on p p p ggeneric user characteristics;inserts personal information;specifies preferences. specifies preferences.

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Registration Registration -- ExampleExample

User Profile

Pre-defined User Profile

“I am an English speaker.I am able to use my mobile

phone and to frequentlyPCMPCM

Name: Mary BrownAddress: London, Oxford streetAge: 45 years old Job: IT Researcher

phone and to frequentlyaccess my email account.

I am a VAT owner.”

Job: IT ResearcherLanguage: EnglishInfo. Channel: email, phone callVAT owner: yes

Preferences: [secure cheap]Preferences: [secure, cheap]

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Query FormulationQuery Formulation

The user selects a pre-defined query presented as a textual description of both precise (e.g., insurance = blanket) and flexible constraints (e.g. price = at most 40€).

The user personalizes the query by modifying the pre-defined constraints, and/or by adding further constraints as short textual d i tidescriptions.

A query expansion process is applied to add further constraints from user profile and user history.

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Query Formulation Query Formulation –– An ExampleAn Example

Pre-defined User QueryUser Profile

Name: Mary BrownPre defined User Query

“I need to perform the transportation of a valuable good. I am looking for

a fast delivery service having a blanket insurance.”

Name: Mary BrownAddress: London, Oxford streetAge: 45 years old Job: IT ResearcherLanguage: EnglishInfo. Channel: email, phone call

Personalized Query

Delivery in at most 24 hours.

Price at most equal to 40€.

Info. Channel: email, phone callVAT owner: yes

Preferences: [secure, cheap]

I would like to receive traceabilityinformation on the transportation

PCMPCM

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FilteringFiltering

Each service contract is offered to one or more user categories that are defined by a set of affiliation conditions (e.g., user age, VAT owner). A user is associated with a category if and only if all the conditions are respected.

Service contracts are filtered complying to the user category affiliations that are determined analyzing user profile and user history.

The result is a set of filtered service contracts.

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Filtering Filtering –– An ExampleAn Example

User History

Provider Contract Category Conditions

Provider A pay-flex Business Plus VAT owner, email account, 30 shipments

Provider A high-trace Business One VAT owner, email account

Provider A secure Business One VAT owner, email account

Provider B fast-plus Silver User 20 shipments

Provider B fast Bronze User 10 shipments

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p

Provider B cheap Senior User ≥ 65 years old

Query EvaluationQuery Evaluation

The multi-constraint query is evaluated against the filtered service contracts. A ranked list of service contracts is returned to the user.

Query constraints and contractual terms are expressed by both specific data and textual descriptions. specific data and textual descriptions.

Different evaluation functions are needed to evaluate the matching degrees between constraints and contractual terms.An aggregation function is used to compute the overall service contract score score.

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Query Evaluation Query Evaluation –– Evaluation FunctionsEvaluation Functions

Constraints on numeric data values:constraints expressed as fuzzy subsets of the attribute domains;p y ;evaluation perfomed by means of parametric linear membership functions;e.g., A membership function for “price at most 40 €” constraint.

Concept-based constraints:pconstraints expressed on concepts defined in ontologies;evaluation performed on the basis of the semantic distances between required and offered values.

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Keyword-based constraints:i f ti t i l t h i d t t t k d f information retrieval techniques are used to extract keyword from contractual terms expressed in plain texts;

evaluation performed using the Vector Space Model that represents each set of keywords as vectors and supports the represents each set of keywords as vectors and supports the evaluation of the similarity between two vectors using a vector distance (e.g., the Cosine similarity).

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The overall service contract score (namely, Degree of Match – DoM) is computed using the following ) p g gaggregation function:

)(])([ +∑ qcsCosSimqscCFnc rr

1),(]),([

),( 1

+

+= ∑ =

ncqcsCosSimqscCF

qscDoM i i

nc = number of query constraints;

CFi = evaluation of constraint i;

CosSim(sc q) evaluation performed using Cosine CosSim(sc,q) = evaluation performed using Cosine Similarity on sc (i.e., keyword vector associated with the contract) and q (i.e., keyword vector associated with the

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query).21

Query Evaluation Query Evaluation –– An ExampleAn Example

Multi-constraint Query

Language: English

PCMPCM

g g gInfo. Channel: email, phone callHours to Delivery: at most 24 hoursPrice: at most 40€Insurance: BlanketPay Methods: credit card electronic transferPay. Methods: credit card, electronic transfer

Preferences: [secure, cheap, traceability]

CONTRACT FAST-PLUSPCMPCM

Info. Channel: SMS

Hours to Delivery: 12-24 hours

0.0

1.0

Price: 40€

Insurance: Fire and Theft

P M th d dit d

1.0

0.33

0 5

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Pay. Methods: credit card

Description: [fast, traceability, english]

0.5

0.50

ContentsContents

Problem Definition





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ExperimentsExperiments

32 service contracs from 5 different providers.

3 multi-constraint queries with increasing complexity.3 multi constraint queries with increasing complexity.

An ideal service contract rank is obtained as an agreement of a pool of experts.

A modified version of the normalized discounted cumulative gain (NDCG) measure is adopted to assess the effectiveness of the proposed approach.

Given a ranked result set Sr and an ideal rank Si, the NDCG is evaluated as follows:

),(),(),(

kSDCGkSDCGkSNDCG

i

rr =

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ExperimentsExperiments

The NDGV average values at different @-cuts were evaluated considering different conditions :

CASE 1: without considering the user profile;CASE 2: only by considering information taken at registration time;CASE 3: only by considering user history;CASE 4 l b id i i f ti t l lCASE 4: only by considering information on punctual values;CASE 5: only by considering information on textual description;CASE 6: the proposed approach.

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ContentsContents

Problem Definition





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Conclusions and Future WorksConclusions and Future Works

We propose a novel approach to service contract selection based on:

definition of multi-constraint queries on precise and flexible preferences both explicitly defined by the users and implicitly inferred from their contexts;filtering of service contracts according to user category affiliations;evaluation of multi-constraint queries using semantic and IR techniques techniques.

Experimental results show the effectiveness of the proposed approach.

Future works deal with:Building of a large benchmark of real service contracts;Management of contractual terms (e g security trust) that

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Management of contractual terms (e.g., security, trust) that cannot be directly quantified.

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Thank you!Questions?

Future Works (?)Future Works (?)

Building of a large benchmark of real service contracts.How IR techniques can be used on available service contract qdescriptions (e.g., ProgrammableWeb)?Focus on specific domains or specific contract types (e.g., data contracts) in order to define the knowledge-base and reduce possible cont act al te ms and al es possible contractual terms and values.

IR techniques to support (functional) service discoveryEvaluation of descriptions, service category and tags.

Design and development of the semantic+IR service contract selector.

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Documents

A Semantic and Information Retrieval based Approach to