SERIMI: Class-based Disambiguation for Effective Instance Matching over Heterogeneous Web Data

DelftUniversity ofTechnology

SERIMI: Class-based Disambiguation for EffectiveInstance Matching over Heterogeneous Web Data

Samur Araujo, Duc Thanh Tran, Arjen de Vries, Jan Hidders, Daniel Schwabe

Delft University of TechnologyWebDB 2012

2SERIMI: Class-based Disambiguation for EffectiveInstance Matching over Heterogeneous Web Data

Me You


AppleMe


You


You

?

Ambiguous


Me


You


Round Shape

Green Color

Eatable

Spherical Shape

Red Color

Eatable

My Apple Your Apple


Shape

Color

Eatable

Shape

Color

Eatable

My Apple Your Apple


FruitRound Shape

Green Color

Eatable

Spherical Shape

Red Color

Eatable

My Apple Your Apple


My Apple Your Apple


Instance Matching

Source

Target


“Instance matching uses a direct comparison paradigm”.


Source

Target

Is your Apple like my Apple?

Humm.. Maybe!


Homogenous data and schema.


The source and target descriptions overlap.

TargetSource


Syntactic Overlap

Population = TotalPopulation


Semantic Overlap

Population = Num_Inhabitants


Web of Data: heterogeneous data and schema


None or limited overlap between schemas

TargetSource


Instances do not instantiate the schema, properly.

TargetSource


Apple

Nutritional Information

Botanical Information


“Direct comparison paradigm does not apply”.

TargetSource


AppleMe


Me

AppleOrangePineapple


You

Apple

Me Orange

Pineapple


You


Food


FoodEatable


Source


My Apple Your Apple

My Orange Your Orange

My Pineapple Your Pineapple


“We use a class-based disambiguation paradigm …”


“We use a class-based disambiguation paradigm …” “… when there is no overlap between schemas.”


Instance Matching with SERIMI

Source

Target


Instance Representation



ValuePredicate

Instance



Roundshape

Apple1

Appletitle

Apple1

Redcolor

Apple1

EatableApple1category



Roundshape

Apple1

Appletitle

Apple1

Redcolor

Apple1


€

P(X) = {p | (s, p, o) ∈ IR(G,X) ∧s ∈ X},

D(X) = {o | (s, p, o) ∈ IR(G,X) ∧s ∈ X ∧o ∈ L},

O(X) = {o | (s, p, o) ∈ IR(G,X) ∧s ∈ X ∧o ∈ U},

T(X) = {(p, o) | (s, p, o) ∈ IR(G,X) ∧s ∈ X}.



Roundshape

Apple1

Appletitle

Apple1

Redcolor

Apple1


€

P(X) = {shape,title,color,category},

D(X) = {Round,Apple,Red,Eatable},

O(X) = {}

T(X) = {(shape,round),(title,Apple),(color,Red), (category,Eatable)}.



[P(hi), D(hi), O(hi), T(hi)]


Step 1: Cluster the source

Source

Cars

FruitsCompanies


Step 2: Blocking Key Selection

Key Selection

Source instances



Key Selection

key

keykey

Source instances



Key Selection

key

keykey

Source instances

e.g.Title


Step 3: Pseudo-Homonyms Builder

Title=apple

Title=orangeTitle=pineapple

Pseudo-Homonyms

Builder

Target


Step 3: Pseudo-Homonyms Builder

Pseudo-Homonyms

Builder

Target

Source instances

Target

Pseudo-homonyms sets

Everything called Apple


Step 4: Class-based disambiguation

Target


Disambiguation

Class-based Disambiguat

or



Target


Disambiguation


or Source instances

Target




h11

h12

h13

h14

h21

h22

h31

h32

h33

H1 H2 H3

pseudo-homonym sets

inst

an

ces



h11

h12

h13

h14

h21

h22

h31

h32

h33

H1 H2 H3

pseudo-homonym sets

inst

an

ces

[P(hi11), D(hi

11), O(hi11),

T(hi11)]



Roundshape

Apple1

Appletitle

Apple1

Redcolor

Apple1


€

P(X) = {shape,title,color,category},

D(X) = {Round,Apple,Red,Eatable},

O(X) = {}

T(X) = {(shape,round),(title,Apple),(color,Red), (category,Eatable)}.



h11

h12

h13

h14

h21

h22

h31

h32

h33

H1 H2 H3

inst

an

ces

pseudo-homonym sets

0.98

0.32

0.32

0.76

0.95

0.53

0.94

0.91

0.87

H1 H2 H3



h11

h12

h13

h14

h21

h22

h31

h32

h33

H1 H2 H3€

SetSim(A,B) = | A ∩ B | -| A - B | + | B - A |

2 | A∪B |

⎛

⎝ ⎜

⎞

⎠ ⎟



h11

h12

h13

h14

h21

h22

h31

h32

h33

H1 H2 H3

€

SetSim(P(h11),P(H2)) + SetSim(P(h11),P(H3))

[P(hi11), D(hi

11), O(hi11),

T(hi11)]



€

RDS(A,B) = SetSim(P(A), P(B)) +SetSim(D(A),D(B)) +

SetSim(O(A),O(B)) + SetSim(T(A), T(B))



h11

h12

h13

h14

h21

h22

h31

h32

h33

H1 H2 H3

0.98

h12

h13

h14

h21

h22

h31

h32

h33

H1 H2 H3



0.98

0.32

0.32

0.76

0.95

0.53

0.94

0.91

0.87

H1 H2 H3



€

URDS(t,PH(S)−) =RDS({t},PH(s'))

|PH(S') |PH (s' )∈PH (S )−

∑



0.98 0.95 0.94

H1 H2 H3

TOP-K or Threshold



Disambiguation


or Source instances

Target



Experiment

• Ontology Alignment Evaluation Initiative (OAEI 2010)

• Collections: the life science (LS) collection (DBPedia, Sider, Drugbank, LinkedCT, Dailymed, TCM, and Diseasome) and the Person-Restaurant (PR)

• 20 gigabytes of data, millions of triples.

• We compared SERIMI to ObjectCoref and RiMON

• Precision, Recall and F1


Results



0.98 0.95 0.94

H1 H2 H3

TOP-K or Threshold


Results for Top-K

Sider-Daily. Sider-Drug. Drug.-Sider P11-P120.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Top-1

Top-2

Top-5

Top-10

Dataset Pair

F1


Results for δ threshold

Sider-Daily. Sider-Drug. Drug.-Sider P11-P120.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

δ >= δmδ = 1.0δ >= 0.95δ >= 0.90δ >= 0.85

Dataset Pair

F1


Conclusion

• SERIMI is complementary approach to direct-match based instance matching tools.

• SERIMI is recommended for heterogeneous data where

there is no overlap between schemas.

• It is recommended for multi-class disambiguation.


THANK YOU!

• Samur Araujo [email protected]

SERIMI: Class-based Disambiguation for EffectiveInstance Matching over Heterogeneous Web Data

mailto:[email protected]



SERIMI: Class-based Disambiguation for Effective Instance Matching over Heterogeneous Web Data

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