Post on 25-May-2015
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
A Method for FilteringLarge Conceptual Schemas
Antonio Villegas and Antoni Oliveavillegas, olive@essi.upc.edu
Services and Information Systems Engineering DepartmentUniversitat Politecnica de Catalunya
A. Villegas and A. Olive ER 2010 November 3, 2010 1 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Outline
1 Introduction
2 Filtering Method
3 Filtered Conceptual Schema
4 Experimentation
5 Conclusions
A. Villegas and A. Olive ER 2010 November 3, 2010 2 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Olive ER 2010 November 3, 2010 3 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Olive ER 2010 November 3, 2010 3 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Olive ER 2010 November 3, 2010 3 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Olive ER 2010 November 3, 2010 3 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Olive ER 2010 November 3, 2010 3 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Olive ER 2010 November 3, 2010 3 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Olive ER 2010 November 3, 2010 3 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
osCommerce
A. Villegas and A. Olive ER 2010 November 3, 2010 4 / 28
84 Entity types, 209 Attributes, 183 Relationship types,28 IsA Relationships, 204 general constraints and derivation rules
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
Health Level 7
A. Villegas and A. Olive ER 2010 November 3, 2010 5 / 28
2,695 Entity types, 160 Attributes,228 Relationship types, 2,934 IsA Relationships
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
ResearchCyc
A. Villegas and A. Olive ER 2010 November 3, 2010 6 / 28
26,725 Entity types, 1,060 Attributes,5,514 Relationship types, 43,323 IsA Relationships
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 7 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 7 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 7 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 7 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 8 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 9 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 10 / 28
Knowledge Request
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 10 / 28
Knowledge Request
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 10 / 28
Knowledge Request
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 10 / 28
Knowledge Request
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Representing the User 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
A. Villegas and A. Olive ER 2010 November 3, 2010 11 / 28
Knowledge Request
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Representing the User 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
A. Villegas and A. Olive ER 2010 November 3, 2010 11 / 28
Knowledge Request
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Representing the User 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
A. Villegas and A. Olive ER 2010 November 3, 2010 11 / 28
Knowledge Request
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Representing the User 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
A. Villegas and A. Olive ER 2010 November 3, 2010 11 / 28
Knowledge Request
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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′)
A. Villegas and A. Olive ER 2010 November 3, 2010 12 / 28
Closeness
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 13 / 28
Closeness
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 13 / 28
Closeness
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 14 / 28
Importance
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 14 / 28
Importance
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 14 / 28
Importance
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 14 / 28
Importance
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 14 / 28
Importance
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtering Measures
Importance of Entity Types (Ψ)
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
A. Villegas and A. Olive ER 2010 November 3, 2010 15 / 28
Importance
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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)
A. Villegas and A. Olive ER 2010 November 3, 2010 16 / 28
Interest
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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)
A. Villegas and A. Olive ER 2010 November 3, 2010 16 / 28
Interest
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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)
A. Villegas and A. Olive ER 2010 November 3, 2010 16 / 28
Interest
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtering Measures
Interest of Entity Types (Φ(e,FS))
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′
A. Villegas and A. Olive ER 2010 November 3, 2010 17 / 28
Interest
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtering Measures
Interest of Entity Types (Φ(e,FS))
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
′ )
A. Villegas and A. Olive ER 2010 November 3, 2010 17 / 28
Interest
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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
A. Villegas and A. Olive ER 2010 November 3, 2010 18 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
Filtered Entity Types (EF)FCS = 〈EF , RF , IF , CF , DF 〉
A. Villegas and A. Olive ER 2010 November 3, 2010 19 / 28
FS: focus set
Etop : most interesting entity types
Eaux : auxiliary entity types
Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |
EF
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
Filtered Entity Types (EF)FCS = 〈EF , RF , IF , CF , DF 〉
A. Villegas and A. Olive ER 2010 November 3, 2010 19 / 28
FS: focus set
Etop : most interesting entity types
Eaux : auxiliary entity types
Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |
FS
EF
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
Filtered Entity Types (EF)FCS = 〈EF , RF , IF , CF , DF 〉
A. Villegas and A. Olive ER 2010 November 3, 2010 19 / 28
FS: focus set
Etop : most interesting entity types
Eaux : auxiliary entity types
Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |Etop
FS
EF
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
Filtered Entity Types (EF)FCS = 〈EF , RF , IF , CF , DF 〉
A. Villegas and A. Olive ER 2010 November 3, 2010 19 / 28
FS: focus set
Etop : most interesting entity types
Eaux : auxiliary entity types
Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |EauxEtop
FS
EF
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
Filtered Entity Types (EF)FCS = 〈EF , RF , IF , CF , DF 〉
A. Villegas and A. Olive ER 2010 November 3, 2010 19 / 28
FS: focus set
Etop : most interesting entity types
Eaux : auxiliary entity types
Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |EauxEtop
FS
EF
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
Filtered Entity Types (EF)FCS = 〈EF , RF , IF , CF , DF 〉
A. Villegas and A. Olive ER 2010 November 3, 2010 19 / 28
FS: focus set
Etop : most interesting entity types
Eaux : auxiliary entity types
Filter Size K = |FS|+ |Etop||EF | = K + |Eaux |EauxEtop
FS
EF
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
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)
A. Villegas and A. Olive ER 2010 November 3, 2010 20 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
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)
A. Villegas and A. Olive ER 2010 November 3, 2010 20 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
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)
A. Villegas and A. Olive ER 2010 November 3, 2010 20 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
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)
A. Villegas and A. Olive ER 2010 November 3, 2010 20 / 28
← included in Eaux
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
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
A. Villegas and A. Olive ER 2010 November 3, 2010 21 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
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
A. Villegas and A. Olive ER 2010 November 3, 2010 21 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Filtered Conceptual Schema
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 .
A. Villegas and A. Olive ER 2010 November 3, 2010 22 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
osCommerce
A. Villegas and A. Olive ER 2010 November 3, 2010 23 / 28
84 Entity types, 209 Attributes, 183 Relationship types,28 IsA Relationships, 204 general constraints and derivation rules
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
osCommerce
A. Villegas and A. Olive ER 2010 November 3, 2010 23 / 28
FS = TaxRate, TaxClassand K = 10
84 Entity types, 209 Attributes, 183 Relationship types,28 IsA Relationships, 204 general constraints and derivation rules
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
osCommerce
A. Villegas and A. Olive ER 2010 November 3, 2010 23 / 28
FS = TaxRate, TaxClassand K = 10
84 Entity types, 209 Attributes, 183 Relationship types,28 IsA Relationships, 204 general constraints and derivation rules
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
Health Level 7
A. Villegas and A. Olive ER 2010 November 3, 2010 24 / 28
2,695 Entity types, 160 Attributes,228 Relationship types, 2,934 IsA Relationships
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
Health Level 7
A. Villegas and A. Olive ER 2010 November 3, 2010 24 / 28
FS = Patient, ActAppointmentand K = 10
2,695 Entity types, 160 Attributes,228 Relationship types, 2,934 IsA Relationships
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
Health Level 7
A. Villegas and A. Olive ER 2010 November 3, 2010 24 / 28
FS = Patient, ActAppointmentand K = 10
2,695 Entity types, 160 Attributes,228 Relationship types, 2,934 IsA Relationships
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
ResearchCyc
A. Villegas and A. Olive ER 2010 November 3, 2010 25 / 28
26,725 Entity types, 1,060 Attributes,5,514 Relationship types, 43,323 IsA Relationships
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
ResearchCyc
A. Villegas and A. Olive ER 2010 November 3, 2010 25 / 28
FS = Cancerand K = 18
26,725 Entity types, 1,060 Attributes,5,514 Relationship types, 43,323 IsA Relationships
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
ResearchCyc
A. Villegas and A. Olive ER 2010 November 3, 2010 25 / 28
FS = Cancerand K = 18
26,725 Entity types, 1,060 Attributes,5,514 Relationship types, 43,323 IsA Relationships
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 26 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 26 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 26 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
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.
A. Villegas and A. Olive ER 2010 November 3, 2010 26 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation 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.
A. Villegas and A. Olive ER 2010 November 3, 2010 27 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation 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.
A. Villegas and A. Olive ER 2010 November 3, 2010 27 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation 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.
A. Villegas and A. Olive ER 2010 November 3, 2010 27 / 28
Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
A Method for FilteringLarge Conceptual Schemas
Antonio Villegas and Antoni Oliveavillegas, olive@essi.upc.edu
Services and Information Systems Engineering DepartmentUniversitat Politecnica de Catalunya
A. Villegas and A. Olive ER 2010 November 3, 2010 28 / 28