A method for filtering large conceptual schemas

Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions

A Method for FilteringLarge Conceptual Schemas

Antonio Villegas and Antoni Oliveavillegas, [email protected]

Services and Information Systems Engineering DepartmentUniversitat Politecnica de Catalunya

A. Villegas and A. Olive ER 2010 November 3, 2010 1 / 28


Outline

1 Introduction

2 Filtering Method

3 Filtered Conceptual Schema

4 Experimentation

5 Conclusions



Conceptual Schemas



Conceptual Schemas



Conceptual Schemas



Conceptual Schemas



Conceptual Schemas



Conceptual Schemas



Conceptual Schemas



Conceptual Schemas

osCommerce


84 Entity types, 209 Attributes, 183 Relationship types,28 IsA Relationships, 204 general constraints and derivation rules


Conceptual Schemas

Health Level 7


2,695 Entity types, 160 Attributes,228 Relationship types, 2,934 IsA Relationships


Conceptual Schemas

ResearchCyc


26,725 Entity types, 1,060 Attributes,5,514 Relationship types, 43,323 IsA Relationships


Conceptual Schemas

Usability Problem

The largeness of conceptual schemas makes it difficult for auser to get the knowledge of interest to her

This task needs computer support



Conceptual Schemas

Usability Problem





Conceptual Schemas

Usability Problem





Conceptual Schemas

Usability Problem





General Overview

Filtering Method Overview

The main idea is to extract a reduced and self-contained viewfrom the large schema, that is, a filtered conceptual schemawith the knowledge of interest to the user.



Concrete Overview

Filtering Method Overview

- A user focuses on one o more entity types of interest

- The method filters the large schema obtaining a subset ofrelevant elements to the user, taking into account:

the importance of each entity type in the whole schema,and its closeness to the entity types in the user focus.



Representing the User Request

The information need of a user looking for (a subset of) theknowledge represented in a large schema includes:

Focus Set What the user is interested in about the schema

Rejection Set What the user is not interested in about theschema

Filter Size How much knowledge the user wants to obtainfrom the schema at a given moment


Knowledge Request








Knowledge Request








Knowledge Request








Knowledge Request



Example of Knowledge Request

The Knowledge Request is based on the concept of Entity Type.

The user wants to know information about taxes in theosCommerce schema:

Focus Set FS = TaxRate, TaxClassRejection Set RS = LanguageFilter Size K = 10


Knowledge Request








Knowledge Request








Knowledge Request








Knowledge Request


Filtering Measures

Closeness of Entity Types (Ω(e,FS))

Our method uses the closeness Ω(e,FS) between each candidate entity type ein the schema with respect to the entity types of the focus set FS.

We say that e is a candidate entity type if e /∈ FS ∪RS.

Intuitively, the closeness of candidate e should be directly related to theinverse of the distance of e to the focus set FS,

Ω(e,FS) =|FS|X

e′∈FSd(e, e′)


Closeness


Filtering Measures

Closeness of Entity Types (Ω(e,FS)) Example Ω(C ,FS = A,B)

Ω(e,FS) =|FS|X

e′∈FS

d(e, e′)

Ω(C,FS) =|A, B|

d(C, A) + d(C, B)

Ω(C,FS) =2

3 + 2= 0.4


Closeness


Filtering Measures

Closeness of Entity Types (Ω(e,FS)) Example Ω(C ,FS = A,B)

Ω(e,FS) =|FS|X

e′∈FS

d(e, e′)

Ω(C,FS) =|A, B|

d(C, A) + d(C, B)

Ω(C,FS) =2

3 + 2= 0.4


Closeness


Filtering Measures

Importance of Entity Types (Ψ)

The importance Ψ(e) of an entity type e ∈ E of a conceptual schema is a realnumber that measures the relevance of e in the schema. There are severalmethods:

Occurrence counting Ψ(e) depends on the number of characteristics theschema has about e

Link analysis Ψ(e) depends on the importance of those entity typesconnected to e

Instance-dependent Ψ(e) depends on the instances of e

Our filtering method can be used in connection with any of the existingimportance-computing methods.


Importance


Filtering Measures








Importance


Filtering Measures








Importance


Filtering Measures








Importance


Filtering Measures








Importance


Filtering Measures


osCommerce Ψ(e)

Product 1

Order 0.63

Language 0.53

Customer 0.39

Store 0.35

OrderLine 0.34

Zone 0.34

TaxZone 0.29

Special 0.28

Country 0.26

HL7 Ψ(e)

Act 1

Role 0.68

ActRelationship 0.53

Participation 0.49

Entity 0.46

Observation 0.35

InfrastructureRoot 0.24

Organization 0.23

RoleLink 0.21

FinancialTransaction 0.2

ResearchCyc Ψ(e)

Individual 1

LexicalWord 0.13

PartiallyTangible 0.12

Thing 0.07

SpatialThing 0.06

Agent 0.05

Organization 0.05

SomethingExisting 0.05

TemporalThing 0.04

HumanActivity 0.04


Importance


Filtering Measures

Importance of Entity Types (Ψ(e)) Closeness of Entity Types (Ω(e,FS))

Interest of Entity Types (Φ(e,FS))

Φ(e,FS) = α×Ψ(e) + (1− α)× Ω(e,FS)


Interest


Filtering Measures



Φ(e,FS) = α×Ψ(e) + (1− α)× Ω(e,FS)


Interest


Filtering Measures



Φ(e,FS) = α×Ψ(e) + (1− α)× Ω(e,FS)


Interest


Filtering Measures


Φ(e,FS) = α×Ψ(e) + (1− α)× Ω(e,FS)

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

Importance Ψ

Clo

sene

ssΩ

r

r′


Interest


Filtering Measures


Φ(e,FS) = α×Ψ(e) + (1− α)× Ω(e,FS)

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1P1

P2

P3P4

P5

P6P7

P8

Importance Ψ

Clo

sene

ssΩ

r′

dist

(P6, r

′ )di

st(P

2, r

′ )


Interest


Filtered Conceptual Schema

Filtered Conceptual Schema (FCS)

Main Task

Construct a filtered conceptual schema, FCS , from the K moreinteresting entity types and the knowledge of the original schemaCS.

FCS Components

- EF is a set of entity types filtered from E of CS- RF is a set of relationship types filtered from R of CS- IF is a set of IsA relationships filtered from I of CS.

- CF is a set of integrity constraints filtered from C of CS.

- DF is a set of derivation rules filtered from D of CS




Filtered Entity Types (EF)FCS = 〈EF , RF , IF , CF , DF 〉


FS: focus set

Etop : most interesting entity types

Eaux : auxiliary entity types

Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |

EF





FS: focus set



Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |

FS

EF





FS: focus set



Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |Etop

FS

EF





FS: focus set



Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |EauxEtop

FS

EF





FS: focus set




FS

EF





FS: focus set




FS

EF



Filtered Relationship Types (RF)FCS = 〈EF , RF , IF , CF , DF 〉

The relationship types in RF are those r of the original schemawhose participant entity types

belong to EFor are ascendants of entity types of EF(in which case a projection of r is required)




















← included in Eaux



Filtered IsA Relationships (IF)FCS = 〈EF , RF , IF , CF , DF 〉

If e and e ′ are entity types in EF and there is a direct or indirectIsA relationship between them in the original schema, then suchIsA relationship must also exist in IF of FCS




Filtered IsA Relationships (IF)FCS = 〈EF , RF , IF , CF , DF 〉

If e and e ′ are entity types in EF and there is a direct or indirectIsA relationship between them in the original schema, then suchIsA relationship must also exist in IF of FCS




Filtered Integrity Constraints (CF ) and Derivation Rules (DF )FCS = 〈EF , RF , IF , CF , DF 〉

The integrity constraints and derivation rules included in CF andDF of FCS are those whose expressions only involve entity typesfrom EF .

Both the integrity constraint ic1 and the derivation rule dr1 areonly included in CF and DF of FCS if A, B ∈ EF .



Examples

osCommerce




Examples

osCommerce


FS = TaxRate, TaxClassand K = 10



Examples

osCommerce


FS = TaxRate, TaxClassand K = 10



Examples

Health Level 7




Examples

Health Level 7


FS = Patient, ActAppointmentand K = 10



Examples

Health Level 7


FS = Patient, ActAppointmentand K = 10



Examples

ResearchCyc




Examples

ResearchCyc


FS = Cancerand K = 18



Examples

ResearchCyc


FS = Cancerand K = 18



Conclusions

The problem of filtering a fragment of the knowledgecontained in a large conceptual schema appears in manyinformation systems development activities.

People needs to operate for some purpose with a fragment ofthe knowledge contained in those large schemas.

We have proposed a filtering method based on theimportance and closeness measures. A user indicates afocus set of entity types, and the method determines asubset of the elements of the large schema that is likely tobe of interest to the user.

We have experimented with three large schemas. In bothcases, our prototype obtains the filtered schema in less thana second.



Conclusions







Conclusions







Conclusions







Further Work

Take into account the importance of relationship types.

Fine-grained filtering of integrity constraints andderivation rules.

Conduct experiments to precisely determine the usefulness ofour method to real users.



Further Work






Further Work






A Method for FilteringLarge Conceptual Schemas

Antonio Villegas and Antoni Oliveavillegas, [email protected]

Services and Information Systems Engineering DepartmentUniversitat Politecnica de Catalunya


Documents

A method for filtering large conceptual schemas