This document describes a method for filtering large conceptual schemas to extract a reduced subset of relevant information for a user. The filtering method considers both the importance of entity types within the full schema and their closeness to the user-specified focus set. It represents the user's information need as a focus set, rejection set, and desired filter size. The interest score for each candidate entity type combines its importance and closeness, allowing extraction of a filtered conceptual schema tailored to the user's knowledge request.

1. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
A Method for Filtering
Large Conceptual Schemas
Antonio Villegas and Antoni Oliv´e
{avillegas, olive}@essi.upc.edu
Services and Information Systems Engineering Department
Universitat Polit`ecnica de Catalunya
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 1 / 28

2. 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. Oliv´e ER 2010 November 3, 2010 2 / 28

3. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 3 / 28

4. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 3 / 28

5. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 3 / 28

6. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 3 / 28

7. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 3 / 28

8. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 3 / 28

9. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 3 / 28

10. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
osCommerce
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 4 / 28
84 Entity types, 209 Attributes, 183 Relationship types,
28 IsA Relationships, 204 general constraints and derivation rules

11. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
Health Level 7
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 5 / 28
2,695 Entity types, 160 Attributes,
228 Relationship types, 2,934 IsA Relationships

12. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
ResearchCyc
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 6 / 28
26,725 Entity types, 1,060 Attributes,
5,514 Relationship types, 43,323 IsA Relationships

13. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
Usability Problem
The largeness of conceptual schemas makes it difficult for a
user to get the knowledge of interest to her
This task needs computer support
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 7 / 28

14. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
Usability Problem
The largeness of conceptual schemas makes it difficult for a
user to get the knowledge of interest to her
This task needs computer support
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 7 / 28

15. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
Usability Problem
The largeness of conceptual schemas makes it difficult for a
user to get the knowledge of interest to her
This task needs computer support
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 7 / 28

16. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conceptual Schemas
Usability Problem
The largeness of conceptual schemas makes it difficult for a
user to get the knowledge of interest to her
This task needs computer support
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 7 / 28

17. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
General Overview
Filtering Method Overview
The main idea is to extract a reduced and self-contained view
from the large schema, that is, a filtered conceptual schema
with the knowledge of interest to the user.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 8 / 28

18. 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 of
relevant 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. Oliv´e ER 2010 November 3, 2010 9 / 28

19. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Representing the User Request
The information need of a user looking for (a subset of) the
knowledge 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 the
schema
Filter Size How much knowledge the user wants to obtain
from the schema at a given moment
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 10 / 28
Knowledge Request

20. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Representing the User Request
The information need of a user looking for (a subset of) the
knowledge 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 the
schema
Filter Size How much knowledge the user wants to obtain
from the schema at a given moment
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 10 / 28
Knowledge Request

21. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Representing the User Request
The information need of a user looking for (a subset of) the
knowledge 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 the
schema
Filter Size How much knowledge the user wants to obtain
from the schema at a given moment
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 10 / 28
Knowledge Request

22. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Representing the User Request
The information need of a user looking for (a subset of) the
knowledge 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 the
schema
Filter Size How much knowledge the user wants to obtain
from the schema at a given moment
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 10 / 28
Knowledge Request

23. 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 the
osCommerce schema:
Focus Set FS = {TaxRate, TaxClass}
Rejection Set RS = {Language}
Filter Size K = 10
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 11 / 28
Knowledge Request

24. 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 the
osCommerce schema:
Focus Set FS = {TaxRate, TaxClass}
Rejection Set RS = {Language}
Filter Size K = 10
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 11 / 28
Knowledge Request

25. 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 the
osCommerce schema:
Focus Set FS = {TaxRate, TaxClass}
Rejection Set RS = {Language}
Filter Size K = 10
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 11 / 28
Knowledge Request

26. 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 the
osCommerce schema:
Focus Set FS = {TaxRate, TaxClass}
Rejection Set RS = {Language}
Filter Size K = 10
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 11 / 28
Knowledge Request

27. 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 e
in 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 the
inverse of the distance of e to the focus set FS,
Ω(e, FS) =
|FS|
X
e ∈FS
d(e, e )
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 12 / 28
Closeness

28. 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. Oliv´e ER 2010 November 3, 2010 13 / 28
Closeness

29. 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. Oliv´e ER 2010 November 3, 2010 13 / 28
Closeness

30. 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 real
number that measures the relevance of e in the schema. There are several
methods:
Occurrence counting Ψ(e) depends on the number of characteristics the
schema has about e
Link analysis Ψ(e) depends on the importance of those entity types
connected to e
Instance-dependent Ψ(e) depends on the instances of e
Our filtering method can be used in connection with any of the existing
importance-computing methods.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 14 / 28
Importance

31. 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 real
number that measures the relevance of e in the schema. There are several
methods:
Occurrence counting Ψ(e) depends on the number of characteristics the
schema has about e
Link analysis Ψ(e) depends on the importance of those entity types
connected to e
Instance-dependent Ψ(e) depends on the instances of e
Our filtering method can be used in connection with any of the existing
importance-computing methods.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 14 / 28
Importance

32. 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 real
number that measures the relevance of e in the schema. There are several
methods:
Occurrence counting Ψ(e) depends on the number of characteristics the
schema has about e
Link analysis Ψ(e) depends on the importance of those entity types
connected to e
Instance-dependent Ψ(e) depends on the instances of e
Our filtering method can be used in connection with any of the existing
importance-computing methods.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 14 / 28
Importance

33. 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 real
number that measures the relevance of e in the schema. There are several
methods:
Occurrence counting Ψ(e) depends on the number of characteristics the
schema has about e
Link analysis Ψ(e) depends on the importance of those entity types
connected to e
Instance-dependent Ψ(e) depends on the instances of e
Our filtering method can be used in connection with any of the existing
importance-computing methods.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 14 / 28
Importance

34. 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 real
number that measures the relevance of e in the schema. There are several
methods:
Occurrence counting Ψ(e) depends on the number of characteristics the
schema has about e
Link analysis Ψ(e) depends on the importance of those entity types
connected to e
Instance-dependent Ψ(e) depends on the instances of e
Our filtering method can be used in connection with any of the existing
importance-computing methods.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 14 / 28
Importance

35. 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. Oliv´e ER 2010 November 3, 2010 15 / 28
Importance

36. 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. Oliv´e ER 2010 November 3, 2010 16 / 28
Interest

37. 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. Oliv´e ER 2010 November 3, 2010 16 / 28
Interest

38. 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. Oliv´e ER 2010 November 3, 2010 16 / 28
Interest

39. 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 1
0
0.2
0.4
0.6
0.8
1
Importance Ψ
ClosenessΩ
r
r
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 17 / 28
Interest

40. 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 1
0
0.2
0.4
0.6
0.8
1 P1
P2
P3P4
P5
P6P7
P8
Importance Ψ
ClosenessΩ
r
dist(P6,r)
dist(P2,r)
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 17 / 28
Interest

41. 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 more
interesting entity types and the knowledge of the original schema
CS.
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. Oliv´e ER 2010 November 3, 2010 18 / 28

42. 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. Oliv´e 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

43. 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. Oliv´e 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

44. 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. Oliv´e 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

45. 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. Oliv´e 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

46. 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. Oliv´e 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

47. 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. Oliv´e 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

48. 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 schema
whose participant entity types
belong to EF
or are ascendants of entity types of EF
(in which case a projection of r is required)
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 20 / 28

49. 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 schema
whose participant entity types
belong to EF
or are ascendants of entity types of EF
(in which case a projection of r is required)
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 20 / 28

50. 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 schema
whose participant entity types
belong to EF
or are ascendants of entity types of EF
(in which case a projection of r is required)
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 20 / 28

51. 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 schema
whose participant entity types
belong to EF
or are ascendants of entity types of EF
(in which case a projection of r is required)
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 20 / 28
← included in Eaux

52. 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 indirect
IsA relationship between them in the original schema, then such
IsA relationship must also exist in IF of FCS
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 21 / 28

53. 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 indirect
IsA relationship between them in the original schema, then such
IsA relationship must also exist in IF of FCS
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 21 / 28

54. 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 and
DF of FCS are those whose expressions only involve entity types
from EF .
Both the integrity constraint ic1 and the derivation rule dr1 are
only included in CF and DF of FCS if A, B ∈ EF .
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 22 / 28

55. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
osCommerce
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 23 / 28
84 Entity types, 209 Attributes, 183 Relationship types,
28 IsA Relationships, 204 general constraints and derivation rules

56. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
osCommerce
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 23 / 28
FS = {TaxRate, TaxClass}
and K = 10
84 Entity types, 209 Attributes, 183 Relationship types,
28 IsA Relationships, 204 general constraints and derivation rules

57. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
osCommerce
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 23 / 28
FS = {TaxRate, TaxClass}
and K = 10
84 Entity types, 209 Attributes, 183 Relationship types,
28 IsA Relationships, 204 general constraints and derivation rules

58. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
Health Level 7
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 24 / 28
2,695 Entity types, 160 Attributes,
228 Relationship types, 2,934 IsA Relationships

59. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
Health Level 7
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 24 / 28
FS = {Patient, ActAppointment}
and K = 10
2,695 Entity types, 160 Attributes,
228 Relationship types, 2,934 IsA Relationships

60. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
Health Level 7
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 24 / 28
FS = {Patient, ActAppointment}
and K = 10
2,695 Entity types, 160 Attributes,
228 Relationship types, 2,934 IsA Relationships

61. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
ResearchCyc
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 25 / 28
26,725 Entity types, 1,060 Attributes,
5,514 Relationship types, 43,323 IsA Relationships

62. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
ResearchCyc
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 25 / 28
FS = {Cancer}
and K = 18
26,725 Entity types, 1,060 Attributes,
5,514 Relationship types, 43,323 IsA Relationships

63. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Examples
ResearchCyc
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 25 / 28
FS = {Cancer}
and K = 18
26,725 Entity types, 1,060 Attributes,
5,514 Relationship types, 43,323 IsA Relationships

64. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conclusions
The problem of filtering a fragment of the knowledge
contained in a large conceptual schema appears in many
information systems development activities.
People needs to operate for some purpose with a fragment of
the knowledge contained in those large schemas.
We have proposed a filtering method based on the
importance and closeness measures. A user indicates a
focus set of entity types, and the method determines a
subset of the elements of the large schema that is likely to
be of interest to the user.
We have experimented with three large schemas. In both
cases, our prototype obtains the filtered schema in less than
a second.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 26 / 28

65. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conclusions
The problem of filtering a fragment of the knowledge
contained in a large conceptual schema appears in many
information systems development activities.
People needs to operate for some purpose with a fragment of
the knowledge contained in those large schemas.
We have proposed a filtering method based on the
importance and closeness measures. A user indicates a
focus set of entity types, and the method determines a
subset of the elements of the large schema that is likely to
be of interest to the user.
We have experimented with three large schemas. In both
cases, our prototype obtains the filtered schema in less than
a second.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 26 / 28

66. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conclusions
The problem of filtering a fragment of the knowledge
contained in a large conceptual schema appears in many
information systems development activities.
People needs to operate for some purpose with a fragment of
the knowledge contained in those large schemas.
We have proposed a filtering method based on the
importance and closeness measures. A user indicates a
focus set of entity types, and the method determines a
subset of the elements of the large schema that is likely to
be of interest to the user.
We have experimented with three large schemas. In both
cases, our prototype obtains the filtered schema in less than
a second.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 26 / 28

67. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Conclusions
The problem of filtering a fragment of the knowledge
contained in a large conceptual schema appears in many
information systems development activities.
People needs to operate for some purpose with a fragment of
the knowledge contained in those large schemas.
We have proposed a filtering method based on the
importance and closeness measures. A user indicates a
focus set of entity types, and the method determines a
subset of the elements of the large schema that is likely to
be of interest to the user.
We have experimented with three large schemas. In both
cases, our prototype obtains the filtered schema in less than
a second.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 26 / 28

68. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Further Work
Take into account the importance of relationship types.
Fine-grained filtering of integrity constraints and
derivation rules.
Conduct experiments to precisely determine the usefulness of
our method to real users.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 27 / 28

69. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Further Work
Take into account the importance of relationship types.
Fine-grained filtering of integrity constraints and
derivation rules.
Conduct experiments to precisely determine the usefulness of
our method to real users.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 27 / 28

70. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
Further Work
Take into account the importance of relationship types.
Fine-grained filtering of integrity constraints and
derivation rules.
Conduct experiments to precisely determine the usefulness of
our method to real users.
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 27 / 28

71. Introduction Filtering Method Filtered Conceptual Schema Experimentation Conclusions
A Method for Filtering
Large Conceptual Schemas
Antonio Villegas and Antoni Oliv´e
{avillegas, olive}@essi.upc.edu
Services and Information Systems Engineering Department
Universitat Polit`ecnica de Catalunya
A. Villegas and A. Oliv´e ER 2010 November 3, 2010 28 / 28