W3C Invited Talk 16/09/2009Giorgos Flouris1 High-Level Change Detection in the Semantic Web Institute of Computer Science Foundation for Research and Technology

16/09/2009 Giorgos Flouris 1

W3C Invited Talk

High-Level Change Detectionin the Semantic Web

Institute of Computer Science Foundation for Research and Technology – Hellas

Heraklion, Greece

Giorgos Flouris

[email protected]

Joint work with:Vicky Papavassiliou, Irini Fundulaki,

Dimitris Kotzinos, Vassilis Christophides


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World Wide Web

WWW (and HTML) focus on human readability

Page presentation (fonts, colors, images, …)Human understandingPresentation Semantical contentContent is not formally described (for a machine to understand)

WWW contains documents, not data


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Problems with Current Web

Search and access becomes difficult

Software ignorant of the semantical content of a web pageKeyword searchHigh recall, low precision

Terminological issues

Synonyms (heart disease = cardiac disease)Hyponyms/hypernyms (parliament members are politicians)

Queries on the semantical content cannot be made

Fetch articles that support B. Obama’s foreign policyFetch the home pages of all members of the Greek Parliament


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Semantic Web

The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation(Berners-Lee et al., 2001)

The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries http://www.w3.org/2001/sw/

[Semantic Web] is a collaborative effort led by W3C with participation from a large number of researchers and industrial partnershttp://www.w3.org/2001/sw/


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Semantic Web in Practice

Web of data, rather than documents

HTML for presentationSemantical languages for semantical contentReadable and understandable by humans and machines

Semantic Web languages, protocols, etc

Web page annotation (metadata descriptions etc)Publication of data on the InternetEfficient communication and manipulation of data over the Internet

Different applications

Efficient searchingSharing of data (e-science, e-government, remote learning, …)


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Ontologies

Backbone of the Semantic Web

Ontologies allow the description of data

Annotation and metadata regarding web pagesTerminological relations (synonyms, hyponyms, …)Communication and description of data, ideas, beliefs

An ontology is an explicit specification of a shared conceptualization of a domain(Gruber, 1993)

Precise, logical account of the intended meaning of terms, data structures etc

Common (shared) interpretation of termsFormal vocabulary for information exchange (for humans and

machines)


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Ontologies in Practice

Basic structures:

Classes (or concepts): collections of objects (e.g., Actor, Politician)

Properties (or roles): binary relationships between objects (e.g., started_on, member_of)

Instances (or individuals): objects (e.g., Giorgos, B. Obama)

Relations between them

Subsumption (Parliament_Member subclass of Politician), instantiation (B. Obama instance of Politician), …

The allowed relations and their semantics depend on the language

Different representation languages for ontologies

RDF, RDFS, DAML+OiL, OWL, OWL-DL, OWL-Lite, OWL2, DLs, …Usually triple-based


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Visualization, Triples, Serialization

Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants

Define classes[Period type Class]Define properties[participants type Property][participants domain Onset][participants range Actor]Instantiate/define individuals[G_Birth type Birth][Giorgos type Actor][G_Birth participants Giorgos]Define hierarchies[Event subClass Period]

G_BirthGiorgosparticipants

<rdfs:Class rdf:ID=“Period”> </rdfs:Class> <rdf:Property rdf:ID=“participants”> <rdfs:domain rdf:resource=“Onset”/> <rdfs:range rdf:resource=“Actor”/> </rdf:Property> <G_Birth rdf:about Birth><participants><Giorgos rdf:about Actor/></participants></G_Birth><rdfs:Class rdf:ID=“Event”> <rdfs:subClassOf rdf:resource=“Period”/> </rdfs:Class>

Visualization Triple Representation Serialization (RDF/XML)

instantiation

subsumption


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Ontology Dynamics

Ontologies change constantly

World changes (dynamic models)View on the world changes (new knowledge, measurements, etc)Perspective and usage changes

Example: GO ontology changes daily

Gene Ontology: information about gene products (biology)

Must find a way to cope with changes

Ontology evolution (modify an ontology in response to a change)Ontology versioning (keep track of versions and their relations)…

We deal with a peripheral problem (change detection)


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What is Change?

Ontology

Real World

Ontology EvolutionAlgorithmDelete_Class(…)

Pull_Up_Class(…)Rename_Class(…)

…


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What is Change Detection?

Ontology

Real World

Delete_Class(…)Pull_Up_Class(…)Rename_Class(…)

…

Change Detection Algorithm


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Keeping Track of Changes

Purpose of this work: change detection

A posteriori detect the differences (delta or diff) between versions in a concise, intuitive and correct way

It is important to store the changes between versions

Visualization of differencesEfficient storage and/or communicationEvolution history

Record changes as they happen (manual or automatic)

Error-prone, difficult (often impossible)

V1 V2 V3 V4 V5

C1 C2 C3 C4


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Sample Evolution

Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants

Version 1 (V1) Version 2 (V2)

Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants


instantiation

subsumption

instantiation

subsumption


Evolution


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Analyzing the Evolution (Using Triples)

Triples in V1 (partial list)

[Event type Class]

[Period type Class]

[Event subclass Period]

[participants type Property]

[participants domain Onset]

[participants range Actor]

[Giorgos type Actor]

[Existing type Class]

[Stuff subclass Existing]

[started_on domain Existing]

[Onset subclass Event]

[Birth subclass Onset]

…

Triples in V2 (partial list)

[Event type Class]

[participants type Property]

[Event domain participants]

[participants range Actor]

[Giorgos type Actor]

[Persistent type Class]

[Stuff subclass Persistent]

[started_on domain Persistent]

[Onset subclass Event]

[Birth subclass Event]

…


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Low-Level Delta

Triples in V2 but not in V1

(added triples)

[Event domain participants]

[Persistent type Class]

[Stuff subclass Persistent]

[started_on domain Persistent]

[Birth subclass Event]

Triples in V1 but not in V2

(deleted triples)

[Period type Class]

[Event subclass Period]

[participants domain Onset]

[Existing type Class]

[Stuff subclass Existing]

[started_on domain Existing]

[Birth subclass Onset]

Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants


Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants


instantiation

subsumption

instantiation

subsumption


Evolution

Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants


Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants


instantiation

subsumption

instantiation

subsumption

instantiation

subsumption

instantiation

subsumption


Evolution

Low-Level DeltaAdd([Event domain participants])

Add([Persistent type Class])…

Del([Period type Class])…


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Analyzing the Evolution (Visually)

Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants


Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants


instantiation

subsumption


Evolution

High-Level DeltaGeneralize_Domain(participants, Onset, Event)

Pull_Up_Class(Birth, Onset, Event)Delete_Class(Period, Ø, {Event}, Ø, Ø, Ø, Ø)

Rename_Class(Existing, Persistent)


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Comparing the Deltas

Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants


Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants


instantiation

subsumption


Evolution

Del([participants domain Onset])Add([participants domain Event])

Generalize_Domain(participants, Onset, Event)

Del([Birth subclass Onset])Add([Birth subclass Event])

Pull_Up_Class(Birth, Onset, Event)

Low-level delta High-level delta

Del([Period type Class])Del([Event subclass Period])

Delete_Class (Period,Ø,{Event},Ø,Ø,Ø,Ø)


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Associations (Partitioning)

Low-Level Changes Associated High-Level Changes

Del([participants domain Onset]) Generalize_Domain(participants, Onset, Event)Add([participants domain Event])

Del([Birth subclass Onset])Pull_Up_Class(Birth, Onset, Event)

Add([Birth subclass Event])

Del([Period type Class]) Delete_Class(Period, Ø, {Event}, Ø, Ø, Ø, Ø)Del([Event subclass Period])

Del([Existing type Class])

Rename_Class(Existing, Persistent)

Del([Stuff subclass Existing])

Del([started_on domain Existing])

Add([Persistent type Class])

Add([Stuff subclass Persistent])

Add([started_on domain Persistent])


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Low-Level Versus High-Level Deltas

Purpose:

A posteriori detect the differences (delta or diff) between versions in a concise, intuitive and correct way

Low-level deltas

Easier to get

High-level deltas

More concise (e.g., Rename_Class)More intuitive (e.g., Pull_Up_Class)Carry additional information (e.g., Generalize_Domain)

Objective: detection of high-level deltas


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Language of Changes and Algorithm

Deltas based on some language of changes

A set of formal definitions that describe the changes that can be understood and detected

Can be high-level or low-levelMust be coupled with a corresponding detection algorithm

Low-level languages easy to define (Add(t), Del(t))

High-level languages more complicated

Several proposals; no standard

Challenges for high-level languages

Must be deterministic (exactly one high-level delta)Must be fine-grained enough to capture subtle changesMust be coarse-grained enough to be concise


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Proposed Language L

The formal definition of a change consists of:

Changes required in the low-level delta (added/deleted triples)

Conditions that should hold in V1 and/or V2

Generalize_Domain(P, X, Y)

Del([P domain X])Add([P domain Y])

P existing property in both V1, V2

X, Y existing classes in both V1, V2

X subclass of Y in both V1, V2

Generalize_Domain(participants, Onset, Event): detectable

Similarly for the other changes in L (about 120 in total)


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Results on L: Granularity

Granularity problem: solved by defining levels of changes

Basic Changes: fine-grained, roughly correspond to low-levelComposite Changes: coarse-grained, group several basic changes

togetherHeuristic Changes: based on heuristics, necessary for Rename,

Merge, Split etc

Problems with determinism

One evolution could correspond to different sets of basic/composite changes

Priorities in detection

Heuristic Composite Basic


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Results on L: Types of Changes

Changes

Low-Level High-Level

Basic Composite Heuristic

AddDel

Delete_SubclassDelete_Domain

Pull_Up_ClassChange_Domain

Rename_ClassSplit_Class


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Results on L: Determinism

Each low-level change is associated with exactly one detectable high-level change

Full partitioning of low-level changes into high-level ones

Each pair of versions (V1, V2) is associated with:

Exactly one low-level deltaExactly one high-level delta

Determinism is necessary

More than one would lead to ambiguities

Less than one would make some inputs (V1, V2) irresolvable


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Results on L: Application

Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants


Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants



Detect C

Apply C

Apply C-1


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Results on L: Deltas Keep Version History

Can reproduce all versions as long as you keep (any) one version and the deltas

Deltas are more concise than the versions themselves

Storage and communication efficiency

V1 V2 V3 V4 V5

C1 C2 C3 C4


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Calculate Low-Level Delta

Detection Algorithm for L (1/2)

Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants

Version 2 (V2)


Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants

Version 2 (V2)


Version 1 (V1)

Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants


Version 1 (V1)

Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants


Triples in V1 (Partial List)[Period type Class]

[Event subclass Period][participants type Property][participants domain Onset][participants range Actor]

[Existing type Class][Stuff subclass Existing]

[started_on domain Existing][Onset subclass Event]

…

Triples in V2 (Partial List)[Event type Class]

[participants type Property][Event domain participants][participants range Actor]

[Giorgos type Actor][Persistent type Class]

[Stuff subclass Persistent][started_on domain Persistent]

[Onset subclass Event][Birth subclass Event]

…

Triples in Delta (step 1: low-level)Del([participants domain Onset])

Del([Birth subclass Onset])Del([Event subclass Period])

Del([Existing type Class])Del([Stuff subclass Existing])

Del([started_on domain Existing])Del([Period type Class])

Add([Birth subclass Event])Add([participants domain Event])

Add([Persistent type Class])Add([Stuff subclass Persistent])

Add([started_on domain Persistent])

Run Matcher(External)

List of Mappings<V1:Existing> is matched with <V2:Persistent>

Compute HeuristicChanges

Heuristic ChangesRename_Class(Existing, Persistent)


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Triples in Delta (step 3: basic and composite)Del([Birth subclass Onset])

Del([Event subclass Period])Del([Period type Class])

Add([Birth subclass Event])Rename_Class(Existing, Persistent)


Detection Algorithm for L (2/2)

Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants

Version 2 (V2)


Persistent

Event

Onset Birth

Stuff

Actor

started_on

participants

Version 2 (V2)


Version 1 (V1)

Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants


Version 1 (V1)

Period

Actor Event

OnsetExisting

Stuff Birth

started_on

participants


Triples in V1 (Partial List)[Period type Class]

[Event subclass Period][participants type Property][participants domain Onset][participants range Actor]

[Existing type Class][Stuff subclass Existing]

[started_on domain Existing][Onset subclass Event]

…

Triples in V2 (Partial List)[Event type Class]

[participants type Property][Event domain participants][participants range Actor]

[Giorgos type Actor][Persistent type Class]

[Stuff subclass Persistent][started_on domain Persistent]

[Onset subclass Event][Birth subclass Event]

…

Triples in Delta (step 2: heuristic)Del([participants domain Onset])

Del([Birth subclass Onset])Del([Event subclass Period])

Del([Period type Class])Add([Birth subclass Event])

Add([participants domain Event])Rename_Class(Existing, Persistent)

Del([participants domain Onset])

FindAssociated

Change


DETECTABLE

Triples in Delta (step 4: result)Delete_Class(Period, Ø, {Event}, Ø, Ø, Ø, Ø)

Pull_Up_Class(Birth, Onset, Event)Rename_Class(Existing, Persistent)


??

?


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Find Associated Change

Del([participants domain Onset])

Required in Low-Level Delta Potentially Associated High-Level Change

Add([participants domain X]) Generalize_Domain(participants, Onset, X)

Add([participants domain X]) Specialize_Domain(participants, Onset, X)

--- Delete_Domain(participants, Onset)

Del([participants type Property])Del([participants range X])

Delete_Property(participants, Onset, X)

… …

OperationsPull_Up_Class(*,*,*) [not in the table]Delete_Property(participants,*,*) [necessary triples not found]Specialize_Domain(participants, Onset, Event) [conditions not true]Generalize_Domain(participants, Onset, Birth) [wrong parameter (triples not found)]Generalize_Domain(participants, Onset, Event) [DETECTABLE (ASSOCIATED)]Delete_Domain(participants, Onset) [composite changes have priority]


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Implementation

Algorithm implemented for experiments and evaluation

Uses the APIs of SWKM

Platform for efficient and scalable management of dynamic RDF/S ontologies and data

Query, update, low-level delta, high-level delta, versioning, …


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Performance

Complexity: O(max{N1,N2,N2})

Linear average-caseHighly dependent on the detected changes (type, number)


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Evaluation: Usefulness and Intuitiveness

L is well-defined (changes used in practice)

GO: add/delete class, comments changingCIDOC: add/delete/rename properties

Results confirmed by literature/editor notes


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Evaluation: Conciseness

Basic ≈ Low-Level

Basic+Composite+Heuristic << Low-Level


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Manual Change Recording (CIDOC)

Editor notes

Delete class: 3

Add property: 54

Delete property: 16

Rename property: 24

Redirect properties (domain): 14

Redirect properties (range): 14

Detection result

Delete class: 6

Add property: 58

Delete property: 18

Rename property: 30

Generalize_Domain: 13Specialize_Domain: 1

Generalize_Range: 14Specialize_Range: 1Change_Range: 1


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Conclusion

High-level change detection

A posteriori detection (input: V1, V2)

No further information needed (e.g., logs, change recording etc)

Formal semantics

Formal results (reversibility, determinism, …)Non-heuristic based (except for heuristic changes)No need for precision and recall evaluation

Efficient, sound and complete detection algorithm

Nice informal properties

Conciseness, intuitiveness

Future work: more operations, evaluation on other datasets, evaluation with real users


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References

1. Vicky Papavassiliou, Giorgos Flouris, Irini Fundulaki, Dimitris Kotzinos, Vassilis Christophides. On Detecting High-Level Changes in RDF/S KBs. In Proceedings of the 8th International Semantic Web Conference (ISWC-09), to appear, 2009

2. Vicky Papavassiliou, Giorgos Flouris, Irini Fundulaki, Dimitris Kotzinos, Vassilis Christophides. Formalizing High-Level Change Detection for RDF/S KBs. Technical Report TR-398, FORTH-ICS, 2009

Documents

W3C Invited Talk 16/09/2009Giorgos Flouris1 High-Level Change Detection in the Semantic Web Institute of Computer Science Foundation for Research and Technology