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
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k
f
a
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ap
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)))((( )( DBq faaspap
Model Translation operator :
Query format :
Translated query :
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pfapT
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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
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else1))((
thenback tracedbe could and If
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Syntactic Similarity (2) : Not all attributes in as are preserved
)(selection Similarity Syntactic : ||||
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attributeeach of importance the: ),...,(
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else))((
thenback tracedbe could and If
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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 : ||||
))(,(
),...,(
)))((())((
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lescommon tup of #FD thesatisfying lescommon tup of #))((
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Semantic Similarity : schema level Cycle analysis : what happened to the original
attributes of the query?
level) (schemaSimilarity Semantic : ||||
))(,(
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cycle negative : },{)( : 3 caseneutral : )( :2 case
cycle positive : }{)( : 1 case
)(
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pppqTfv
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P1 : the likelyhood of the cycles, given translation being correct
P2 : the likelyhood of the cycles, given translation being incorrect
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)),||,(||1(),||,(||
)),||,(||1(),||,(||)1( ),...,(: }c ..., ,{c cycles ofset a having of likelihood The)1)()1(1()1(),||,(||
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: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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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