RiMOM: A Dynamic Multistrategy

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13 Δεκ 2013 (πριν από 3 χρόνια και 10 μήνες)

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By:
Juanzi Li, Jie Tang, Yi Li and Qiong Luo


Presenter:
Abhijit

Gali

RiMOM
: A Dynamic
Multistrategy

Ontology Alignment Framework

RiMOM


A systematic approach to quantitatively estimate
the similarity characteristics for each alignment
task and strategy selection method to automatically
combine the matching strategies based on two
estimated factors

Problems faced by ontology alignment


Combination of different strategies for ontology
alignment


When to use the combination strategies

Ontologies

and ontology alignment

Ontology


Definition 1: An ontology is a formal specification of a
shared conceptualization. We describe the ontology as a
6
-
tuple : O={C, P,
Hc
, Hp,
Ao
, I}


OWL provides vocabularies to define the formal
semantics of ontology


owl:Class

and
rdfs:subClassOf

define the concepts and
subconcepts



rdfs:Property

and
rdfs:subPropertyOf

define property
and
subproperties



rdfs:domain

and
rdfs:range

of a property define what
concepts can have the property and what instances of
the concepts can be the values of the property.




Concept description
-
Description(c): A concept
c
є

C is described by a 4
-
tuple:


Description (c)={Meta(c),
Hier
(c), Rest(c), Inst(c)}


Property description
-
Description(p): A property p
є
P is described by a 5
-
tuple:


Description (p)= {Meta(p),
Hier
(p),
Doma
(p),
Rang(p), Inst(p)}



Ontology alignment



Definition: Given two
ontologies

O1 and O2, an
alignment (or alignment task) finds, for each entity
in O1, a corresponding entity in O2. O1 is called the
source ontology and O2 the target ontology.

Align(O1,O2)={(ei1, ei2,
coni
,
relationi
)|ei1
є

O1, ei2
є

O2,
coni

є

[0,1],
relationi

є

(exact, narrower,
broader, overlap)}




Fragments of three
ontologies

to be
aligned

Dynamic
Multistrategy

Ontology
Alignment



Goal
-

to detect a selection strategy and how
confident we should be about the strategy


Tasks : a) Definition of criteria for selection



strategy


b) Dynamic selection of multiple strategies

Similarity Factors between Two
Ontologies


Label similarity factor: similarity between two
ontologies

based on the entities’ names

F_LS(O1, O2)=
#
iden_conc
-
label +#
iden_prop_label


max(|C1|+|P1|,|C2|+|P2|)


Structure similarity factor: similarity of two
ontologies

based on their structure information

F_SS(O1, O2)=
(#
comm_nonl_conc
+#
comm_nonl_prop
)


(max(#nonl_C1+#nonl_P1, #nonl_C2+#nonl_P2)

SIMILARITIES AND OVERVIEW OF
RiMOM

Entity similarity


For two concepts :


sim
(e1,e2)= f(
sim_Meta
(e1,e2),
sim_Hier
(e1,e2),
sim_Rest
(e1,e2),
sim_Inst
(e1,e2) )


For two properties:


sim
(e1,e2)= f (
sim_Meta
(e1,e2),
sim_Hier
(e1,e2),
sim_Doma
(e1,e2),
sim_Rang
(e1,e2),
sim_Inst
(e1,e2))


RiMOM

alignment processing flow


Overview of
RiMOM


Preprocessing


Linguistic
-
based ontology alignment


Similarity combination


Similarity propagation


Alignment generation and refinement

ONTOLOGY ALIGNMENT STRATEGIES
IN
RiMOM

Linguistic
-
Based Strategies



Edit
-
Distance
-
Based Strategy
-

involving calculation
of
sim_Name
(w1, w2) and
sim_Name
(e1, e2)


Vector
-
Distance (VD)
-
Based Strategy

Structure
-
Based Strategies



Pairwise

Connectivity Graph (PCG) construction
and similarity propagation


Directed Labeled Graph(DLG) has edges
represented by triple (
s,p,o
)


Construction of DLG_O using
HasSubConcept
,
HasSibling
,
HasProperty
,
HasRange
, and
HasSubProperty


Construction of SPG_O using nodes that are entity
pairs from two
ontologies

that have some structural
relationship in common



Example of DLG_O and SPG_O

STRATEGY SELECTION

Feature Selection in Vector
-
Distance
-
Based

Strategy


Determination of Hierarchical Information Use:
F_SS> threshold
ε
1


Enhancement of Structure Information: Depends
on the path length from the root concept, the
number of properties, and the number of
subconcepts

of the current entity

Weight Calculation of Similarity
Combination


sim
(e1,e2)=
(
wname
σ
(
sim_Name
(e1,e2))+
wvec

σ
(
sim_Vec
(e1,e2)))


(
wname+wvec
)

σ
(x)= 1/(1+exp(
-
5(x
-
α
))), where
α
=0.5

wname
= F_LS/ max(F_LS, F_SS)

wvec
= F_SS/max(F_LS, F_SS)




Selection of Similarity Propagation Strategy:

1.
Concept
-
Concept(CC)
-

HasSubclass

and
HasConceptSibling

relations

2.
Concept
-
Property(CP)
-

HasRange

and
HasProperty

relations

3.
Property
-
Property(PP)
-

HasSubproperty

and
HasPropertySibling

relations



Parameter Setting

EVALUATION

Test Sets and Evaluation Methods


Benchmark Data Set in OAEI 2006


Name, comments, specialization hierarchy,
instances, properties, classes, additions of 4 real
ontologies


Directory and Food Data Sets in OAEI 2006


i
) SKOS version of the United Nations Food

ii) SKOS version of the United States National
Agricultural Library


Evaluation Metrics:
i
) Precision(P) ii) Recall (R)

Results on Benchmark Data Set





(a) F SS in VD
-
based strategy (b) F SS in SF (c) Combined effects of F SS

Result on OAEI 2007

Graph of the precision and recall. (a) OAEI 2006. (b) OAEI 2007

Summary


High performance


Effectiveness of strategy selection


Contribution of the SF strategy


Inefficiency for dealing with large
-
scale
ontologies

Related Work


Schema Matching
-

COMA , Rondo, and Cupid are
three composite methods


Ontology alignment and the combination of
multiple ontology alignment strategies


Structure
-
based ontology alignment


Relationship with other alignment methods

Relationship with Several Classical
Methods

CONCLUSION


A
multistrategy

framework,
RiMOM
, to
automatically and dynamically compose strategies
for individual ontology alignment tasks was
proposed


Experimental results on the data sets from OAEI
2006 and OAEI 2007 demonstrate that the system
performs better than most of the participants and
is among the top three performers on the
benchmark data sets

QUESTIONS ???