A Flexible Framework for Understanding the Dynamics of ... · Y. Roussakis1, I. Chrysakis1, K. Stefanidis1, G. Flouris1, and Y. Stavrakas2. 1: Institute of Computer Science, FORTH,

Y. Roussakis1, I. Chrysakis1, K. Stefanidis1, G. Flouris1, andY. Stavrakas2

1: Institute of Computer Science, FORTH, Heraklion, Greece2: Institute for the Management of Information Systems, ATHENA, Athens, Greece

A Flexible Framework for Understanding the Dynamics

of Evolving RDF Datasets

ISWC 2015, Bethlehem PA

Introduction

• LOD datasets are constantly evolving– Inclusion of new experimental evidence– Correction of erroneous conceptualizations– Other observations

• Detection and analysis of the differences (deltas) between datasets is crucial – Synchronization of autonomously developed versions– Visualization of the evolution history of a dataset – Synchronization of interconnected LOD datasets

2


Introduction

3


Introduction

3

Questions to address:1. Which changes?2. How to detect?

3. How to represent?


Contributions

• A framework for detecting and analyzing changes and the evolution history of LOD datasets

• Key Features✔Change Definition

• Different change types w.r.t. the domain of expertise✔Change Detection

• Efficient change detection algorithm✔Change Representation

• Based on ontologies• Allows queries spanning multiple versions and consider both

changes and data

5


D1 D2

name

Real_Madrid

SoccerClub

Real_Madrid_CF

Motivation

6

name

Real_Madrid

SoccerClub

Real_Madrid_CF


D1 D2

name

Real_Madrid

SoccerClub

Real_Madrid_CF

Mikel_Lasa

team

type

Athlete

name

“Mikel Lasa”

Motivation

6

name

Real_Madrid

SoccerClub

Real_Madrid_CF


D1 D2

Low-level changes

name

Real_Madrid

SoccerClub

Real_Madrid_CF

Mikel_Lasa

team

type

Athlete

name

“Mikel Lasa”

(Mikel_Lasa, team, Real_Madrid_CF)

(Mikel_Lasa, name, Mikel Lasa)

( (Mikel_Lasa, rdf:type, Athlete)

Motivation

6

name

Real_Madrid

SoccerClub

Real_Madrid_CF


D1 D2

Low-level changes Simple changes

name

Real_Madrid

SoccerClub

Real_Madrid_CF

Mikel_Lasa

team

type

Athlete

name

“Mikel Lasa”

Add_Property_Instance(Mikel_Lasa, team, Real_Madrid_CF)

Add_Property_Instance (Mikel_Lasa, name, “Mikel Lasa”)

Add_Type_To_Individual(Mikel_Lasa, Athlete)

(Mikel_Lasa, team, Real_Madrid_CF)



Motivation

6

name

Real_Madrid

SoccerClub

Real_Madrid_CF


D1 D2

Low-level changes Simple changes Complex changes

name

Real_Madrid

SoccerClub

Real_Madrid_CF

Mikel_Lasa

team

type

Athlete

name

“Mikel Lasa”

Add_Property_Instance(Mikel_Lasa, team, Real_Madrid_CF)

Add_Property_Instance (Mikel_Lasa, name, “Mikel Lasa”)

Add_Type_To_Individual(Mikel_Lasa, Athlete)

Add_Athlete(“Mikel Lasa”,Real_Madrid_CF) (Mikel_Lasa, team, Real_Madrid_CF)



Motivation

6

name

Real_Madrid

SoccerClub

Real_Madrid_CF


Simple vs Complex Changes

11

Simple Changes Complex ChangesTerminology Fixed, domain-independent

(e.g., Add_Property_Instance)Custom, domain-dependent (e.g., Add_Athlete)

Changes Set Fixed, pre-defined Variable, user-definedComposition Low-level changes Simple changes

Usability Fine-grained types of evolution Coarse-grained types of evolutionChanges

PartitioningPerfect, i.e., complete and unambiguous

Cannot capture all the evolution aspects

SC1

SC2

SC1

SC2

SC1

SC2


Change Detection - Approach

• Detection based on plain SPARQL queries – one per change

• Simple changes – Prebuilt-queries

• Complex changes – Queries constructed dynamically

upon change definition

• Query results create the change-set

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ADD_SUPERCLASSSELECT ?sub,?sup WHERE {GRAPH <v2>{?sub rdfs:subClassOf ?sup } FILTER NOT EXISTS {GRAPH <v1> { ?sub rdfs:subClassOf ?sup } }}

ADD_ATHLETESELECT ?athlete WHERE {GRAPH <changes>{?sc a co:Add_Type_To_Individual.?sc co:atti_p1 ?athlete.?sc co:atti_p1 dbpedia:athlete.} }


Change Representation - Motivation

13

What can I do with all these changes ???


• Interesting queriesReturn all the left backs,born before 1980, which were transferred to Athletic Bilbao between versions V1 and V2, and used to play for Real Madrid CF in any version

• Access to both the changes and the data is required, across multiple versions– Changes are first-class citizens

Change Representation - Motivation

14


Change Representation - Approach

15

Change

Data

Schema

Simple_ChangeComplex_Change

consumes



15

Change

Data

Schema


Add_Property_Instance

Add_Type_To_Individual

consumes



15

Change

Data

Schema




SELECT …

sparql_info

Add_Player

consumes



15

Change

Data

Schema




SELECT …

sparql_info

Add_Player

consumes

D/changes/App1/schema



15

Change

Data

Schema




SELECT …

sparql_info

Add_Player

consumes

AddPropIn1




15

Change

Data

Schema


consumes



SELECT …

sparql_info

Add_Player

consumes

AddPropIn1


AddPlayer1



15

Change

Data

Schema

Simple_Change

D/changes/v1-v2

Complex_Change

consumes



SELECT …

sparql_info

Add_Player

consumes

AddPropIn1


AddPlayer1



15

Change

Data

Schema

Simple_Change

D/changes/v1-v2

Complex_Change

consumes



SELECT …

sparql_info

Add_Player

consumes

AddPropIn1team

…

Real_Madrid_CF

“Mikel Lasa”

…


AddPlayer1



15

Data

Change

Data

Schema

Simple_Change

D/changes/v1-v2

Complex_Change

consumes



SELECT …

sparql_info

Add_Player

consumes

AddPropIn1team

…

Real_Madrid_CF

“Mikel Lasa”

…


AddPlayer1


Experimental Evaluation

• Purpose1. Identify the number and type of simple and complex changes

that usually occur in real-world settings2. Study the performance and quantify the effect of different

parameters of our change detection process

• Setting– Open source version of Virtuoso Universal Server hosted on a

machine which uses a 12-core Intel Xeon E5-2630 at 2.30GHz and 64GB of RAM dedicated for Virtuoso

– For the multi-threaded part of the queries execution and files ingestion, the experiments were conducted using 8 of the cores in parallel

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Datasets

• We used 3 real RDF datasets of different sizes: – A subset of the English Dbpedia 6 (consisting of article

categories, instance types, labels and mapping-based properties) - http://dbpedia.org

– The Foundational Model of Anatomy ontology (FMA) -http://sig.biostr.washington.edu/projects/fm/AboutFM.html

– The Experimental Factor Ontology (EFO) -http://www.ebi.ac.uk/efo

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http://dbpedia.org/

http://sig.biostr.washington.edu/projects/fm/AboutFM.html

http://www.ebi.ac.uk/efo


Simple Changes

Simple Changes for all datasets

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ADDITIONS DELETIONSADD_TYPE_CLASS DELETE_TYPE_CLASS

ADD_TYPE_PROPERTY DELETE_TYPE_PROPERTY

ADD_SUPERCLASS DELETE_SUPERCLASS

ADD_SUPERPROPERTY DELETE_SUPERPROPERTY

ADD_DOMAIN DELETE_DOMAIN

ADD_RANGE DELETE_RANGE

ADD_PROPERTY_INSTANCE DELETE_PROPERTY_INSTANCE

ADD_COMMENT DELETE_COMMENT

ADD_LABEL DELETE_LABEL

ADD_TYPE_TO_INDIVIDUAL DELETE_TYPE_FROM_INDIVIDUAL


Complex Changes per dataset

Complex Changes

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DBpedia FMA EFOAdd_Subject Add_Concept Add_Definition

Delete_Subject Delete_Concept Delete_Definition

Add_Thing Add_Restriction Add_Synonym

Delete_Thing Delete_Restriction Delete_Synonym

Add_Athlete Add_Synonym Mark_as_Obsolete

Update_Label Update_Comment Update_Comment

Add_Place Update_Domain Update_Domain

Delete_Place Update_Range Update_Range

Add_Person Add_Observation Add_Observation

Delete_Person Delete_Observation Delete_Observation


1. Simple Changes Analysis

28

539

137

333

29

6116

20

1008

210

564

103

9425

28

1108994

1195937

2693767

10135868

1110

126

252

75

98556

25

611

76

107

28

6110

17

156913

230542

4018781

5904209

51

256

5871

2

16

274

66

6297

6

1079243

1040740

1207457

3177835

17

81

9438

7

5864

151384

142863

1350035

1822612

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

EFO6

EFO5

EFO4

EFO3

EFO2

EFO1

FMA2

FMA1

DBp2

DBp1

ADD_COMMENT ADD_DOMAIN ADD_LABELADD_PROPERTY_INSTANCE ADD_RANGE ADD_SUPERCLASSADD_TYPE_CLASS ADD_TYPE_TO_INDIVIDUAL DELETE_COMMENTDELETE_DOMAIN DELETE_LABEL DELETE_PROPERTY_INSTANCEDELETE_RANGE DELETE_SUPERCLASS DELETE_TYPE_CLASSDELETE_TYPE_FROM_INDIVIDUAL


1. Complex Change Analysis

29

144844

142132

482

66

209

1402

20

144791

142105

408562

2693767

2685039

526

144

355

38

8022

7

47170

46684

1087642

874318

1

70

11

4

142105

139348

1207457

1182412

1

15

204

1

6286

2

142132

139374

61

224

6

2

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

EFO6

EFO5

EFO4

EFO3

EFO2

EFO1

FMA2

FMA1

DBp2

DBp1

Add Athlete Add Concept Add Definition Add Observation Add Person

Add Place Add Restriction Add Subject Add Synonym Add Thing

Delete Definition Delete Observation Delete Person Delete Place Delete Restriction

Delete Subject Delete Synonym Delete Thing Mark as Obsolete Update Comment

Update Domain Update Label Update Property Update Range


2. Simple Changes Performance

• Size of the compared versions • Type of the detected simple changes • Number of detected simple changes

# detected simple changes >> size of compared versions30


2. Complex Changes Performance

• Changes ontology size • Number of detected complex changes• Type of detected complex changes• Number of consumed simple changes per detected

complex change more complex queries

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• Low-level change detections (e.g., [1])– Report simple add/delete operations– Not concise or intuitive enough to human users

• High-level change detections (e.g., [2,3])– Provide more human-readable deltas– Predefined list of considered changes w.r.t the context

• [3] the closest to our work– Fixed high-level changes is proposed – no configurable– No cross-version, data-and-changes queries – Significantly less scalable and efficient implementation

Related Work

1. D. Zeginis et al. On computing deltas of rdf/s knowledge bases. ACM Trans. Web, 20112. P. Plessers, et al. Understanding ontology evolution: A change detection approach. Web Semant., 20073. V. Papavasileiou et al. High-level change detection in RDF(S) KBs. ACM Trans. Database Syst., 2013


Summary

• We proposed an approach to cope with the dynamicity of web datasets that:– Manages fixed, application-generic changes with formal properties

(simple changes)– Allows defining customized changes at run-time (complex changes)– Offers a scalable generic detection mechanism (via sparql queries)

• We performed sophisticated analysis on top of the detected changes– We represent changes in an ontology and treat them as first-class

citizens– We consider cross-snapshot queries, and queries involving

evolution history and data

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Q & A

• Demo– D2V: A Tool for Defining, Detecting and Visualizing Changes on

the Data Web. www.ics.forth.gr/isl/D2VSystem

• Acknowledgements

http://www.ics.forth.gr/isl/D2VSystem

Documents

A Flexible Framework for Understanding the Dynamics of ... · Y. Roussakis1, I. Chrysakis1, K. Stefanidis1, G. Flouris1, and Y. Stavrakas2. 1: Institute of Computer Science, FORTH,