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Getting Answers to Fuzzy and Flexible Searches by Easy Modelling of - - PowerPoint PPT Presentation

Getting Answers to Fuzzy and Flexible Searches by Easy Modelling of Real-World Knowledge Victor Pablos-Ceruelo and Susana Mu noz-Hernandez susana@fi.upm.es Universidad Polit ecnica de Madrid (T.U. of Madrid), Spain FCTA2013, Vilamoura,


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SLIDE 1

Getting Answers to Fuzzy and Flexible Searches by Easy Modelling of Real-World Knowledge

Victor Pablos-Ceruelo and Susana Mu˜ noz-Hernandez susana@fi.upm.es

Universidad Polit´ ecnica de Madrid (T.U. of Madrid), Spain

FCTA’2013, Vilamoura, Algarve, Portugal

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 1 / 29

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Overview

  • Motivation

− Crisp Databases − Fuzzy Databases − Real Databases − Expressive Queries − Limited Queries

  • Goal
  • Query Framework

− Query Components − Query Structure − Basis − Semantics & Syntax − Syntactical Constructions

  • Present work
  • Conclusions

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 2 / 29

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SLIDE 3

Overview

  • Motivation

− Crisp Databases − Fuzzy Databases − Real Databases − Expressive Queries − Limited Queries

  • Goal
  • Query Framework

− Query Components − Query Structure − Basis − Semantics & Syntax − Syntactical Constructions

  • Present work
  • Conclusions

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 3 / 29

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SLIDE 4

Crisp Databases

Restaurants table

name distance price avg. food type Il tempietto 100 30 italian Tapasbar 300 20 spanish Ni Hao 900 10 chinese Kenzo 1200 40 japanese

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Fuzzy Databases

Restaurants table

name close cheap similar to mediterranean Il tempietto 1 0.5 0.8 Tapasbar 0.9 0.8 0.9 Ni Hao 0.4 1 0.1 Kenzo 0.1 0.2 0.2

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Real Databases

  • Crisp values (traditional)
  • Easy update values
  • Fuzzy concept personalization
  • Less number of fields
  • Data objectivity

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Expressive Queries

I’m looking for a ...

  • restaurant of mediterranean food or similar and not far from the city

center

  • cheap flat of two rooms very close to the beach

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Limited Queries

I’m looking for a restaurant or a flat

  • mediterranean food
  • 2 rooms
  • cheaper than 30 euros menu

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 8 / 29

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SLIDE 9

Limited Queries

I’m looking for a restaurant

  • food type

= mediterranean

  • number of rooms = 1
  • price

30 euros

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Overview

  • Motivation

− Crisp Databases − Fuzzy Databases − Real Databases − Expressive Queries − Limited Queries

  • Goal
  • Query Framework

− Query Components − Query Structure − Basis − Semantics & Syntax − Syntactical Constructions

  • Present work
  • Conclusions

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 10 / 29

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Goal

  • Defining a flexible and expressive query structure
  • Providing a sound framework with a search engine of these queries over

traditional databases

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 11 / 29

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SLIDE 12

Overview

  • Motivation

− Crisp Databases − Fuzzy Databases − Real Databases − Expressive Queries − Limited Queries

  • Goal
  • Query Framework

− Query Components − Query Structure − Basis − Semantics & Syntax − Syntactical Constructions

  • Present work
  • Conclusions

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 12 / 29

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Query Components

I’m looking for a ...

  • restaurant of mediterranean food or similar and

individual value nfp co not far from the city center negation fp

  • cheap

flat of two rooms very close to the beach fp individual value nfp q fp

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Query Components

  • individuals (restaurant, film, house, ...)
  • comparison operands co (equal, distinct, greater, less, similar, ...)
  • fuzzy concepts fp (big, cheap, close to the beach, ...)
  • quantifiers q (quite, rather, much, very, little, ...)
  • crisp concepts nfp (prize, size, distance, food type, ...)
  • values (30000, 3, mediterranean, comedy, ...)

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Query Structure

I ′m looking for a/an individual

  • not

q fp whose nfp co value

  • AND

(1)

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 15 / 29

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Basis

RFuzzy (very expressive fusion information tool)

  • Multi adjoint Logic
  • Over Prolog (distributed computation, constraints, finite domains, ...)
  • Sugar Syntax
  • Similarity
  • Types
  • Combine crisp and fuzzy information
  • Incomplete information (default values, conditioned)

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Semantics and Syntax

  • Framework Semantics ← RFuzzy Semantics
  • Framework Syntax ← defined using RFuzzy predicates
  • Fuzzy concepts ← fuzzyfication of crisp values of nfp
  • Similarity ← explicit or advance definition
  • Configuration file ← fp definition

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Syntactical constructions

Database definition

define database(pT/pA, [(pN, pT ′)]) (2) define database(restaurant/4, [ (id, string type), (food type, enum type), (years since opening, integer type), (distance to the city center, integer type)]). (3)

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Syntactical constructions

Similarity Definition

similarity between(pT, pN(V 1), pN(V 2), TV ) (4) with credibility(credOp, credVal) (5)

  • nly for user ′UserName′

(6) similarity between(restaurant, food type(mediterranean), food type(spanish), 0.8) (7) similarity(pT(pN(V 1, V 2)))

(p, v), &i

← − − − − − − TV if COND (8)

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 19 / 29

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Syntactical constructions

Truth value Definition

fPredName(pT) :∼ value(TV ) (9) if (pN(pT) cond value). (10) cheap(restaurant) : value(0.1) if (id(restaurant) is equal to zalacain). (11) fPredName

(p, v), &i

← − − − − − − TV if COND (12)

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Syntactical constructions

Fuzzification Definition

fPredName(pT) :∼ function(pN(pT), [(valIn, valOut)]) (13) traditional(restaurant) : function( years since opening(restaurant), [(0, 0), (5, 0.1), (10, 0.4), (15, 1), (100, 1)]). (14) fPredName(valIn)

(p, v), &i

← − − − − − − OP if COND (15) OP = valIn ∗ (valOut2 − valIn1) (valIn2 − valIn1) (16) COND′ = (valIn1 < valIn < valIn2) (17)

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Syntactical constructions

RFuzzy Rule Definition

fPredName(pT) :∼ rule(aggr, complexBody) (18) fPredName(pT) :∼ rule(simpleBody) (19) fPredName

(p, v), &i

← − − − − − − aggr(complexBody) if COND (20) fPredName

(p, v), &i

← − − − − − − simplexBody if COND (21)

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SLIDE 23

Syntactical constructions

Default Values Definition

fPredName(pT) :∼ defaults to(TV ) (22) near the city center(restaurant) :∼ defaults to(0). (23) fPredName

(p, v), &i

← − − − − − − TV if COND (24)

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 23 / 29

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SLIDE 24

Overview

  • Motivation

− Crisp Databases − Fuzzy Databases − Real Databases − Expressive Queries − Limited Queries

  • Goal
  • Query Framework

− Query Components − Query Structure − Basis − Semantics & Syntax − Syntactical Constructions

  • Present work
  • Conclusions

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SLIDE 25

Present work

Prototype

  • Web Interface Application
  • Parametric (databases)
  • Personalized (fuzzy concepts)

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 25 / 29

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SLIDE 26

Overview

  • Motivation

− Crisp Databases − Fuzzy Databases − Real Databases − Expressive Queries − Limited Queries

  • Goal
  • Query Framework

− Query Components − Query Structure − Basis − Semantics & Syntax − Syntactical Constructions

  • Present work
  • Conclusions

(UPM, Spain) Getting Answers to Fuzzy and Flexible Searches FCTA’2013, Portugal 26 / 29

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Conclusions

  • Useful, expressive query structure (friendly user interface)
  • Flexible and fuzzy search included combination with crisp search and

similarity

  • Easily implemented over CLP
  • Adequate for any kind of traditional database
  • Multiple possibilities of personalization

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Questions

Thank you

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Getting Answers to Fuzzy and Flexible Searches by Easy Modelling of Real-World Knowledge

Victor Pablos-Ceruelo and Susana Mu˜ noz-Hernandez susana@fi.upm.es

Universidad Polit´ ecnica de Madrid (T.U. of Madrid), Spain

FCTA’2013, Vilamoura, Algarve, Portugal

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