service provider and potential service consumers to use a specific service under ... ti ll. l t th b t. 5. How to automatically select the best among a set of .... a fast delivery service having a ... Business Plus VAT owner, email account,. 30 shipments.
A Semantic and Information Retrieval based Approach to Service Contract Selection Silvia Calegari, Marco Comerio, Andrea Maurino, Emanuele Panzeri, Panzeri and Gabriella Pasi Department of Informatics, Systems and Communication (DISCo) University of Milano-Bicocca Milano Bicocca {calegari,comerio,maurino,panzeri,pasi}@disco.unimib.it
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Contents
Problem Definition
Motivation, Background and Contributions
The Semantic and IR based Approach pp 9
Multi-constraint query formulation
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Filtering and query evaluation
Experimental Results
Conclusions and Future Works
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Service Contract
A Service Contract represents the agreement between 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: 9
Quality of Service (e.g., response time and availability);
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Legal Terms (e.g., limitation of liability and copyrights),
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Intellectual Rights (e.g., denying composition),
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Business Terms (e.g., payment and tax).
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Service Contract Selection
For each service, multiple service contracts are available. 9
Each service contract can be offered to specific user categories;
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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 p y specified p by y the user, and/or implicitly p y inferred from user information.
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Service Contract Selection
How tto automatically H t ti ll select l t the th best b t among a set of functional-equivalent services? 5
Contents
Problem Definition
Motivation, Background and Contributions
The Semantic and IR based Approach pp 9
Multi-constraint query formulation
9
Filtering and query evaluation
Experimental Results
Conclusions and Future Works
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Motivation & Background
An approach to automatic service contract selection should cover the following characteristics: 9
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;
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extensibility as the possibility to customize evaluation functions;
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flexibility as the possibility to perform evaluation in case of incomplete specifications.
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Motivation & Background
In ICSOC 2009, we proposed an hybrid approach to service contract selection that 9
combines logic-based and algorithmic techniques;
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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 framework k presented d the h following f ll i limitations: li i i 9
no support for the formulation of user requests;
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no support for the evaluation of user category affiliations.
PoliMaR is available at: http://sourceforge.net/projects/polimar/
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Contribution
A new approach to service contract selection based on: 9
the exploitation of preferences explicitly specified by the user, and implicitly inferred from user information;
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the use off both th b th semantic-based ti b d and d information i f ti retrieval (IR) techniques to filter and rank service contracts.
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Contents
Problem Definition
Motivation, Background and Contributions
The Semantic and IR based Approach pp 9
Multi-constraint query formulation
9
Filtering and query evaluation
Experimental Results
Conclusions and Future Works
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The Proposed Approach Set-up time
Run time
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Registration
At set-up time, the user: 9
selects a p pre-defined p profile p providing g information on generic user characteristics;
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inserts personal information;
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specifies preferences.
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Registration - Example
Pre-defined User Profile
“I am an English speaker. I am able to use my mobile phone and to frequently access my email account. I am a VAT owner.”
User Profile
PCM
Name: Mary Brown Address: London, Oxford street Age: 45 years old Job: IT Researcher Language: English Info. Channel: email, phone call VAT owner: yes Preferences: [secure, [secure cheap]
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Query 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 descriptions. i ti
A query expansion process is applied to add further constraints from user profile and user history.
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Query Formulation – An Example User Profile Pre-defined Pre 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.” Personalized Query
Name: Mary Brown Address: London, Oxford street Age: 45 years old Job: IT Researcher Language: English Info. Channel: email, phone call VAT owner: yes Preferences: [secure, cheap]
Delivery in at most 24 hours. Price at most equal to 40€. I would like to receive traceability information on the transportation
PCM
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Filtering
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 – An 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
p 10 shipments
Provider B
cheap
Senior17User
≥ 65 years old
Query 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. 9
Different evaluation functions are needed to evaluate the matching degrees between constraints and contractual terms.
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An aggregation function is used to compute the overall service contract score. score
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Query Evaluation – Evaluation Functions
Constraints on numeric data values: 9
constraints expressed p as fuzzy y subsets of the attribute domains;;
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evaluation perfomed by means of parametric linear membership functions;
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e.g., A membership function for “price at most 40 €” constraint.
Concept-based p constraints: 9
constraints expressed on concepts defined in ontologies;
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evaluation performed on the basis of the semantic distances between required and offered values.
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Query Evaluation – Evaluation Functions
Keyword-based constraints: 9
iinformation f ti retrieval t i l ttechniques h i are used d to t extract t t keyword k d from f contractual terms expressed in plain texts;
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evaluation performed using the Vector Space Model that 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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Query Evaluation – Evaluation Functions
The overall service contract score (namely, Degree of Match – DoM)) is computed p using g the following g aggregation function:
r r [∑i =1 CFi ( sc, q )] + CosSim( sc , q ) nc
DoM ( sc, q ) =
nc + 1
nc = number of query constraints; CFi = evaluation of constraint i; CosSim(sc,q) 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 query). 21
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Query Evaluation – An Example PCM
Multi-constraint Query
g g English g Language: Info. Channel: email, phone call Hours to Delivery: at most 24 hours Price: at most 40€ Insurance: Blanket Pay Methods: credit card, Pay. card electronic transfer Preferences: [secure, cheap, traceability] CONTRACT FAST-PLUS
PCM
Info. Channel: SMS
0.0
Hours to Delivery: 12-24 hours
1.0
Price: 40€
1.0
Insurance: Fire and Theft
0.33
P Pay. M th d credit Methods: dit card d
05 0.5
Description: [fast,22 traceability, english]
0.50
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Contents
Problem Definition
Motivation, Background and Contributions
The Semantic and IR based Approach pp 9
Multi-constraint query formulation
9
Filtering and query evaluation
Experimental Results
Conclusions and Future Works
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Experiments
32 service contracs from 5 different providers.
3 multi multi-constraint 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. 9
Given a ranked result set Sr and an ideal rank Si, the NDCG is evaluated as follows:
DCG ( S r , k ) NDCG ( S r , k ) = DCG ( Si , k ) 24
Experiments
The NDGV average values at different @-cuts were evaluated considering different conditions : 9
CASE 1: without considering the user profile;
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CASE 2: only by considering information taken at registration time;
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CASE 3: only by considering user history;
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CASE 4: 4 only l by b considering id i information i f ti on punctual t l values; l
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CASE 5: only by considering information on textual description;
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CASE 6: the proposed approach.
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Contents
Problem Definition
Motivation, Background and Contributions
The Semantic and IR based Approach pp 9
Multi-constraint query formulation
9
Filtering and query evaluation
Experimental Results
Conclusions and Future Works
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Conclusions and Future Works
We propose a novel approach to service contract selection based on: 9
definition of multi-constraint queries on precise and flexible preferences both explicitly defined by the users and implicitly inferred from their contexts;
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filtering of service contracts according to user category affiliations;
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evaluation of multi-constraint queries using semantic and IR techniques. techniques
Experimental results show the effectiveness of the proposed approach.
Future works deal with: 9
Building of a large benchmark of real service contracts;
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Management of contractual terms (e.g., (e g security, security trust) that cannot be directly quantified. 27
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Thank you! Questions?
Future Works (?)
Building of a large benchmark of real service contracts. 9
How IR techniques q can be used on available service contract descriptions (e.g., ProgrammableWeb)?
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Focus on specific domains or specific contract types (e.g., data contracts) in order to define the knowledge-base and reduce possible cont contractual act al terms te ms and values. al es
IR techniques to support (functional) service discovery 9
Evaluation of descriptions, service category and tags.
Design and development of the semantic+IR service contract selector.
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