Data and Knowledge Evolution
Giorgos Flouris
fgeo@ics.forth.gr
Open Data Tutorials, May 2013
Giorgos Flouris
Slides available at:
http://www.ics.forth.gr/~fgeo/Publications/WOD13.ppt
Open Data Tutorials, May 2013
1
World Wide Web
WWW (and HTML) focus on human readability
Page presentation (fonts, colors, images, …)
Human understanding
Presentation  Semantical content
Content is not formally described (for a machine to understand)
WWW contains documents, not data
Giorgos Flouris
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Problems with the Current Web
Search and access becomes difficult
Software ignorant of the semantical content of a web page
Keyword search
High 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 policy
Fetch 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
[BLHL01]
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
partners
http://www.w3.org/2001/sw/
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Semantic Web in Practice
Web of data, rather than documents
HTML for presentation
Semantical languages for semantical content
Readable and understandable by humans and machines
Semantic Web languages, protocols, etc
Web page annotation (metadata descriptions etc)
Publication of data on the Internet
Efficient communication and manipulation of data over the Internet
Different applications
Efficient searching
Sharing of data (e-science, e-government, remote learning, …)
Linked Open Data (more on that later)
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Ontologies and Data (Datasets)
An ontology is an explicit specification of a shared
conceptualization of a domain [Gru93]
Precise, logical account of the intended meaning of terms
Common (shared) interpretation of terms
Formal vocabulary for information exchange (humans/machines)
Ontologies (vocabularies) allow the description of data
Terminology:
Ontology = vocabulary = schema
Data = instances
Dataset = data and the related ontology (i.e., a dataset may contain
schema and/or data)
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Dataset Dynamics
Datasets change constantly
World changes (dynamic models)
View on the world changes (new knowledge, measurements, etc)
Perspective and usage changes
Example:
Gene Ontology (information about gene products): daily versions
DBPedia: 1,4 updates/second (http://live.dbpedia.org/LiveStats/) [MLA+12]
Need methodologies to cope with the problems related to dynamicity
Evolution (modify a dataset in response to a change)
Versioning (keep track of versions and their relations)
Debugging, cleaning, repairing, quality (maintain consistency and quality
in a dynamic environment)
Change monitoring, detection and propagation (identify changes and use
them to synchronize remote datasets)
…
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Linked (Open) Data
Datasets can be interlinked
Sharing knowledge
Reusing knowledge
Modular development
Reuse of schemas
Linked Open Data (LOD) movement
Constantly growing
31 billion triples and 295 datasets as of September 2011
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Linked Open Data Cloud Diagram
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
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Linked Open Data Challenges
Both a blessing and a curse
Added-value benefits
Discovery of unknown correlations, connections, relationships
Vast amount of interrelated knowledge
No central control, everyone can publish and relate to others
Quality of datasets lies/depends on different providers
A change in one dataset affects all related ones
Several new problems related to dynamics
Propagation of changes among interrelated datasets
Maintaining the quality of local datasets
Co-evolution
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Scope: Dynamic Linked Datasets
You are here
Dynamic
Datasets
Giorgos Flouris
Linked
Datasets
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Purpose of This Talk
To survey different research areas related to dynamic LOD
Remote Change Management
Repair
Data and Knowledge Evolution
Categorize and classify works in each field
Broad but shallow description
Several references for more in-depth study
No claims of completeness (references are just indicative)
Two relevant surveys: [FMK+08, ZAA+13]
Emphasis on some related work done in FORTH
Will avoid technical discussion
References will be given for further details
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Defining Remote Change Management
Managing the effects of remote changes on interlinked datasets
Remote changes have profound effects on local datasets
Good practices are important
—Proper versioning, change logging, adaptation to remote changes, …
Attention exploded after the success of the LOD paradigm
Related research questions
How should I version my data?
How can I efficiently monitor changes in my dataset?
How can I detect changes in remote datasets?
How does the evolution of remote datasets affect my data?
How can I efficiently propagate changes from one dataset to
another?
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Remote Change Management: Visualization
RD1
Versioning, Change Monitoring
Remote Site
RD0
Change Detection
LD1
Change Propagation
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Open Data Tutorials, May 2013
Local Site
LD0
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Remote Change Management: Structure
Three subfields
Versioning
Change monitoring and detection
Change propagation
Structure
Introduction, definition of subfields
Literature review
An approach for change detection [PFF+13]
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Defining Repair
Assessing and improving the quality and the semantical or
structural integrity of the data
Maintaining consistency, coherency, validity
Restoring consistency, coherency, validity, when violated
Assessing and improving quality
Preserve quality/integrity in the face of remote changes
Related research questions
How can I preserve the integrity and quality of my data in a
dynamic and interlinked environment?
How can I guarantee consistency and validity?
How can I restore consistency and validity, if violated?
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Repair: Visualization
D0
D1
Repair Process
(Cleaning, Debugging,
Repairing, Quality Enhancement)
Assessment Module
(Diagnosis, Quality Assessment)
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Repair: Structure
Four subfields
Cleaning
Debugging
Validity repair
Quality enhancement
Structure
Introduction, definition of subfields
Literature review
An approach for validity repair [RFC11]
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Defining Evolution
Modifying a dataset in response to a change in the domain or its
conceptualization
Identify the result of applying new information on the dataset
Determine the result of change propagation from remote datasets
Understand the process of change
Related research questions
What is the semantics of evolution and change?
How can I efficiently compute the ideal evolution result?
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Evolution: Visualization
Real
World
D0
Dataset
Giorgos Flouris
Evolution
Algorithm
D1
Delete_Class(…)
Pull_Up_Class(…)
Rename_Class(…)
…
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Evolution: Summary
Evolution topics
Understanding the evolution challenges
Understanding the process of change
—Balancing between philosophical and practical considerations
Cross-fertilization with belief change
Structure
Introduction, connection with belief change
Understanding the process of change
Literature review
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General Structure of this Talk
A. Introduction to RDF/S, DLs, OWL
B. Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C. Repair
Part I
(2 hours)
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D. Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
Part II
(1 hour)
The final few slides contain citations for the references in this talk
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Talk Structure (A)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Datasets
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 LOD
RDF/S, OWL
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Visualization, Triples, Serialization
Visualization
Triple Representation
Period
Actor
Event
participants
Existing
started_on
Stuff
Onset
Birth
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]
participants
Giorgos
G_Birth
Serialization (RDF/XML)
<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>
instantiation
subsumption
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RDF and RDFS
An RDF dataset consists of triples
RDFS adds semantics
Subsumption hierarchies (classes and properties)
—Transitive
Instantiation
—Inheritance, implicit instantiation
Sometimes more than subsumption/instantiation is needed
Combining concepts, roles to form more complex relations
—Concept definitions: a mother is a female who has a child
—Other knowledge: all items stored in warehouse X are flammable
Constraints on data
—Each person must have one mother
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Extensions of RDF/S: DLs (1/2)
Description Logics (DLs)
http://dl.kr.org/
Formal underpinning of web representation languages
Family of logical formalisms
—Well-defined semantics
—Model-theoretic reasoning based on interpretations
Formally studied
—Expressiveness, reasoning tools, computational complexity, …
Components
Individuals: specific objects (instances) – Giorgos
Concepts: sets of individuals (classes) – Parent
Roles: sets of pairs of individuals (properties) – has_child
Operators: , ⊓, , {.}, ⊤, …
Connectives: ⊑, ≡, …
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Extensions of RDF/S: DLs (2/2)
Definitions, partial definitions, constraints, subsumptions, …
A mother is a female who has a child
—Mother ≡ has_child ⊓ Female
Each person must have one mother
—Person ⊑ has_child-1.Mother
A great variety of DLs (trade-off involved)
Different properties
Different expressive power
Different reasoning complexity
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Extensions of RDF/S: OWL
OWL (Web Ontology Language)
http://www.w3.org/2004/OWL/
General-purpose representation language
Compatible with the architecture of the Semantic Web
A family of languages
Flavors: OWL-Lite, OWL-DL, OWL Full
Profiles: OWL 2 EL, OWL 2 QL, OWL 2 RL
Different expressiveness (and complexity)
Each corresponds to a specific DL
Useful from a modeling perspective
Expressive but not too complex
Appealing computationally
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Representation Languages in LOD
Mostly RDF
With RDFS semantics
—Instantiations
—Class subsumption
—Property subsumption is rare
Some OWL
Mostly OWL Lite
Extensive use of owl:sameAs
—Often abusing it [HHM+10]
OWL 2 profiles are gaining ground
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Talk Structure (B1)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Motivation for Remote Change Management
Crucial problem for dynamic linked datasets
Linking: datasets linked to other datasets (e.g., vocabularies)
Dynamics: changes cause problems to linked datasets
No central curation or control
—No control over (or knowledge of) other datasets’ evolution process
Curators don’t bother annotating and logging changes
—Temporal and versioning information is usually missing [RPH+12]
Remote change management seeks solutions to allow:
DR
Keeping track of versions
Restoring previous versions
uses
Assessing compatibility of versions
Monitoring and detecting changes
Tracing back the evolution history (of datasets, concepts, …) DL
—For visualization and understanding
Propagating changes to synchronize linked datasets
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Subfields of Remote Change Management
Remote Change Management
Versioning
—Keep track of versions
Change monitoring and detection
—Monitoring: record changes as they happen
—Detection: identify changes after they happen
Change propagation
—Propagate changes across linked datasets for synchronization purposes
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Versioning
Versioning
Keep track of versions
Identify different versions of a dataset
Enable transparent access to the “correct” version (smooth interoperation)
Issues involved
Identification
—Determine which versions to store and how to identify them
—Manually or automatically (syntactical, semantical considerations)
—Packaging of changes
Relation between versions
—A sequence or a tree
Compatibility information
—Backwards/forwards compatibility and how to determine it (often manually)
—Dataset-wide compatibility or fine-grained compatibility (e.g., at resource level)
—Metadata on the different versions
Transparent access
—Relate versions with (compatible) data sources, applications etc
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Change Monitoring and Detection
Change monitoring
Record changes as they happen
—Manual (error-prone and often incorrect)
—Automatic (not used in practice)
In the good will of the dataset owner
Sometimes change logs are inaccessible
Change detection
DR
uses
Identify changes after they happen
Based on the previous and current versions
DL
In both cases, a change language is required
Supported set of changes, along with their semantics
Can be low-level or high-level
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Change Propagation
Change propagation
Communicate changes to linked datasets for synchronization
Push-based or pull-based propagation
Push-based: locally-initiated, via “registration” or via monitoring
and versioning
Pull-based: consumer-initiated
Communication based on deltas (rather than versions)
Reduce communication overhead
Reduce storage requirements
On average, 2-3% of a dataset changes between versions [OK02]
Deltas are based on a language of changes
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Talk Structure (B2)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Versioning Approaches (1/3)
Capture different aspects of versioning, such as:
Detecting versions
Storing versions efficiently
Allow cross-snapshot queries
—Find gene products whose functions have not changed in the last 50 versions
—Determine price fluctuation for x along different versions of the product catalog
Early versioning approaches inspired by SVN
Good for files, not directly adaptable to semantical languages
SHOE language [HH00]
Machine-readable version information (e.g., compatibility)
Provided by curator as SHOE statements
Memento [SSN+10]
Fine-grained versioning at URI level (resources, web pages)
Machine-readable version information, in the HTTP header
—Timestamps, traversal information (prior/current versions) etc
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Versioning Approaches (2/3)
Theoretical foundations for versioning [HP04]
Formal definitions to capture notions such as:
—Compatibility (between versions)
—Commitment (resources committing to a certain ontology)
—Ontology perspectives (the part of the web committing to an ontology)
Temporal approaches [HS05, PTC05, KLGE07]
For capturing temporal relations between versions
For allowing cross-snapshot queries
Versioning in multi-editor environments [RSDT08]
Via change monitoring
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Versioning Approaches (3/3)
Automatically detecting version relationships [AAM09]
Using heuristics based on URIs
Study of “relatedness” between versions [CQ13]
A model of “relatedness” between vocabularies from various
sources
Similar to links in web pages
POI: Partial Order Index [TTA08]
Efficient method for storing versions and their differences
Stores several versions, exploiting their common triples for
efficient storage
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Change Languages (1/2)
Change languages necessary for monitoring, detection,
propagation
Granularity
Low-level (or atomic, or elementary)
—Simple add/remove operations
—Add(s,p,o), Delete(s,p,o)
—Simple to detect and define
—Focus on machine-readability: determinism, well-defined semantics
High-level (or complex, or composite)
—More coarse-grained, compact, closer to editor’s perception and intuition
—Generalize_Domain(P,A), Delete_Class(A)
—More interesting; harder to detect and define
—Focus on human-understandability: often unclear and/or informal semantics
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Change Languages (2/2)
Many different high-level languages (no standard)
[HGR12, JAP09, PFF+13, SK03, AH06, DA09, PTC07, …]
Some are domain-specific (e.g., [HGR12])
Some are dynamic (e.g,, [AH06, DA09, PTC07])
—Allow custom, user-defined changes
Some allow terminological changes (e.g., [PFF+13])
—Rename, merge, split
—Common, but tough to detect (easily confused with add/delete)
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Representation Issues
Deltas are just sets of changes from the change language
Changes usually represented using a change ontology
Ontology represents changes
A specific change is an instance of such an ontology
Deltas associated with sets of such instances
Different proposals [NCLM06, KFKO02, KN03, PT05]
Allows the manipulation and communication of deltas/changes
using standard Semantic Web technologies
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Change Monitoring Approaches
Using a version log [PT05]
Logging actions on the dataset
Use it for change detection, as well as proper versioning
Good quality, high-level change monitoring
Based on a dynamic language of changes
Using migration specifications [ZZL+03]
Similar to logs, but with a more formal structure
DBPedia change monitoring [MLA+12]
http://live.dbpedia.org/
Live versions, as opposed to “standard” versions
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Low-Level Change Detection (1/2)
SemVersion [VWS+05]
Developed in Karlsruhe (FZI, AIFB)
Low-level change detection tool for RDF
Provides also versioning functionalities
Allows cross-snapshot queries
For RDF [ILK12]
Low-level change detection based on set difference
Aggregating and compressing deltas
Also dealing with versioning issues
For RDF/S [ZTC11]
Takes into account semantics (RDFS inference)
Four different methods to compute deltas (all based on set difference)
Formal analysis of these methods’ properties and semantics
Extension: effect of blank nodes on change detection [TLZ12]
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Low-Level Change Detection (2/2)
Bubastis (http://www.ebi.ac.uk/fgpt/sw/bubastis/index.html)
Simple diff tool (triple-based comparison)
Basically RDF, but also supports OWL
For DL-Lite [KWZ08]
Formal, semantical approach
For EL [KWW08]
Uses a concept-based description of changes
For propositional knowledge bases [FMV10]
Propositional, but generic; it can be applied to DLs
Formal analysis of the problem
Also dealing with propagation semantics
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High-Level Change Detection (1/2)
For OWL: PromptDiff [NKKM04], OntoView [KFKO02]
Employ heuristics and probabilistic methods
Evaluation using precision/recall metrics against a gold standard
Integrated into tools that also provide versioning functionalities
For RDF/S [PFF+13]
Dealing with both machine-readability and humanunderstandability
Also dealing with propagation (applying changes)
To be discussed in detail later
COnto-Diff [HGR12]
Rule-based approach
Also dealing with propagation
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Change Propagation Approaches
Usually part of other tools [SMMS02, MMS+03]
Versioning, monitoring tools (push-based propagation)
Detection tools (pull-based propagation)
Evolution and repair tools (pull-based propagation)
—Adapt your data to be “compatible” with the new remote version
SparqlPush [PM10]
Push-based propagation of changes on SPARQL “views”
PRISM, PRISM++ [CMZ08, CMDZ10]
High-level language of schema changes for relational data
—Also supports changes on the integrity constraints
Identifies and propagates the changes required in the data for
abiding to the new schema
Query and update rewriting
—For applications that try to access the old schema
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Other Change Management Approaches
Complete approach for XML [SP10]
Representing changes inline with the data using a graph
(“evograph”)
Supports different change representation languages (both lowlevel and high-level)
Timestamps changes
Monitoring: evograph can be used to log the changes
Propagation: changes can be accessed and propagated
Versioning: timestamps in changes can be used to generate
snapshots (versions) at different times
Allows cross-snapshot queries
Fairly generic, can be adapted for RDF
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Talk Structure (B3)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Our Approach on Change Detection
Purpose of this work: change detection [PFF+13]
A posteriori detect the differences (delta or diff) between versions
in a concise, intuitive and correct way
Main design choices
Change detection based on a general-purpose high-level language
Human-understandable, but also machine-readable
Clear, formal semantics
Provable formal properties and functionality guarantees
Detection and application (propagation) semantics
V1
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C1
V2
C2
V3
C3
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V4
C4
V5
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Sample Evolution
Version 1 (V1)
Version 2 (V2)
Period
Actor
Actor
Event
Event
Persistent
participants
Existing
participants
started_on
Onset
started_on
Onset
Birth
Evolution
Stuff
Stuff
Birth
participants
Giorgos
G_Birth
participants
Giorgos
G_Birth
instantiation
subsumption
instantiation
subsumption
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Analyzing the Evolution (Using Triples)
Triples in V1 (partial list)
Triples in V2 (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]
…
Giorgos Flouris
[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
Triples in V1 but not in V2
(added triples)
(deleted triples)
[Event domain participants]
[Persistent type Class]
[Stuff subclass Persistent]
[started_on domain Persistent]
[Birth subclass Event]
Version 1 (V1)
[Period type Class]
[Event subclass Period]
[participants domain Onset]
[Existing type Class]
[Stuff subclass Existing]
[started_on domain Existing]
[Birth subclass Onset]
Version 2 (V2)
Period
Actor
Actor
Event
Event
Persistent
participants
Existing
participants
started_on
Onset
started_on
Onset
Birth
Evolution
Stuff
Stuff
Birth
participants
Giorgos
participants
Giorgos
G_Birth
instantiation
subsumption
Giorgos Flouris
instantiation
subsumption
G_Birth
Low-Level Delta
Add([Event domain participants])
Add([Persistent type Class])
…
Del([Period type Class])
…
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Analyzing the Evolution (Visually)
Version 1 (V1)
Version 2 (V2)
Period
participants
Actor
Actor
Event
Persistent
Event
started_on
Birth
Onset
participants
Existing
started_on
Onset
Evolution
Stuff
participants
Stuff
Birth
participants
Giorgos
G_Birth
instantiation
subsumption
Giorgos Flouris
Giorgos
G_Birth
High-Level Delta
Generalize_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
Version 1 (V1)
Version 2 (V2)
Period
participants
Actor
Actor
Event
Persistent
Event
started_on
Onset
Birth
participants
Existing
started_on
Onset
Evolution
Stuff
participants
Stuff
Giorgos
Birth
participants
Giorgos
G_Birth
instantiation
subsumption
Giorgos Flouris
Low-level delta
Del([participants
Del([Period
Del([Birth subclass
type
domain
Class])
Onset])
Onset])
Add([participants
Del([Event
Add([Birth
subclass
subclass
domain
Period])
Event])
Event])
Open Data Tutorials, May 2013
G_Birth
High-level delta
Generalize_Domain
Delete_Class
Pull_Up_Class
(Period,Ø,{Event},Ø,Ø,Ø,Ø)
(participants,
(Birth, Onset,
Onset,
Event)
Event)
56
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])
Del([Stuff subclass Existing])
Del([started_on domain Existing])
Add([Persistent type Class])
Rename_Class(Existing, Persistent)
Add([Stuff subclass Persistent])
Add([started_on domain Persistent])
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Challenges for High-Level Languages
High-level deltas are superior
More concise (e.g., Rename_Class)
More intuitive (e.g., Pull_Up_Class)
Carry additional information (e.g., Generalize_Domain)
Challenges for high-level languages
Must be deterministic (exactly one high-level delta)
Must be fine-grained enough to capture subtle changes
Must be coarse-grained enough to be concise
Must be intuitive and close to editor’s perception of the changes
Compatible detection and application algorithms
Intuitive results
Efficient
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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 (132 high-level ones)
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Types and Number of Defined Changes
Changes
(134)
Low-Level
(2)
High-Level
(132)
Add
Del
Giorgos Flouris
Basic
(54)
Composite
(51)
Heuristic
(27)
Delete_Subclass
Delete_Domain
Pull_Up_Class
Change_Domain
Rename_Class
Split_Class
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Results on L: Granularity
Granularity problem: solved by defining levels of changes
Basic Changes: fine-grained, roughly correspond to low-level
Composite Changes: coarse-grained, group several basic changes
together
Heuristic Changes: based on heuristics, necessary for Rename,
Merge, Split etc; require mappings between URIs
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: 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 delta
Exactly 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: Propagation
Version 1 (V1)
Version 2 (V2)
Period
Actor
Actor
Event
Event
Detect C
participants
Existing
participants
started_on
Onset
Persistent
started_on
Onset
Birth
Apply C
Stuff
Apply C-1
Stuff
Birth
participants
Giorgos
G_Birth
participants
Giorgos
G_Birth
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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
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V2
C2
V3
C3
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V4
C4
V5
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Change Detection: Evaluation
Detection and application algorithms implemented for evaluation
Performance
Complexity: O(max{N1,N2,N2})
Performance depends on the detected changes (type, number)
Bottleneck: calculating the low-level delta (>80% of total time)
Intuitiveness
Changes in our language are used in practice
Results confirmed by literature/editor notes (CIDOC, GO)
Better than CIDOC’s manually recorded changes (18 changes missed)
Conciseness
Basic ≈ Low-Level
Basic + Composite + Heuristic << Low-Level
Up to 80% reduction, depending on the case
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Summary and Conclusions: RCM
Remote change management is at the heart of LOD
Uncontrolled character of LOD makes it critical
Various related fields
Versioning, change monitoring and detection, change propagation
Unfortunately, not used in practice in LOD
Presented a formal approach for change detection [PFF+13]
Other possible directions (related to LOD)
Best practices should be studied and promoted
—Automated versioning and monitoring mechanisms embedded in evolution
tools/editors
—Understand and use temporal and provenance metadata on versions
Improved change monitoring and detection
—A standard change language?
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Talk Structure (C1)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Motivation for Repair
Published data is usually problematic
Several different types of problems in LOD [HHP+10]
Pedantic web initiative (http://pedantic-web.org/)
—Advice for data owners on how to prevent common problems in their data
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Causes of Data Problems
Several reasons for data problems
Erroneous data (faulty sensors, human mistakes etc)
Different symbolisms and terminology
Modeling errors (e.g., all birds fly)
Requirements (constraints) on the data may change
—E.g., when applications’ needs change
Reuse data by different providers (no quality guarantees)
Quality jeopardized by re-use and open evolution
Integration/merging of datasets
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Generic Approaches
Four ways to deal with problems in data [HHH+05]
Prevent it (careful evolution, merging etc)
—Can only prevent problems caused by changes in the local dataset
Correct it (repair)
—Actively address the problem (after it appears)
Ignore it (consistent query answering, non-monotonic reasoning)
—CQA: popular in database community; prevents user from noticing the
problem by rewriting queries (common denominator approach)
—NMR: popular in AI community; avoid trivialization of reasoning
(paraconsistent reasoning, defeasible reasoning, default reasoning, …)
Use versions (versioning)
—Make sure you refer to the correct (compatible) version
—Only when the problem is due to a remote change
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Subfields of Repair
Cleaning
Mainly related to literal quality
Terminology, symbols, metric units etc
Debugging
Consistency (at least one model)
Coherency (no unsatisfiable classes)
Relevant for DL/OWL only
Validity repair
Satisfaction of custom integrity constraints (e.g., business rules)
Expressed in OWL, DL, Datalog or predicate logic
Quality enhancement
Assessing and improving the quality of data
Different dimensions (timeliness, completeness, reputation, …)
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Cleaning
Literals in LOD are often messy, and have to be “cleaned up”
Different formats for names, dates etc
—&gf name “Giorgos Flouris”
—&gf birth_date 03/05/76
—&gf birthplace “Hellas”
&gf name “Flouris, Giorgos”
&gf birth_date 05/03/76
&gf birthplace “Greece”
—Paris land_area 105,4
—Paris population 2.234.105
Paris land_area 105.4
Paris population 2,234,105
—Paris land_area 105,4
—&x price 30
Paris land_area 40,7
&x price 39
—&x price 0
&x price “free”
—LIP6 addr “4, P. Jussieu”
LIP6 street “P. Jussieu”
Different symbols
Different metric units
Inconsistent values
Data is not in the desired form (data transformation)
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Debugging
Coherency
No unsatisfiable classes
Indicates good modeling
hasHorns
hasHorns
Consistency
At least one model
Avoids reasoning triviality
canFly
Horse
canFly
Bird
Relevant for DL/OWL only
Unicorn
Penguin
Pengo
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Validity Repair
Validity repair
Satisfaction of custom integrity constraints (e.g., business rules)
Encode context- or application-specific requirements
—PROV-DM: http://www.w3.org/TR/2013/REC-prov-constraints-20130430/
Applications may be useless over invalid data
Expressed in OWL, DL, Datalog, Datalog±, predicate logic, …
Different expressive power
Different semantics (OWA/CWA, UNA) [TSBM10, MHS09]
Various types of constraints
Functional, inverse functional, transitivity, cardinality constraints
Disjointness constraints
Primary key, foreign key, inclusion constraints
Tuple-generating dependencies (tgd), equality-generating dependencies
(egd)
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Quality Enhancement
Quality is defined as “fitness for use” [Jur74]
Multi-faceted (timeliness, completeness, reputation, …)
Task-dependent
Subjective
Assessing quality
Via assessment functions (e.g., [BC09]) or SPARQL queries (e.g.,
[FH10])
Some kind of combined scoring over the relevant dimensions
Improving (enhancing) quality
Usually manual
Tries to improve the assessment score
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Talk Structure (C2)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Cleaning Tool: OpenRefine
Open source
Originally developed by google (GoogleRefine)
http://openrefine.org/
Applies on various representations of the input data
CSV/TSV, Excel, JSON, XML, RDF as XML, etc
RDF extension
Functionalities (related to this talk)
Data exploration and cleaning
—Both automated and manual (interface assists in manual cleaning)
Data transformation (format conversion)
—Uses GREL (Google Refine Expression Language) and regular expressions
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Cleaning Tool: ODCleanStore
Web application, written in Java
Developed by Charles University (Prague)
http://www.ksi.mff.cuni.cz/~knap/odcs/sections/odcs.html
Functionalities (related to this talk)
Cleaning
—Via “transformers” (policies for cleaning)
—Expressed using SPARQL or regular expressions
Quality assessment
—Transformer assigns a score to data
Validity repair
—Supports conflict resolution for functional properties
—Decides what to drop based on the quality of the data items involved
—Supports aggregation functionalities based on “aggregation policies”
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Other Cleaning Approaches
Involve users in the loop [KHS12]
Manual requests for improvements (cleaning, quality, …)
Patch Request Ontology (PRO)
Use a GWAP (Game With A Purpose) for identifying data problems
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Debugging: Literature Overview
Identify and resolve inconsistency/incoherency
Two phases
Diagnosis: identify inconsistency/incoherency
Repair: remove inconsistency/incoherency
Literature mostly dealing with diagnosis
Repair requires additional user input
Diagnosis is more than reasoning
Pinpoint the causes of inconsistency/incoherency
Repair
User input required (manual or semi-automatic approaches)
Automatic approaches also require user input or domain
knowledge (ad-hoc solutions)
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Debugging Approaches
Diagnosis using tableau-based algorithms for various DLs
Identify minimal sets of responsible axioms
—[SC03, MLBP06, PT06, WHR+05]
Identify responsible parts of axioms (more fine-grained)
—[KPS+06, LPSV06]
Repair
Manual: editors and related tools
—Onion [MWK00], PROMPT [NM00], Chimaera [MFRW00]
Semi-automatic
—Interactive approach via suggestions: ORE tool [LB10]
Automatic:
—Using external information, e.g., for stratified datasets [QP07, MLB05]
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Validity Repair: Literature Overview
Identify and resolve invalidity (custom constraints)
Two phases
Diagnosis: identify invalidity
Repair: remove invalidity
Literature mostly dealing with diagnosis
Repair requires additional user input
Diagnosis is more than validation
Pinpoint the causes of invalidity
Repair
User input required (manual or semi-automatic approaches)
Automatic approaches also require user input or domain
knowledge (ad-hoc solutions)
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Validity Repair Approaches
Not much work in repairing custom constraints in LOD
A large body of related work for the relational setting
—For various constraint types and repair methodologies
Existing tools
Stardog (http://www.stardog.com/docs/)
—Commercial RDF database that supports validation of custom constraints
Rondo (relational/XML) [Mel04]
—Repair based on a fixed “importance” of data items
Declarative repairing based on preferences [RFC11]
—To be discussed in detail later
Repairing functional properties ([FRPV+12], Sieve [MMB12])
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Data Quality Frameworks (1/4)
Many different quality assessment methodologies and
frameworks
Several different quality dimensions
Different works consider different dimensions
Different proposals for their classification and organization
There is no single, generally accepted data quality framework
Cannot be one
Different applications have different needs
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Data Quality Frameworks (2/4)
Quality dimensions, quality indicators, scoring functions and
assessment metrics [BC09]
Different quality dimensions
—Timeliness, completeness, reputation, …
Each dimension associated with different indicators
—Timeliness: last modification date, creation date, …
Each indicator associated with different scoring functions
—E.g., days since last update
Scoring functions from relevant indicators are combined using
assessment metrics
—E.g., Reputation_value*0,6 + days_since_update*0,4
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Data Quality Frameworks (3/4)
[RH09]
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Data Quality Frameworks (4/4)
[ADA98]
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Talk Structure (C3)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Our Approach on Validity Repair
Declarative approach for validity repair [RFC11]
Main design choices
Both diagnosis and repair
Applicable for RDF/S
Adopted relational semantics (CWA) for the constraints
Generality on the supported constraints (DEDs)
Minimal user interaction (all info provided at input)
Automatic diagnosis
Automatic repair using preferences (provided by the user at input)
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RDF/S Representation Model
Express RDF/S over an adequate relational schema
Hybrid method
—C_IsA(A,B): A is a subclass of B
—C_Inst(x,A): x is an instance of A
—Domain(P,A): the domain of P is A
—…
Alternatives
Schema-specific
—One table/predicate for each class/property (A(x), B(x), P(x,y), …)
—Not amenable to changes (e.g., delete class)
Schema-agnostic (triple-store)
—One table with three columns (spo)
—Harder to define constraints, less intuitive
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Allowed Constraints
Considered a very general class of constraints
Disjunctive Embedded Dependencies (DEDs) [Deu09]
Very general class
Functional, inverse functional, transitivity, cardinality constraints
Disjointness constraints
Primary key, foreign key, inclusion constraints
Tuple-generating dependencies (tgd), equality-generating
dependencies (egd)
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Constraints
Express validity constraints over the aforementioned schema:
Class subsumption must be acyclic
—x,y C_IsA(x,y)  C_IsA(y,x) ⊥
Correct classification in property instances
—x,y,p,a P_Inst(x,y,p)  Domain(p,a)  C_Inst(x,a)
—x,y,p,a P_Inst(x,y,p)  Range(p,a)  C_Inst(y,a)
Closed World Assumption (CWA)
Failure to prove something, is a proof for its negation
Syntactical manipulations on constraints allow
Diagnosis
—Finding violated constraints
Repair
—Identifying repairing options per violation
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Repairing Example
Dataset D0
Class(Sensor), Class(SpatialThing), Class(Observation)
Prop(geo:location)
Sensor
Domain(geo:location,Sensor)
Range(geo:location,SpatialThing)
Observation
Inst(Item1), Inst(ST1)
P_Inst(Item1,ST1,geo:location)
C_Inst(Item1,Observation), C_Inst(ST1,SpatialThing)
Correct classification in property instances
x,y,p,a P_Inst(x,y,p)  Domain(p,a)  C_Inst(x,a)
Item1
geo:location
SpatialThing
Schema
Data
geo:location
ST1
Item1 geo:location ST1
P_Inst(Item1,ST1,geo:location)D0
Sensor is the domain of geo:location
Domain(geo:location,Sensor)D0
Item1 is not a Sensor
C_Inst(Item1,Sensor)D0
Remove P_Inst(Item1,ST1,geo:location)
Remove Domain(geo:location,Sensor)
Add C_Inst(Item1,Sensor)
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Preferences for Repair
Which repairing option is best?
Data owner determines that via preferences
Preferences
Specified beforehand
High-level “specifications” for the ideal repair
Serve as “instructions” to determine the preferred (optimal)
solution
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Preferences (On Datasets)
D1
Score: 3
D2
Score: 4
D3
D0
Score: 6
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Preferences (On Deltas)
D1
Score: 2
D2
-P_Inst
(Item1,ST1,
geo:location)
D3
D0
Score: 1
-Dom
(geo:location,
Sensor)
+C_Inst
(Item1,Sensor)
Score: 5
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More Details on Preferences
Preferences on datasets are result-oriented
Consider the quality of the repair result
Ignore the impact of repair
Popular options: prefer newest/trustable information, prefer a
specific schema structure
Preferences on deltas are impact-oriented
Consider the impact of repair
Ignore the quality of the repair result
Popular options: minimize schema changes, minimize
addition/deletion of information, minimize delta size
Properties of preferences
Quality metrics can be used for stating preferences
Metadata on the data can be used (e.g., provenance)
Can be qualitative or quantitative
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Generalizing the Approach

For one violated constraint
1. Diagnose invalidity
2. Determine minimal ways to resolve it
3. Determine and return preferred solution based on the preference

For many violated constraints
 Problem becomes more complicated
 More than one resolution steps are required

Issues:
1. Resolution order
2. When and how to filter non-optimal solutions?
3. Constraint (and resolution) interdependencies
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Constraint Interdependencies

A given resolution may:
 Cause other violations (bad)
 Resolve other violations (good)

Optimal resolution unknown ‘a priori’
 Cannot predict a resolution’s ramifications
 Exhaustive, recursive search required (resolution tree)

Two ways to create the resolution tree
 Globally-optimal (GO) / locally-optimal (LO)
 When and how to filter non-optimal solutions?
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Resolution Tree Creation (GO)
–
–
Find all minimal resolutions for all the
violated constraints, then find the
optimal ones
Globally-optimal (GO)
 Find all minimal resolutions for one



violation
Explore them all
Repeat recursively until valid
Return the optimal leaves
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Optimal repairs
(returned)
100
Resolution Tree Creation (LO)
–
–
Find the minimal and optimal resolutions
for one violated constraint, then repeat
for the next
Locally-optimal (LO)
 Find all minimal resolutions for one



violation
Explore the optimal one(s)
Repeat recursively until valid
Return all remaining leaves
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Optimal repair
(returned)
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Comparison (GO versus LO)
Characteristics of GO
Characteristics of LO
Exhaustive
Less efficient:
Greedy
More efficient:
large resolution trees
Always returns optimal repairs
Insensitive to constraint syntax
Deterministic (result does not
depend on resolution order)
small resolution trees
May return sub-optimal repairs
Sensitive to constraint syntax
Non-deterministic (result may
depend on resolution order)
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Repair: Generality Results
The approach is very general
Thanks to the generality/flexibility of preferences
Repair approaches can be captured using adequately designed
preferences
Using either the LO or the GO strategy
All the current approaches that we checked
Practically all future ones
—This has been proved, under some general conditions regarding the
behavior of the repair approach
Our model can be viewed as a general approach engulfing other
repair approaches
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Repair: Algorithms and Complexity
Implemented both algorithms
Detailed complexity analysis for GO/LO and various different
types of constraints and preferences
Inherently difficult problem
Exponential complexity (in general)
Main exception: LO is polynomial (in special cases)
Theoretical complexity is misleading as to the actual
performance of the algorithms
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Performance in Practice
Performance in practice
Linear with respect to dataset size
Linear with respect to tree size
—Types of violated constraints (tree width)
—Number of violations (tree height) – causes the exponential blowup
—Constraint interdependencies (tree height)
—Preference (for LO): affects pruning (tree width)
Further performance improvement
Use optimizations
Use LO with restrictive preference
Currently considering a redesign for further improvement
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Summary and Conclusions: Repair
Data usually problematic
Different types of problems
Repair is done using different approaches depending on the
type of the problem
Cleaning, debugging, repairing, quality assessment and
enrichment
Presented a formal approach for validity repair [RFC11]
Other possible directions (related to LOD)
Most approaches detect problems, but don’t resolve them
Efficiency problems (for repairing algorithms)
Exploit external knowledge on the cause of the problem (e.g.,
propagation of invalidity by a linked dataset)
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Talk Structure (D1)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Motivation for Evolution
Reasons for evolution
New observations or experiments
Change in the viewpoint or usage of the dataset
Newly gained access to information (previously classified,
unknown or otherwise unavailable)
Incomplete or inaccurate conceptualization
Changes in the world itself
Repairing
Change propagation (cascading evolution in LOD)
Not an LOD-specific problem
But critical for LOD as well
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Definition of Evolution
The process of modifying a dataset in response to a change in
the domain or its conceptualization
Dealing with both data and schema changes
Original
Dataset
Evolution
Algorithm
Modified
Dataset
New
Data/Knowledge
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Evolution: Setting the Scope
Evolution is an overloaded term
Phases of evolution
Six phases in [SMMS02], five phases in [PT05]
Detecting the need for evolution, change propagation, logging
changes, versioning etc
Scope: apply the change and compute the new dataset
Out of scope: deciding on the change, evaluating the result,
managing versions, logging changes etc
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Explaining Evolution (1/4)
Chess Dataset
Representation Language: RDF
Chess
Piece
Wooden
Plastic
Change: Add([King rdf:type Red])
Red
White
Black
Schema Level
Data Level
King
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Explaining Evolution (2/4)
Chess Dataset
Representation Language: RDF
Chess
Piece
Wooden
Plastic
Is the King Wooden?
Red
White
Change: Del([King rdf:type Black])
Black
Schema Level
Data Level
King
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Explaining Evolution (3/4)
Chess Dataset
Representation Language: RDF
Chess
Piece
Wooden
Plastic
Red
White
Black
Change: Del([King rdf:type Wooden])
Some domain knowledge required
(extra-logical considerations)
Schema Level
Data Level
King
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Explaining Evolution (4/4)
Chess Dataset
Representation Language: OWL
Chess
Piece
Wooden and Plastic are disjoint
[Wooden owl:disjointClass Plastic]
Plastic
disjoint
Wooden
Change: Add([King rdf:type Plastic])
Red
White
Black
Schema Level
Data Level
Is the King Black?
Is the King Wooden?
King
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Side-effects in Evolution
Changes should not undermine the “quality” of the dataset
Side-effects: additional changes that need to be applied along with
the original change to maintain knowledge integrity and quality
Consistency, coherency, custom constraints, quality metrics, …
Main challenge in determining the evolution result
Determining side-effects
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Determining Side-effects
Challenges in determining side-effects
Evolution result not always obvious (even for humans)
—Understand the process of change
—Various philosophical considerations involved
Selection involved (extra-logical considerations)
—Domain expertise
—Preferences (trust, provenance, axiom “strength” or “entrenchment”)
Early evolution approaches rather naïve in this respect
Ignored such issues or addressed them in an ad-hoc manner
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Belief Change
Belief change (often referred to as belief revision)
The process of modifying a knowledge base in the face of new,
possibly contradictory knowledge
Mature, well-established field
Focuses for logical formalisms (propositional, first-order logic)
Recent survey on belief change [FH11]
Aims to understand the process of change
The philosophical/logical counterpart of dataset evolution
Can provide solutions and inspiration
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Cross-Fertilization with Belief Change
Cross-fertilization beneficial [Flo06, FPA05, FPA06]
Benefits
Similar problems
Differences on the underlying intuitions are minimal
Belief change field more mature
Frame problems and provide inspiration towards a solution
Protect from pitfalls
Avoid “reinventing the wheel”
Problems
Representation languages and formalisms are different
Assumptions regarding the underlying representation language
—These assumptions do not hold for LOD representation languages
Can reuse the ideas, not the results themselves
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Talk Structure (D2)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Challenges and Considerations
List of challenges and problems related to evolution
As well as some answers from the belief change field
Challenges and the complexity of formalisms
Some of the problems do not appear in simpler formalisms (RDF)
Some of the problems are only relevant in the presence of schema
—Data changes are simpler (on a fixed schema)
Part of the discussion only relevant for DL, OWL
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Importance of Implicit Data (Example)
Chess Dataset
Representation Language: RDF
Chess
Piece
Wooden
Plastic
Is the King Wooden?
Red
White
Change: Del([King rdf:type Black])
Black
Schema Level
Data Level
King
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Importance of Implicit Data
Explicit and implicit data equally
important
Explicit data more important
than implicit
The coherence viewpoint
King is Wooden
The closure of the dataset is
The foundational viewpoint
King is not Wooden
Only explicit knowledge is
considered during changes
considered during changes
—Belief set semantics
—Belief base semantics
Implicit data persistent
Implicit data volatile
—Explicit support not necessary for
—Retained only as long as there is
implicit data
explicit support
No discrimination
Discrimination
—No need to distinguish explicit
—Explicit data should be explicitly
data from implicit
—Redundant data can be deleted
marked as such
—Redundant data should persist
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Redundant Data
Chess Dataset
Representation Language: RDF
Chess
Piece
Wooden
Plastic
Change: Add([King rdf:type Black])
Red
White
Black
Schema Level
Data Level
King
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The King Is Black
Chess Dataset
Representation Language: RDF
Chess
Piece
Observation: the King is Black
Wooden
Plastic
Change: Add([King rdf:type Black])
Red
White
Black
Is the King Wooden?
Schema Level
Data Level
King
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Paint It Black
Chess Dataset
Representation Language: RDF
Chess
Piece
Action: King is painted Black
Wooden
Plastic
Change: Add([King rdf:type Black])
Red
White
Black
Is the King Wooden?
Schema Level
Data Level
King
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Static and Dynamic Worlds
Same dataset, same change, but different expected result
Different semantics between the two cases [KM91]
Different operations
Static world change semantics
The world does not change, but our perception of it changes
Modeling or conceptualization problems, new observation etc
Dynamic world change semantics
The world changes, and we need to keep ourselves up-to-date
No problems with the original conceptualization
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Types of Operations
Static world
Revision (add)
Contraction (delete)
Static
Dynamic world
Addition Revision
Update (add)
Erasure (delete)
Dynamic
Update
Deletion Contraction Erasure
Plus some more (forget, expansion, …)
Less well-studied
Ignored for this talk
Irrelevant for LOD or trivial
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Example: Revision and Contraction
Chess Dataset
Representation Language: OWL
Chess
Piece
Wooden
Plastic
Change #1
I believe that the King is not Black
Add([King rdf:type NotBlack],
[NotBlack owl:complementOf Black])
Red
White
Black
NotBlack
Schema Level
Data Level
Change #2
I do not believe that the King is Black
Del([King rdf:type Black])
King
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Expressing the Change
Different paradigms for expressing the change
Modification-based
—“Add([King rdf:type NotBlack], [NotBlack owl:complementOf Black])”
—The exact modifications that should be applied to accommodate the new knowledge
—Must know the conceptualization
—Closer to the ontology expert
Fact-based
—“I believe that the King is not Black”
—A new fact that should be accommodated in the dataset
—Extra layer of abstraction (extra step required to determine modifications)
—Closer to the domain expert
Handling multiple changes
Iterated belief change
Package versus choice semantics (contraction and erasure)
Merging
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Evolution Principles (Partial List)
Principle of Success (Primacy of New Information)
New information is unconditionally accepted
Non-prioritized belief change
Principle of Validity (Consistency Maintenance)
Belief change: usually logical consistency
LOD evolution: consistency, coherency, custom constraints, …
Principle of Minimal Change
Determine the side-effects that have minimal impact
—But satisfying the other principles
Corresponds to the selection process
Minimality depends on the task, context, user, application, …
Different postulates and intuitions (recovery, relevance etc)
Different metrics (model-based, formula-based, cardinality etc)
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Understanding the Principles
Chess Dataset
Representation Language: OWL
Chess
Piece
Wooden and Plastic are disjoint
[Wooden owl:disjointClass Plastic]
Plastic
disjoint
Wooden
Change: Add([King rdf:type Plastic])
Red
White
Black
Schema Level
Data Level
Invalidity (basically, inconsistency)
The King is both Wooden and Plastic
Three options (Minimal Change)
King
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Non-obvious Side-effects
Chess Dataset Chess_Piece
Representation Language: ALC DL
Chess
Piece
White
White
Wooden
Plastic
Plastic
I don’t believe that all White items
are Chess_Pieces
Red
White
Black
Schema Level
Data Level
Replace subsumptions with:
White ⊓ Chess_Piece ⊑ Plastic
Plastic ⊑ White ⊔ Chess_Piece
King
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Talk Structure (D3)
A.Introduction to RDF/S, DLs, OWL
B.Remote change management
1. Introduction, definition of subfields
2. Literature review
3. An approach for change detection [PFF+13]
C.Repair
1. Introduction, definition of subfields
2. Literature review
3. An approach for validity repair [RFC11]
D.Data and Knowledge Evolution
1. Introduction, connection with belief change
2. Understanding the process of change
3. Literature review
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Classes of Belief Change Approaches (1/2)
Postulates (one set for each operation)
Formalize the principles, using logical conditions
Essentially define the properties of a rational change operator
—Some principles not considered or given varying semantics
—Principle of Minimal Change is the most controversial
Do not uniquely define an operator
—A class of operators (expected rational results)
—Extra-logical considerations would determine the actual result
—Operator-specific (preferences, axiom strength, hard-coded semantics, …)
Belief change context [AGM85, KM91, Han91]
Evolution context [FKAC13, WWT10, QLB06a, QLB06b]
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Classes of Belief Change Approaches (2/2)
Construction methods
Intuitive constructions for a family of operators of a certain type
Representation theorems
—Proof that the constructed family corresponds exactly to the class of
operators that satisfy a certain set of postulates
Can be used as “templates” to construct rational change operators
Parameterized selection process
—Preferences, axiom strength, etc
Popular in belief change, not so much in evolution
Explicit algorithms
Implement a specific operator that satisfies some of the postulates
Hard-coded or parameterized selection process
Popular in evolution context, not so much in belief change
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Discussion on the Operator Types (1/2)
Connections between the various operators
Static: revision/contraction interdefinable [AGM85]
Dynamic: update/erasure interdefinable [KM91]
Model-theoretic characterization of the connection between
static/dynamic worlds (revision-update, contraction-erasure) [KM91]
Postulates critical for establishing those results
Revision and update more useful in practice
Contraction/erasure only used to express agnosticism
Contraction and erasure more interesting from a theoretical
perspective
More fundamental operations
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Discussion on the Operator Types (2/2)
Revise with φ (in belief change)
Contract ¬φ
—This resolves, a priori, any potential inconsistency problems
Add φ (without side-effects)
Revise with φ (in LOD)
Contract data that could potentially cause problems
—Inconsistency, incoherency, …
Add φ (without side-effects)
Contraction is the basis for revision
Simpler operation
Basically, if you know how to contract, you know how to revise
Most of the focus in belief change and also in LOD evolution
Same for update/erasure
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Evolution via Editors
Features
Intuitive interfaces
Easy to add/delete triples (but not facts)
Some help for determining the side-effects of a change
—Embedded reasoners and/or debugging/repair tools to propose side-effects
Additional facilities
—Versioning, monitoring, undo/redo, …
Main problems
User should be both ontology and domain expert
Not applicable in some cases
—Examples: automated agents, time-critical applications, massive streaming input
No formal properties
Examples
Protégé (http://protege.stanford.edu/)
NeOn toolkit (http://neon-toolkit.org/wiki/Main_Page)
OntoStudio (http://www.semafora-systems.com/en/products/ontostudio/)
KAON2 (http://kaon2.semanticweb.org/)
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Declarative Approaches
SPARQL Update (http://www.w3.org/TR/sparql11-update/)
For RDF
Fixed semantics, no side-effects
Data and schema operations (also bulk changes)
RUL [MSCK05]
For RDF/S, taking into account RDFS semantics
Fixed semantics, predefined set of side-effects per operation
Only for data operations (also bulk changes)
EvoPat [RHTA10]
Declaratively associate changes with side-effects (using SPARQL)
SPARQL queries determine whether side-effects should be applied
SPARQL update statements represent such side-effects
Tempus fugit [LRV09]
Event-driven, declarative specification of the operators’ semantics
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Fixed-Operations Approach
Standard approach in the early days (e.g., [SMMS02])
Set of supported operations (Add_Class, Add_Domain, …)
Identify potential problems and side-effects per operation
—Decision is hard-coded or user-defined (from a set of options)
—Example: when deleting a subsumption, how about implicit subsumptions?
Automatic or semi-automatic
Problems
No consensus on the language of changes
No limit on the number of operations
—What about unknown/unsupported operators?
No exhaustive formal analysis of potential side-effects
No formal properties or other guarantees
Incomplete understanding of the change process
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Approaches Inspired by Belief Change (1/2)
Revision in ALU DL [LM04]
Using preferences among axioms
Inspired by “epistemic entrenchment”
Revision in generic DLs [QD09]
Three model-based revision operators for DLs
Emphasis on the Principle of Irrelevance of Syntax
—Semantical, rather than syntactical, considerations should drive the result
Revision in DL-Lite [GQW12]
Using a graph-based algorithm
For data changes only (Abox)
Update and erasure in RDF/S [GHV06, GHV11]
Taking into account RDFS inference
Update is trivial, erasure is challenging (due to RDFS inference)
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Approaches Inspired by Belief Change (2/2)
Using the maxi-adjustment algorithm [MLB05, QLB06a, QLB06b]
Used to repair inconsistencies in propositional knowledge bases
Requires a stratification in the knowledge
Adapted for disjunctive DLs
Using kernel operators [Han94]
Kernels: minimal sets of formulas leading to inconsistency
—Minimal Inconsistency Preserving Sub-Tboxes (MIPS) [SC03]
OWL [HWK06]
DLs [QHHP08]
Generic formalisms with no negation (such as RDF) [RW07]
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Postulation Approaches in Evolution (1/3)
AGM: dominating paradigm in belief change [AGM85]
The single most influential work in the field of belief change
Contributions
AGM postulates: two sets of 6 basic and 2 supplementary
postulates
—One set for each operator (revision and contraction)
Plus various related results
—Partial meet contraction
—Representation theorems
—Connections between operators
Only for classical logics (satisfying certain assumptions)
Propositional, first-order, modal logics, …
Not for LOD formalisms (RDF/S, DLs, OWL)
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Postulation Approaches in Evolution (2/3)
AGM contraction postulates adapted for monotonic logics
[Flo06, FPA05, FPA06]
Includes all LOD formalisms
But: no satisfying contraction operator exists for many such logics
Cannot find a proper result in certain cases
Necessary and sufficient conditions for the existence of such an
operator [FPA06, Flo06]
Negative results for RDF/S, OWL, most DLs [FPA05, RWFA13]
Problem stems from the postulate of recovery [AGM85]
Captures the Principle of Minimal Change
Controversial [Han91]
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Postulation Approaches in Evolution (3/3)
Replacing recovery with optimal recovery [FPA06, FHP+06]
Equivalent to recovery for classical logics
But weaker in general
Not particularly successful either
Replacing recovery with relevance [Han91]
An intuitive, well-established alternative to recovery
Equivalent with recovery for classical logics
Applicable under quite general conditions [RWFA13]
—Applicable for all compact logics
—Includes RDF/S, practically all DLs and OWL flavors and profiles
Adequate for expressing the principles of contraction in LOD
languages
Connections with recovery established for non-classical logics
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Principle of Adequacy of Representation
Principle of Adequacy of Representation
The evolution result should be expressible in the same formalism
as the original dataset
Obvious and trivial
Not always compatible with our requirements for the evolution
result
Postulates (e.g., AGM postulates)
Specific incarnations of the Principle of Minimal Change
Specific computational methods or classes of operators
Two stages for the computation [CGKZ12]
Find the “optimal” evolution result according to the requirements
Express it in the target language (not always possible)
—Inexpressibility results
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Inexpressibility for Classes of Operators
Generic contraction methods [CGKZ12]
Syntactic: remove a minimal set of explicit axioms
Formula-based: remove a minimal set of axioms from the closure
—Three different semantics for minimality
Model-based: modify the model in a minimal manner
—Eight different methods to find the “minimal” distance between models
Existing contraction algorithms can be categorized along these generic
classes of methods
Different contraction methods not compatible in general (for DLs)
Model-based and formula-based are compatible in classical logics
Inexpressibility results for DL-Lite, EL (i.e., OWL2 QL, OWL2 EL)
[CGKZ12]
Proposal: a “hybrid” operator combining ideas from syntactic and
formula-based approaches [CGKZ12]
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More Inexpressibility Results
DL-Lite evolution [CKNZ10]
Focusing on model-based and formula-based approaches for contraction
Inexpressibility results
Propose a formula-based approach
DL revision [LLMW06]
Model-based approach, limited to Abox only (data level)
Inexpressibility results
Propose a new DL that supports model-based evolution
Approximations
DL-LiteF [GLPR07, GLPR09]
—Update and erasure approximation algorithms for data-level changes only
—Alternative: extend DL-LiteF to make sure that result is expressible
DL-Lite [WWT10]
—Provide postulates and approximation algorithms for revision
Giorgos Flouris
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Other Approaches
Evolution using ideas from argumentation frameworks [MRF08]
ALC DL
Inconsistency in a dataset is an “attack” between arguments
Acceptability semantics used to resolve such attacks and eliminate
inconsistencies
Useful for both debugging and evolution
Evolution can be reduced to debugging/repair [HHH+05]
Apply the change
Then repair the result to resolve problems (Principle of Validity)
—Making sure the change is not “undone” during repair (Principle of Success)
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Evolution Under Custom Constraints
Evolution in the presence of custom validity constraints [KFAC07,
FKAC13]
Methodology
Apply the change (Principle of Success)
Guarantee satisfaction of constraints (Principle of Validity)
Use a preference to determine minimality (Principle of Minimal Change)
Features
Generic method, applied for RDF/S evolution
A formal expression of the principles for the proposed setting
Exhaustive method to determine all possible side-effects and identify the
“best” (according to the preference)
Constrain allowed preferences for rationality and performance
Based on similar ideas as the repairing approach of [RFC11]
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Summary and Conclusions: Evolution
The problem of evolution is very challenging
Several issues need to be considered
—Not obvious to a newcomer
—Often ignored
Evolution approaches
Direct: manual, based on fixed operators, declarative
Indirect: postulation attempts
Adapted: adapting belief change algorithms or methods
Other possible directions (related to LOD)
Adapt for the “linked” character of LOD
—Evolution during propagation or after change detection
—Extra knowledge that can be exploited for adapting preferences, fine-tuning
of automated algorithms etc
Giorgos Flouris
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Thank You!
Giorgos Flouris
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