Towards Fine-grained Service Matchmaking by Using Concept Similarity

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Towards Fine-grained Service Matchmaking

by Using Concept Similarity

Alberto Fernández, Axel Polleres, Sascha Ossowski {alberto.fernandez,sascha.ossowski}@urjc.es

[email protected]

University Rey Juan Carlos (Madrid - Spain)DERI, National University of Ireland, Galway

SMR2’07. ISWC, Busan.

Nov. 11 – 15, 2007.

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Nov. 11 – 15, 2007.

Outline

Introduction Concept Similarity Matching Semantic Web Services Towards a combined notion of similarity-based SM Conclusions

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Nov. 11 – 15, 2007.

Outline


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Nov. 11 – 15, 2007.

Introduction

Location and selection of services in SOA Service Descriptions

Provided services (advertisements) Service requests Both based on shared formal ontologies

Notions of match between advertisements and requests

Subsumption checking Boolean (or several degrees of) match

Concept similarity Numerical (fine grained)

Objective: Unified framework: Notions of match + concept similarity

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Nov. 11 – 15, 2007.

Outline


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Nov. 11 – 15, 2007.

Concept Similarity

Semantic distance approaches Rada et al.: Shortest path between two concepts in the

taxonomydist(c1, c2) = depth(c1) + depth(c2) − 2 × depth(lcs(c1, c2))

Leacock & Chodorow

Fernandez et al.

H

), cdist(c -) , cs(crelatednes

2log 21

21

otherwise

csubsumescife

csubsumescife

ccif

ccsimccdist

ccdist

02

12

1

2

1

1

),(

21),(

12),(

21

21

21

21

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Nov. 11 – 15, 2007.

Semantic distance: taking depth into account Wu & Palmer

Li et al.

Concept Similarity

t,lcs)length(roo),h,cdist(c,l,βα

otherwise

ccifee

eee

),csim(c βhβh

βhβhαl

21

2121

00

1

t,lcs)length(rooN

,lcs),length(c,lcs),Nlength(cN

NNN

N),csim(c

3

2211

321

321 2

2

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Nov. 11 – 15, 2007.

Concept Similarity

Feature-based approaches (Tversky) Contrast model

contrast(C,D) = f(ftrs(C) ftrs(D))−f(ftrs(C)\ftrs(D))−f(ftrs(D)\ftrs(C)) f(·) is usually the count of features, ftrs(C) set of features in

C number of common minus the number of non-common

features Ratio model

Which is commonly taken as

ftrs(C))f(ftrs(D)\ βftrs(D)) f(ftrs(C)\α ftrs(D)) f(ftrs(C)

ftrs(D))f(ftrs(C) sim(C,D)

f(ftrs(D))f(ftrs(C))

ftrs(D))f(ftrs(C) sim(C,D)

2

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Nov. 11 – 15, 2007.

Concept Similarity

Information Content approaches pr(c) = probability of an individual being described by a

specific concept c Resnik

sim(c1, c2) = IC(lcs(c1, c2)) = −log pr(lcs(c1, c2))

Jiang & Conrathsim(c1, c2) = IC(c1) + IC(c2) − 2 × IC(lcs(c1, c2))

Lin

) IC(c) IC(c

)), cIC(lcs(c),csim(c

21

2121

2

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Nov. 11 – 15, 2007.

Concept Similarity

Description Logics approaches Borgida et al.

Applyies distance, feature and information content models Very simple DL (A): only conjunctions

Di Noia et al. potential match (some requests in demand D are not specified in

S): the number of concepts names in D not in S, the number of number restrictions of D not implied by those of S add recursively rankPotential for each universal role quantification in

D Fanizzi & d’Amato

define a similarity measure between concepts in ALN DL. decompose the normal form of the concept descriptions:

Primitive concepts: ratio of common individuals wrt. either conjunct. Value restrictions: computed recursively, the average value is taken. Numeric restrictions: ratio of overlap, the average value is taken

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Nov. 11 – 15, 2007.

Concept Similarity

Information Retrieval approaches OWLS-MX (Klusch et al.)

logic-based reasoning is complemented by IR based similarity

four different token-based string metrics the cosine the loss of information the extended Jacquard Jensen-Shannon information divergence

applied to unfolded concepts: (and C (and B (and A))) corresponds to the concept C (C B

A).

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Nov. 11 – 15, 2007.

Concept Similarity: compound concepts

Rada et al. Disjunction

Conjunction

Ehrig et al. (cosine) = (sim(e, e1), sim(e, e2), . .

. , sim(e, f1), sim(e,

f2), . . .),

Sierra & Debenham

1 2

),(||||

1

0

),(

21

21

21

Vu Vv

otherwisevudistVV

VVif

VVdist

,C)}{dist(C C) C. . . dist(C ii

k min,1

FfEe

FfEe

fe

fe

FEsim ),(

)},({minmax),()()(

dcsimsimOdOc

e

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Nov. 11 – 15, 2007.

Outline


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Nov. 11 – 15, 2007.

Matching SWS: notions of match

Paolucci et al. An advertisement (S) matches a request (R) iff

for each output of R there is a matching output in S. for each input of S there is a matching input in R.

Degree of match for outputs (inverse for inputs) Exact: OUTR and OUTS are equivalent or OUTR subclass of OUTS Plug In: OUTS subsumes OUTR

Subsumes: OUTR subsumes OUTS

Fail: no subsumption relation If there are several outputs with different degree of match,

the minimum degree is used The set of service advertisements is sorted by comparing

output matches first

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Nov. 11 – 15, 2007.


OWLS-MX Hybrid: Logic based + Syntactic IR based similarity Matching filters

Exact: INS INR: INS= INR OUTR OUTS: OUTR= OUTS

Plug In: INS INR: INS INR OUTR OUTS: OUTS LSC(OUTR)

Subsumes: INS INR: INS INR OUTR OUTS : OUTR OUTS

Subsumed-by: INS INR: INS INR OUTR OUTS: (OUTS= OUTR OUTSLGC(OUTR))

SIMIR(S,R) Logic-based fail: above logic based filters fail Nearest-neighbour:

INS INR: INS INR OUTR OUTS: OUTR OUTS SIMIR(S,R) Fail

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Nov. 11 – 15, 2007.


Li & Horrocks One DL concept defines the inputs and one the outputs Extend the degree levels proposed by Paolucci

Exact: if S = R Plug In: if R S Subsume: if S R Intersection: if (S⊓R ) Disjoint: if S ⊓ R

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Nov. 11 – 15, 2007.

Outline

Introduction Concept Similarity Matching Semantic Web Services Towards a combined notion of similarity-

based SM Conclusions

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Nov. 11 – 15, 2007.

Towards a combined notion of simil.-based SM

Notion of similarity match (NoSM) Real number in [0..1]

Notion of match Logic-based, coarse grained Several levels of match NoM {exact, level1, level2, …, leveln,

fail} Refining with concept similarity (sim)

Real number in [0..1] Aggregation

Compound concepts (e.g. set of inputs) Components: Inputs, Outputs, Operations Maintaining NoM (logic-based) semantic

0

1

sim

1

0

NoSMNoM

level1

level2

leveln

exact

fail

.

.

.

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Nov. 11 – 15, 2007.

Outline


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Nov. 11 – 15, 2007.

Conclusions

Concept Similarity Distance is commonly used …

Assumes equally distributed instances over concepts Difficult to apply to DL

Adoption of canonical representation? Spanning tree of pre-classification, new atomic concept names for R.C, R.C, …

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Nov. 11 – 15, 2007.

Example

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Nov. 11 – 15, 2007.

Conclusions

Concept Similarity Distance is commonly used …

Assumes equally distributed instances over concepts Difficult to apply to DL

Adoption of canonical representation? Spanning tree of pre-classification, new atomic concept names for R.C, R.C, …

… but other approaches exist (features, IC, IR …) Concept definitions vs instances

Matching SWS Most current approaches based on inputs/outputs Logic based reasoning: subsumption Several (non-numerical) degrees of match

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Nov. 11 – 15, 2007.

Conclusions and further work

Notion of similarity-based service matching Using concept similarity to refine notion of match Fine-grained degree of match: facilitates service ranking

Open issues Which service description framework to focus on? OWL-S,

WSMO, etc, or a new one to which these approaches could be easily mapped?

Which concept similarity measure better fits our framework? Is there a single “best” measure? What are the conditions that it must fulfill?

How should values corresponding to different elements be combined?

Do different applications require the same framework or should it be adapted for each of them?

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Thanks!!

Questions?

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Towards Fine-grained Service Matchmaking by Using Concept Similarity