The Chatty Web : Emergent Semantics Through

Gossiping

Karl Aberer, Philippe Cudre-Mauroux, Manfred Hauswirth

Presented by Yookyung Jo

Overview : This paper presents

Achieving an effective global semantic agreement

starting from local semantic agreements In loosely coupled information sharing systems

Overview : Table of Contents

Motivation Problem Definition Model Similarity measures Algorithm Case Study Comments

Motivation : The need for global semantic agreement

P2P systems Semantic Web

Their approach global agreement starting from local agreement Self-organizing behavior

Application Scenario Meta-data support for P2P applications federating, loosely-coupled databases

Problem Overview : Loosely coupled information sharing system Local schema mappings available

Missing attributes Possibly erroneous

Mapping graph Transitivity : semantic gossiping Cycle : assess the quality of mappings

End result Routing link assessment => query routing

decision Gradual reinforcement of the correct mappings

System Overview :

Semantic network Semantic neighbors and Semantic translations

Model : Each peer : a single relational table Basic relational algebra :

Query :

),...,,(: functions oflist a is )( : Mapping)( : Projection

,...,,(,,...,, )( :Selection

210

)2121

)(

k

f

a

kk

ap

AAAFAfRR

AAApAAAaR

)))((( )( DBq faaspap

Model Translation operator :

Query format :

Translated query :

))(()())(())((

''

'''

pfapT

pTppppp

DBDBqDBqqDBqT

on trace translatia is ),,,(

TTTTpqidquery

)))))((((())(( ')(' pfafaaspapp DBDBqT

Neighbor schema : A_1’’(city), A_2’’(addr), A_3’’(job), …, A_k’’

A_1’(addr), A_2’(title),…,A_j’(phone)…,A_r

Own schema : A_1’(addr), A_2’(title), A_i,A_j’(phone)…, A_m’ A_1, A_2, A_3, … , A_s, …, A_n

A_1, A_3, … , A_x

A_2, A_3, … , A_y

)))))((((())(( ')(' pfafaaspapp DBDBqT

f : mapping

a : projection

fa : mapping

Original query

translation

)( :selection asp

ap : projection

Query routing

Query routing based on similarity measures “similarity measures”, given q, TT, candidate translation link How promising is it to route a query through the translation

link Good semantic agreement => good similarity measures The reverse is not true!

Similarity measures Syntactic similarity

For selection in query For projection in query

Semantic similarity At the schema level At the data level

),,,( TTpqidquery

Syntactic Similarity (1) : Not all attributes in ap are preserved

n)(projectio Similarity Syntactic : ||||

))(,(

attributeeach of importance the: ),...,(

),...,())((

0))((

else1))((

thenback tracedbe could and If

...1

...1

...1

...1

1

1

FVWFVWqTqS

wwW

fvfvqTFV

qTfv

qTfv

AapA

n

AA

AAn

nA

nA

ii

k

k

i

i

Syntactic Similarity (2) : Not all attributes in as are preserved

)(selection Similarity Syntactic : ||||

))(,(

attributeeach of importance the: ),...,(

),...,())((

0))((

else))((

thenback tracedbe could and If

...1

...1

...1

...1

1

1

FVWFVWqTqS

wwW

fvfvqTFV

qTfv

selqTfv

AasA

n

AA

AAn

nA

AnA

ii

k

k

i

ii

Semantic Similarity : data level Semantic agreement => preservation of data dependency Cycle (in query routing) => Checks the functional

dependency

Given an attribute , outgoing translation link

level) (data similarity Semantic : ||||

))(,(

),...,(

)))((())((

))((

lescommon tup of #FD thesatisfying lescommon tup of #))((

...1

...1...1

,...1

1

FVWFVWqTqS

fvfvFV

qTTfvqTfv

qTfv

qTfv

j

AA

kjkAikAi

jkkAi

jAi

k

iA jT

Semantic Similarity : schema level Cycle analysis : what happened to the original

attributes of the query?

level) (schemaSimilarity Semantic : ||||

))(,(

)))((())(())((

))((

cycle negative : },{)( : 3 caseneutral : )( :2 case

cycle positive : }{)( : 1 case

)(

...1

...1...1,...1

21

1

1...1

FVWFVWqTqS

qTTfvqTfvqTfv

pppqTfv

ijAAsourceAsource

AAsource

qTq

n

kjkiAkiAjkkiA

jiA

jiT

iT

iiT

nn

P1 : the likelyhood of the cycles, given translation being correct

P2 : the likelyhood of the cycles, given translation being incorrect

d d ),...,(lim ,d d ),...,(lim

)),||,(||1(),||,(||

)),||,(||1(),||,(||)1( ),...,(: }c ..., ,{c cycles ofset a having of likelihood The)1)()1(1()1(),||,(||

:incorrect slation given tran awith negative, being cycle a ofy probabilit The))1(1()1(),||,(||

:correct slation given tran awith positive, being cycle a ofy probabilit The

nstranslatioincorrect ofy probabiliton compensati : ongslation wrother tran ofy probabilit the:

ongslation wrgiven tran theofy probabilit the:

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10 101

1

k1

1||||1||||

1||||1||||

fe kefe ke

Ccfi

Ccfis

Ccfi

Ccfisk

cif

ciffi

cif

ciffi

f

s

ecclpecclp

ecqecqe

ecpecpeccl

eeecq

eeecp

ee

fs

fs

ii

ii

Gossiping Algorithm :

Upon reception of a query message: Detect any semantic cycles Forward it to the local neighbor, if needed Return potential results

For each outgoing translation links : Apply the translation to the query Update the similarity measures Perform similarity tests forward the query only to the links that pass all similary tests

),,,,,,,,,,( min FVFVFVFVSselWTTpqidquery

Case Study :

T(A->C) : no translation for “title”T(A->D) : erroneous translation for “title” -> “acronym”

Case Study :Query = FOR $project IN “project_A.xml”/* RETURN <title>$project/title</title>

Case Study :

Cycle T(A->D) erroneous

T(B->D) erroneous

A,B,D,E,A + -A,B,D,E,F,A + -A,B,E,A + +A,B,E,F,A + +A,B,F,A + +A,D,E,A - +A,D,E,B,F,A - +A,D,E,F,A - +

Syntactic similarity (selection) : not applicable

Syntactic similarity (projection) : T(A->C) is not used

Semantic similarity (data level) : not applicable (no FDs)

Semantic similarity (schema level) : as shown in the table

Comments : Interesting problem framework :

Assumption : Deriving a global semantic agreement based on local semantic agreements

Solution : Similarity measures based on syntactic, semantic agreement, No global processing

Questions on technical details Similarity measure :

the size of FV does not matter? Semantic similarity at schema level

Serious empirical evaluation over a specific domain is desired

Comments : Related Work

OBSERVER, KRAFT, EDUTELLA Schema matching at the local level : GLUE

A perspective : Local relationships exploited to derive the

global assessment on their quality Semantic Interoperability :

Important in loosely coupled information sharing system

A key issue to the success of Semantic Web