1

Argument-based Critics and Recommenders:

A qualitative perspective on user support systems

Universitat de Girona (Febrer 2006)

Carlos Iván Chesñevarcic@eps.udl.es

Artificial Intelligence Research GroupDepartment of Computer Science - University of Lleida

Lleida, Catalunya (Spain)

2

Where I am from…

Univ. Nacional del Sur Bahía Blanca (Argentina)

University of Lleida Lleida, Catalonia (Spain)

3

Research linesResearch lines

•• Theory of Argumentation in a CS settingTheory of Argumentation in a CS setting

•• Argumentation: Applications and extensionsArgumentation: Applications and extensions

I have been working in I have been working in defeasible defeasible argumentationargumentation since 1992. since 1992.

Main research lines:

4

Applications & extensions Applications & extensions •• Recommender Systems and argumentationRecommender Systems and argumentation

•• Semantic Web Applications of ArgumentationSemantic Web Applications of Argumentation

• Learning and argumentationLearning and argumentation

•• Decision Theoretic extensions of DeLPDecision Theoretic extensions of DeLP

•• Integrating DeLP and Neural Networks Integrating DeLP and Neural Networks

•• Implementation Issues in Argument SystemsImplementation Issues in Argument Systems

5

About this talk• Argumentation systems provide a sound setting to formalize qualitative reasoning, with a growing number of real-world applications.• Recommender Systems (or suggesters) have evolved as sophisticated tools to assist user in computer-mediated tasks (mainly web-oriented).• Proposal: to integrate RS technologies with an argumentation-based framework called DeLP, based on extended logic programming.

6

Outline

•(2) Recommender Systems (RS)•(1) Argumentation & DeLP

•(3) Argument-Based RS

•(4) A case study: • ArgueNet: An Argument-Based Search Engine• Argumentation for Natural Lang. Assessment

•(5) Conclusions. Ongoing work.

7

Defeasible Argumentation: a very brief overview

• When a rule supporting a conclusion may be defeated by new information, we say that such a rule is defeasible.• When we chain defeasible reasons to reach a conclusion, we say that we have an argumentinstead of a proof (in a logical sense).• Arguments may compete, rebutting (attacking) each other, so that the process of argumentation is a natural result of a search for arguments.• Argumentation systems formalize the above notions in different ways.

8

Logic Programming & Defeasible Argumentation

• Recently several approaches based on combining logic programming & defeasible argumentation have been proposed.• Defeasible Logic Programming (DeLP) is one of such approaches.• DeLP has been particularly promising for modelling problems in the context of developing real-world applications.

9

Logic Programming

IntelligentAgents

Multiagent Systems

DefeasibleArgumentation

DeLP: related areas

DeLP

10

DeLP: SyntaxA fact is a ground literal. E.g.: innocent(mark)

A strict rule is denoted Lo ← L1 ,L2 , ... ,Ln

and stands for sound information

A defeasible rule is denoted Lo % L1 ,L2 , ... ,Ln

and stands for tentative information

~guilty(mark) ← innocent(mark)mammal(X) ← dog(X)

flies % bird~good_weather % low_pressure

11

Defeasible Logic Program A Defeasible Logic Program (delp) is a set of facts, strict rules and defeasible rules denoted P= (Π , ∆),where- Π is a set of facts and strict rules.- ∆ is a set of defeasible rules.

P

Sample DeLP program

buy_stock(X) % good_price(X) ~buy_stock(X)%good_price(X),risky_company(X)risky_company(X) % in_fusion(X,Y)risky_company(X) % closing(X)~risky_company(X) % in_fusion(X,Y), strong(Y)good_price(acme) in_fusion(acme, steel) strong(steel)

12

Defeasible DerivationA defeasible derivation for L from P= (Π , ∆) is a finite sequence of ground literals L1 ,L2 , ... , Lnsuch that for each literal Li- Li is a fact, or- there exists a rule in P with head Li and bodyB1,B2,.. Bk and each literal of the body is in L1,L2,..,Li-1

A set S ⊆ (Π,∆) is contradictory if there exists adefeasible derivation for a pair of complementary literalsp and ~p from S.Obs: for any delp P= (Π , ∆), the set Π is required to be not contradictory.

13

buy_stock(X) % good_price(X) ~buy_stock(X)%good_price(X),risky_company(X)risky_company(X) % in_fusion(X,Y)risky_company(X) % closing(X)~risky_company(X) in_fusion(X,Y), strong(Y)good_price(acme) in_fusion(acme, steel) strong(steel)

Example

A derivation for the literal“buy_stock(acme)” can be built from the sequence:good_price(acme)

buy_stock(acme)

14

Examplebuy_stock(X) % good_price(X) ~buy_stock(X)% good_price(X),risky_company(X)risky_company(X) % in_fusion(X,Y)risky_company(X) % closing(X)~risky_company(X) % in_fusion(X,Y), strong(Y)good_price(acme) in_fusion(acme, steel) strong(steel)

But..there is also a derivationfor “~buy_stock(acme)”:

good_price(acme)In_fusion(acme,steel)

risky_company(acme)~buy_stock(acme)

15

Argument•An argument for a ground literal h from P= (Π , ∆) is a pair <A,h> where A is a set of defeasible rules such that:(1) there exists a defeasible derivation for h from A∪Π.(2) A∪Π is not a contradictory set.(3) A is a minimal set satisfying (1) & (2)

Example: < A ,~buy_stock(acme)>A={ ~buy_stock(acme) % good_price(acme),

risky_company(acme);risky_company(acme) % in_fusion(acme,steel) }

16

Argument (example)

h

A

q

S

An argument <S,q> is a sub-argument of <A,h> iff S ⊆ A

~buy_stock(acme)

good_price(acme) risky_company(acme)

good_price(acme) in_fusion(acme,steel)

in_fusion(acme,steel)

A={ ~buy_stock(acme) % good_price(acme),risky_company(acme)

risky_company(acme)%in_fusion(acme,steel) }

17

Counter-argument

<B,p> is a counter-argument for <A,h> ifthere exists a subargument <S,q> of <A,h>such that {p,q} ∪ Π is a contradictory set.Note: the subargument can be <A,h> itself.

h

A

q

S

p

B

h

A

p

B

18

Example of counterarguments

~risky_company(acme)

in_fusion(acme,steel) strong(steel)

in_fusion(acme,steel) strong(steel)

~buy_stock(acme)

good_price(acme) risky_company(acme)

good_price(acme) in_fusion(acme,steel)

in_fusion(acme,steel)

19

Comparison of arguments

Arguments and counterarguments arearguments in conflict.

An argument can be deemed better than another by using a preference criterionamong arguments: “≤” ⊆ Args x Args

DeLP relies on specificity as a syntax-based preference criterion.

20

Comparison of argument (1)

• Specificity criterion: prefers arguments(a) with more information or (b) more direct

h

A

p

B

< <

p

B

h

A

Discussion: “Computing Generalized Specificity” (F.Stolzenburg-A.García – C.Chesñevar – G.Simari). Journal of Applied Non-Classical Logics, Vol.13, No. 1, pp.87-113, 2003.

21

Example

~risky_company(acme)

in_fusion(acme,steel) strong(steel)

in_fusion(acme,steel) strong(steel)

~buy_stock(acme)

good_price(acme) risky_company(acme)

good_price(acme) in_fusion(acme,steel)

in_fusion(acme,steel)

Preference criterion among arguments is modular (ie, specificity could be changed for other alternative criterion).

>spec

22

Defeater<B,p> is a defeater for <A,h> iff <B,p> is a counter-argument for <A,h> attacking the subargument <S,q>and:

a) <B,p> is strictly better than <S,q> wrt the preference criterion (proper defeat), or

b) <B,p> is not comparable with <S,q> wrt the preference criterion (blocking defeat)

h

A

q

S

p

B

23

DeLP with Default Negation

h % not pp % cc

h{ h% not p }

•DeLP can be extended to include default negated literals (not p) as new potential points of attack.

p{ p % c }

Blocking defeater

Discussion: “Relating Defeasible and Normal Logic Programming through Transformation Properties” (C.Chesñevar, J.Dix, F.Stolzenburg, G.Simari). Theoretical Computer Science, Vol.290, Issue 1, pp. 499-529, 2003.

24

This results in tree-like structure, in which every path can be thought of as an exchange of arguments between two parties (Pro and Con).

Arguments, defeaters, and defeaters for defeaters, ….

A

h As defeaters are arguments, they may on its turn be defeated by other arguments…

25

A0

h0

Argumentation lineAn argumentation line is a sequence

[<A0,h0>, <A1,h1>, <A2,h2>, <A3,h3>, <A4,h4>,...] where each argument (except for the first) is a defeater for the previous argument in the sequence.

h1

A1

h2

A2

h3

A3

h4

A4

Argumentation lines should be acceptable by verifying some additional constraints (e.g. no cycles) in order to avoid fallacious argumentation.

26

U

UD

D

D

U

UD

A

h

An argument <A,h> is warrantedif the root of the associated tree T<A,h> is labelled as U.

In order to determine whether an argument <A,h> is ultimately acceptable, a dialectical tree T<A,h> rootedin <A,h> is built.

Leaves are U-nodes.

Inner node is U iff everychildren node is a D-node.

Inner node is D iff at least one children node is a U-node.

Dialectical Tree

Arguments are undefeated or defeated nodes (U- or D-nodes).

27

How DeLP works

DeLPInterpreter

Defeasible rules

AbstractMachine

?- buy_stock(acme)

• YES (there exists a warrantedargument <A,h> )

• NO (there exists a warrantedargument for <A,~h>)

• UNDECIDED (none of theabove cases hold).

Possible Answers to Query h

User Query

Strict rules Facts

DeLP Program P

Obs: for any DeLP program P, it holds that h and ~h cannot be both warranted.

28

DeLP Implementation

• A special abstract machine (similar to Warren’s abstract machine for Prolog) was developed to efficiently solve queries in DeLP.• A Java-based Integrated Development Environment (IDE) for DeLP (based on this abstract machine) is available.• A web-based querying service for solving DeLP queries is currently under development, available at:

http://lidia.cs.uns.edu.ar/DeLP

29

DeLP Applications• Several DeLP-based applications have been developed.• E.g.: pattern recognition, web-based forms, recommender systems, etc.• Current research focuses on:Integrating DeLP and Knowledge Mangamentunder the JITIK Platform (in collaboration with TEC Monterrey, Mexico).Integrating DeLP and recommendation technologies (in collaboration with Ana Maguitman, Indiana Univ., USA & Univ. Nac. Del Sur, Argentina).

30

Outline

•(2) Recommender Systems (RS)•(1) Argumentation & DeLP

•(3) Argument-Based RS

•(4) A case study: • ArgueNet: An Argument-Based Search Engine• Argumentation for Natural Lang. Assessment

•(5) Conclusions. Ongoing work.

31

• Recommender Systems address the problem of information overload by providing guidelines or hints.

The Problem: Information Overload

32

Recommender Systems (RS)• RS are programs that create a model of the

user’s preferences or the user’s tasks to help identify worthwhile stuff (news, web pages, books, etc.)

• Find what users want.• Know what users like.• Gain trustworthiness from users.

Goals

33

Limitations of Traditional Views• Mostly unable to perform qualitative inference on

the recommendations.• Mostly unable to deal with the defeasible nature of

user’s preferences.• Unable to provide explanations: trustworthiness

issues!

Integrate recommender system technologies with DeLPOur Proposal

Remark: part of this research work is being jointly developed with Ana Maguitman (Indiana University, USA), who has been working with web-based suggesters, recommender systems and automated concept map generation (supported by a NASA Project).

34

Traditional Approaches to RS• Collaborative Filtering Recommenders: infer

preferences of individual users based on behavior of multiple users.

• Content-Based Recommenders: infer preferences of individual users based on what the user liked in the past.

• Hybrid Recommenders: combine both.

35

Hybrid RS: outline

36

Outline

•(2) Recommender Systems (RS)•(1) Argumentation & DeLP

•(3) Argument-Based RS

•(4) A case study: • ArgueNet: An Argument-Based Search Engine• Argumentation for Natural Lang. Assessment

•(5) Conclusions. Ongoing work.

37

Argument-based RS

Proposal: Model the users’ preference criteria in terms of a DeLP program built on top of a content-based search engine.

Users’ preference criteria are generally:•Incomplete.•Potentially Inconsistent.

As a basis for manipulating knowledge, we rely on techniques forconverting XML code into first-order formulas.A. Hunter, R. Summerton: Fusion Rules for Context-Dependent Aggregation of Structured News Reports. Journal of Applied Non-Classical Logics 14(3): 329-366 (2004)

38

Encoding Users’ Preferences

DeLP Program

P

Puser : preferences and behavior of active user

Ppool: preferences and behavior of pool of users

Pdomain: domain background knowledge

39

Argument-Based RS Architecture

40

Prioritizing Recommendations• Recommendations can be prioritized

according to their epistemic status:• Sw: warranted results: those results si for which

there exists at least one warranted argument supporting “rel(si)” wrt P’

• Sd: defeated results. those results si for which there exists at least one warranted argument supporting “~rel(si)” wrt P’

• Su: undecided results: results which are neither warranted nor defeated.

41

Distinguished predicate name to evaluate relevance of results

Algorithm RecommendOnQueryInput: Query Q, DeLP program P = Puser ∪ Ppool ∪ PdomainOutput: List LnewBeginLet L=[s1, s2, … sk] be the output of solving Q wrt content-

based search engine SEPsearch = {facts encoding info(s1), info(s2), … info(sk) }P’ = P ∪ PsearchInitialize Sw, Su, Sd as empty setsFOR EVERY si in L

DOSolve query “rel(si)” using DeLP program P’IF rel(si) is warranted wrt P’ THEN add si to Sw

ELSE IF ~rel(si) is warranted wrt P’ THEN add si to Sd

ELSE add si to Su

Return Lnew = [warranted si’s, undecided si’s, defeated si’s ]

42

Outline

•(2) Recommender Systems (RS)•(1) Argumentation & DeLP

•(3) Argument-Based RS

•(4) A case study: • ArgueNet: An Argument-Based Search Engine• Argumentation for Natural Lang. Assessment

•(5) Conclusions. Ongoing work."Argument-Based Critics and Recommenders: A Qualitative Perspective on User Support Systems" (C. Chesñevar, A. Maguitman, G. Simari). In Data & Knowledge Engineering (to appear), 2006

43

Argument-Based Search Engine

44

Case-Study: Solving Web Search Queries• Consider a journalist who wants to search for

news articles about recent outbreaks of bird flu.

Outbreaks of bird flu

?

news bird flu

Too many results!Proposal: rank search results according to their epistemic status wrt a DeLP program representing user’s knowledge

45

From search results into DeLP facts

news bird flu

S1

S2

S3

S4

Factsauthor(s1, chin_yao_lin)address(s1, “jpt.jp/...”)date(s1, 20031003)

author(s2, jen_doe)address(s2, “news.co.uk/...”)date(s2, 20001003)

author(s3, jane_truth)address(s3, “jpt.jp”)date(s3, 20031003)

author(s4, bob_beak)address(s4,“mynews.com/”)date(s4, 20031003)

S1

S2

S3

S4

Facts are extracted from HTML or XML tags in Google search results

46

Applying Implicit Knowledge• Articles written by Bob Beak are reliable.• Usually, if the journalist is trustworthy then the

article is reliable.• Old articles are not reliable.• If a journalist never faked a report then she is reliable.

• Thailandian and Japanese newspapers usually offer a biased viewpoint on bird flu outbreaks.

• The “Japanese Times” is non biased.

• Chin Yao Lin faked a report.

47

Sorting search resultsFor every search result si, analyze relevance of si wrt implicit knowledge

•List first all results warranted tobe relevant;•Then all those whose relevance is undecided;

•Finally, all those which are warranted to be non- relevant

Distinguished predicate “rel(si)”

author(s1, chin_yao_lin)….

author(s2, jen_doe)……

author(s3, jane_truth)….

author(s4, bob_beak)….

S1

S2

S3

S4

DeLP rules(Implicit User Knowledge)

DeLP program

48

Bird Flu: Worked example

thailandian(Url).%biased(Url)

D=“jpt.jp...”.japanese(Url),domain(Url,D),%~biased(Url)japanese(Url).%biased(Url)

address(X,Url),biased(Url).%~rel(X)not faked_news(A).%trust(A)outdated(X).author(X,A),trust(A),%~rel(X)author(X,A),trust(A).%rel(X)

Defeasible Rules

49

Bird flu: worked example

author(X,bob_beak).#rel(X)

[Computed elsewhere]#domain(Url,D)[Computed elsewhere]#getdate(T)

[Computed elsewhere]#japanese(X)[Computed elsewhere]#thailandian(X)(Today-D)>100.date(X,D),getdate(Today),#outdated(X)

#faked_news(chin_yao_lin)

Strict Rules

50

S1: Is this Article Relevant?author(s1,chin_yao_lin)address(s1, “jpt.jp/...”)date(s1,20031003)

rel(s1)

author(s1; chin_yao_lin) trust(chin_yao_lin)

not faked_news(chin_yao_lin)

~ rel(s1)

address(s1; “jpt.jp/...”) biased(“jpt.jp/...”)

japanese(“jpt.jp/...”)

~ biased(“jpt.jp/...”)

japanese(“jpt.jp/...”) domain(“jpt.jp/...”; “jpt.jp/...”) (“jpt.jp” = “jpt.jp”)

faked_news(chin_yao_lin)

S1

U

U

D

D

51

S1: Is this Article Relevant? (cntd)

~ rel(s1)

address(s1; “jpt.jp/...”) biased(“jpt.jp/...”)

japanese(“jpt.jp/...”)

author(s1,chin_yao_lin)address(s1, “jpt.jp/...”)date(s1,20031003)

S1

~ biased(“jpt.jp/...”)

japanese(“jpt.jp/...”) domain(“jpt.jp/...”; “jpt.jp/...”) (“jpt.jp” = “jpt.jp”)

U

D

Status of rel(s1): not warranted

Status of ~rel(s1): not warrantedStatus of s1 Undecided

52

S2: Is this Article Relevant?

rel(s2)

author(s2; jen_doe) trust(jen_doe)

not faked_news(jen_doe)

~ rel(s2)

~ author(s2,jen_doe) trust(jen_doe) outdated(s2)

not faked_news(jen_doe)

~ rel(s2)

author(s2, jen_doe)address(s2, “news.co.uk/...”)date(s2, 20001003)

S2Status of s2 Warranted as non-relevant

U

DU

53

S3: Is this Article Relevant?author(s3, jane_truth)address(s3, “jpt.jp”)date(s3, 20031003)

rel(s3)

author(s3, jane_truth) trust(jane_truth)

not faked_news(jane_truth)

~ rel(s3)

address(s3, “jpt.jp/...”) biased(“jpt.jp/...”)

japanese(“jpt.jp/...”)

~ biased(“jpt.jp/...”)

japanese(“jpt.jp/...”) domain(“jpt.jp/...”;“jpt.jp/...”) (“jpt.jp” = “jpt.jp”)

S3

Status of s3 Warranted as relevant

U

D

U

54

S4: Is this Article Relevant?author(s4, bob_beak)address(s4,“mynews.com/..”)date(s4, 20031003)

rel(s4)

∅

S4Status of s4 Warranted as relevant

S1

S2

S3

S4

S4

S3

S1

S2

After analysis based on implicit knowledge

Warranted as rel.

Warranted as rel.

Undecided

Warranted as non-rel.

55

Outline

•(2) Recommender Systems (RS)•(1) Argumentation & DeLP

•(3) Argument-Based RS

•(4) A case study: • ArgueNet: An Argument-Based Search Engine• Argumentation for Natural Lang. Assessment

•(5) Conclusions. Ongoing work.

56

Using The Web as a Corpus• A huge amount of sentences in natural languages are available as Web documents.

• Pattern matching capabilities of current search engines allow to use the Web as a linguistic corpus, reflecting the current status of a living language (e.g. English, Spanish, etc.).

• We developed an argumentative approach to provide proactive assistance for language usage assessment on the basis of the Web Corpus

"An Argument-based Decision Support System for Assessing Natural Language Usage on the Basis of the Web Corpus" (C. Chesñevar, M. Sabaté, A. Maguitman). In Intl. Journal of Intelligent Systems, Wiley (in press), 2006.

57

Assessing Natural Language Usage• Usage indices provide statistical measures which are relevant to assess natural language usage.

Situations like these can be detected via usage indices. Usage Indices can be implemented as built-in Prolog predicates, and computed from hit-counts associated with Google queries.

E.g: in English, “warm” and “hot” have similar meanings…However, “warm regards” is pragmatically valid in English whereas “hot regards” is not !

E.g.: in English, “associated with” is correct whereas “associated to” is incorrect (common error for Spanish speakers).

58

Usage indices: examplesConsider the strings “rearing children”, “parents”, and “of twins”, and domains “.uk” and “.babycentre.co.uk”. Then it holds that:

UC(“rearing children”,{“.uk”}) = 435UC(“rearing children”,Web) = 13700UR(“rearing children”,{“.uk”},Web) = 3.76UP(“parents”, “of twins”,{“.babycentre.co.uk”} )

= 677/747=0.906

• Constrained usage UC(s,D) = how many times a given string s appears in set D of web domains.• Ratio usage UR(s,D1,D2) = ratio between frequency of string s in D1 and frequency of string s in D2.• Prefix usage UP(s1,s2,D) = likelihood of finding a string s1immediately preceding another string s2 in a page from some domain in D.

59

Application Area

Consider an American journalist who writes articles in Spanish about Latinamerican issues intended for audiences both in Spain andArgentina....Spanish is not her mother tongue… she is in doubt about the usage of certain expressions which could lead to misunderstandings.

I’d like to write something about the “corralito”economic crisis in Argentina in 2001…

DeLP program encoding defeasible knowledge about language usage

60

Defeasible rules for language usageacc(S) % common_spanish(S)

~acc(S) % common_spanish(S),regionalism(S,[‘.ar’]).

~acc( S) % common_spanish(S),regionalism(S,[‘.es’]).

regionalism(S,Ctry) % locally_freq(S,[Ctry]).~regionalism(S,[‘.ar’]) % locally_freq(S,[‘.ar’]),

appears_in_news(S,’.es’).….

For the sake of simplicity, we assume only two Spanish speaking countries (Spain and Argentina).

61

Predicates based on Usage Indicesrare_in_spanish(S) #not common_spanish(S).

common_spanish(S) #spanish_speaking(Countries),V is Uc(S,Countries), V>200.

appears_in_news(S,Cty) #news_domains(Ds,Cty), V is Uc(S,Ds), V>100.

locally_freq(S,[‘.ar’]) #V is UR(S,[‘.ar’],[‘.es’]),V > 10.

locally_freq(S,[‘.es’]) #V is UR(S,[‘.es’],[‘.ar’]),V > 10.

spanish_speaking([‘.es’,’.ar’]).news_domains([‘elmundo.es’,’elpais.es’],’.es’).list_synonyms(liviano,[ligero,sutil, …]).

62

Control rules for language usage assessment

Solve(S) #acc(S), write(´Acceptable´)Solve(S) #~acc(S), repair(S,R),

acc(R ), write(´Accept. if rephrased as ´,R).Solve(S) #~acc( S), write(´Not acceptable´).Repair(S,R) # <replaces parts of S by synonyms

or related expressions using e.g. WordNet, given R as an output >

Acc(S) Holds iff S is a string that can be warranted to be accepted according to a DeLP program containing user-specified criteria

63

A Sample Paragraph

• “El corralito fue un fenómeno muy complejo [...] Para el colectivo de los trabajadores autónomos cualquier liviano error tenía consecuencias [....]”

• “The “corralito” was a very complex phenomenon [...] For the syndicate of autonomous workers any *light* mistake had consequences [...].”

64

Is Expression s1 acceptable?corralito

acc(s1)

commmon_spanish(s1)

~ acc(s1)

commmon_in_spanish(s1), regionalism(s1,’.ar’)

locally_freq(s1,’.ar’)

S1

Status of s1 Warranted as acceptable

U

D

Ulocally_freq(s1,’.ar’), appears_in_news(s1,’.sp’)

~ regionalism(s1,’.ar’)

UR(corralito,[‘.ar’],[‘.es’])=33.1 > 10

Uc(corralito,[‘.elmundo.es’,‘elpais.es’])=140 > 100

65

Is Expression s2 Acceptable?

acc(s2)

common_in_spanish(s2)

~ acc(s2)

common_in_spanish(s2), regionalism(s2)

locally_freq(s2,’.es’)

colectivo de los trabajadores autónomos

S2 Status of s2 Warranted as non-acceptable

U

D ~ acc(s2) U

UR(‘colectivo de..’,[‘.es’],[‘.ar’])=44 > 10

66

Is Expression s3 Acceptable?liviano error

S3

Status of s3 Warranted as non-acceptable

UU~ acc(s3)

rare_in_spanish(s3)

acc(s3’)

Status of s3’Warranted as acceptable

common_in_spanish(s3’)

ligero errorS3’

Repair s3

resulting ins3’

67

Text

Web Search Engine

s1…skDeLP

Program P

DeLP Interpreter

Suggestion

Userpreferences

Web Corpus

Compute built-in usage predicates

LexicalDatabase

si ={t1...tm}

s*i={t1...t*j ...tm}

Parser

Repairer

Critiquing System: outline

68

Outline

•(2) Recommender Systems (RS)•(1) Argumentation & DeLP

•(3) Argument-Based RS

•(4) A case study: • ArgueNet: An Argument-Based Search Engine• Argumentation for Natural Lang. Assessment

•(5) Conclusions. Ongoing work.

69

Conclusions • Information needs are complex for many

applications.– Users’ preferences and domain knowledge

are potentially inconsistent and incomplete.• Most traditional recommender systems are

unable to perform qualitative inference.• We have suggested a novel way of

enhancing recommendation technologies through the use of qualitative analysis using argumentation.

70

Thanks for your attention!Questions…?