convegno italiano di logica computazionale cilc 2006 bari 26-27 giugno 2006 semantic-based...

Convegno Italiano di Logica ComputazionaleConvegno Italiano di Logica Computazionale CILC 2006CILC 2006

Bari 26-27 giugno 2006Bari 26-27 giugno 2006

Semantic-based matchmaking and query Semantic-based matchmaking and query refinement for B2C e-marketplacesrefinement for B2C e-marketplaces

S. Colucci, T. Di Noia, E. Di Sciascio,

A. Ragone, R. Rizzi

Politecnico di Bari

F.M. Donini

Università della Tuscia, Viterbo

Sistemi Sistemi InformativiInformativiDEE - Politecnico di BariDEE - Politecnico di Bari

Convegno Italiano di Logica Computazionale. Bari, 26-27 Convegno Italiano di Logica Computazionale. Bari, 26-27 Giugno 2006Giugno 2006

B2C ScenarioB2C Scenario

Satellite TV system for football world

championship

B (Business)

•Domain expertise

•Technically advertised resources

•Fix resources descriptions

C (Consumer)

•Lack of knowledge domain

•Lack of technical vocabulary

•Vague buying ideas (shop assistant)

NEED FOR A BRIDGE



ChallengeChallenge

• Match resource to potential buyer’s interests (semantic annotation)

• Facilitating exploration and selection of product characteristics (user friendly interaction)



OutlineOutline

• Application general features• Common sense user needs• System Description• Matchmaking Steps• Conclusions



ApplicationApplication FeaturesFeatures

• Benefit of semantic annotation:- richness of descriptions- reasoning services for matchmaking, ranking and explanation

• User friendly interaction:- query formulation processquery language (expressiveness)



User NeedsUser Needs

• Support in the searching process: find the right product starting from a “vague idea” (incomplete information and preference elicitation)

• Efficiency and trust: find the right product being confident the system finds the best one.

• Ranking Criteria: price is not the only one criterion!

• Friendliness: no technological gaps to overcome in order to use the system (no specific skill or learning effort)



System BehaviorSystem Behavior (1/8)(1/8)

MARKETPLACE SELECTION

•Domain independence

•On the fly ontology selection

•On the fly marketplace creation




GUI :

• Section (a)-(b)-(d): navigation panel

• Section (c)-(e): query panel




NAVIGATION PANEL:• Ontology browsing• Intensional navigation (top-down

approach) • Most generic ontology classes shown

in the top of (a) as entry points• Properties on which is possible to

impose numeric restrictions shown in the bottom of (a)

• Subclasses and roles of selected entry points shown in (b)

• Navigation and zoom out by the history bar in (d)

• Selected characteristics dragged in the query panel and added to the user final query




QUERY PANEL:• Positive preferences in (e)

• Negative preferences in (c)

• Preference removal by right clicking

• Positive preferences all set strict in the initial query




MATCHMAKING PROCESS EXECUTION:• Search for a match for the formulated query with

all the semantic-enabled descriptions of supplies within the marketplace

• Reasoning services for matchmaking and ranking provided by

MaMaS: http://sisinflab.poliba.it/MAMAS-tng/• Communication with MaMaS via DIG 1.1 interface

over http




• RESULTS WINDOW:

• Section (a)-(b): LIST PANEL

• Section (c)-(d): QUERY REFINEMENT PANEL




LIST PANEL:•Ranked list of appealing supplies within the marketplace

•Logical Explanation on match results

• Information for each retrieved item:

- Description: image, natural language description (transliteration of OWL description)

- Match value: semantic-based computed rank

- Match Explanation: fulfilled, unspecified, conflicting and additional

characteristics w.r.t. the request

• Multi-page visualization to be browsed by (b)




QUERY REFINEMENT PANEL:• Query visualized in (c)

• Additional information (bonus) related to the offers currently displayed in the list panel shown in (d)

•Possible query refinements:

- relaxing some characteristics setting them to negotiable (also supplies with

features in conflict with the negotiable ones are considered)

- adding new characteristics from the bonus currently displayed in (d)

A NEW SEARCH CAN START!



MATCHMAKING MATCHMAKING STEPS(1/2)STEPS(1/2)

1. Formalization of the user request w.r.t. the ontology: all requested characteristics are set strict.

2. All the supplies in potential match are retrieved: fulfilled, uncertain and additional feature (Concept Abduction Problem). Computation of a semantic-based match value

3. Ranking of all the retrieved supplies w.r.t. their semantic-based match value. All the additional features displayed in the bottom side of the query refinement panel.



MATCHMAKING MATCHMAKING STEPS(2/2)STEPS(2/2)

4. Possible query refinement by the user.

5. New retrieved process performed: computation of a semantic-based match value based on fulfilled, unspecified, conflicting and additional characteristics.

Partial Potential FullContract Abduce



ConclusionsConclusions

• System showing benefits of semantic markup of descriptions in an e-marketplace

• System satisfying common sense user needs: support in the searching process, efficiency and trust and ranking criteria



Future WorkFuture Work

• System is being tested by human volunteers for evaluating both the degree of correspondence of the approach to commonsense judgment and the usability of the tool

• Ajax-based GUI• Good qualities ontology modeling• Evaluation of different match degree functions,

with extra-ontological information, under investigation



Special ThanksSpecial Thanks

Marketplace application originally designed and developed by

Raffaele RizziRe-engineered and maintained by

Francesco Di Cugno

http://sisinflab.poliba.it/marketplace/



Thank you



Query processQuery process



ConclusionsConclusions

• System showing benefits of semantic markup ofdescriptions in an e-marketplace

• System satisfying common sense user needs: support in the searching process, efficiency and trust and ranking criteria

• System tested by human volunteers for evaluating both the theoretical approach and the usability of the tool

• Evaluation of different match degree functions, with extra-ontological information, under investigation



Semantic Semantic AnnotationAnnotation

• Resources formalized in ALN Description Logic(DL):- unambiguous shared meaning of terms- Open World Assumption (OWA) for descriptions

• Semantic-based Matchmaking- Subsumption for Potential Match- Contraction for belief revision

PARTIAL POTENTIAL- Abduction for explanation

POTENTIAL FULL• Logic-based Ranking of resources



Query processQuery process

• Fully supported searching process- hidden product technicalities- visual representation of user needs- ongoing specification of buying ideas- searched product features revision - additional features suggested- logic-based overall ranking vs feature-specific ranking

convegno italiano di logica computazionale cilc 2006 bari 26-27 giugno 2006 semantic-based...

Documents