W3C Invited Talk 16/09/2009Giorgos Flouris1 High-Level Change Detection in the Semantic Web Institute of Computer Science Foundation for Research and Technology – Hellas Heraklion, Greece Giorgos Flouris Joint work with: Vicky Papavassiliou, Irini Fundulaki, Dimitris Kotzinos, Vassilis Christophides

W3C Invited Talk 16/09/2009Giorgos Flouris2 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

W3C Invited Talk 16/09/2009Giorgos Flouris3 Problems with 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

W3C Invited Talk 16/09/2009Giorgos Flouris4 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  [Semantic Web] is a collaborative effort led by W3C with participation from a large number of researchers and industrial partners

W3C Invited Talk 16/09/2009Giorgos Flouris5 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, …)

W3C Invited Talk 16/09/2009Giorgos Flouris6 Ontologies  Backbone of the Semantic Web  Ontologies allow the description of data  Annotation and metadata regarding web pages  Terminological 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 terms  Formal vocabulary for information exchange (for humans and machines)

W3C Invited Talk 16/09/2009Giorgos Flouris7 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

W3C Invited Talk 16/09/2009Giorgos Flouris8 Visualization, Triples, Serialization Period Actor Event Onset Existing 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_Birth Giorgos participants VisualizationTriple RepresentationSerialization (RDF/XML) instantiation subsumption

W3C Invited Talk 16/09/2009Giorgos Flouris9 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)

W3C Invited Talk 16/09/2009Giorgos Flouris10 What is Change? Ontology Real World Ontology Evolution Algorithm Delete_Class(…) Pull_Up_Class(…) Rename_Class(…) …

W3C Invited Talk 16/09/2009Giorgos Flouris11 What is Change Detection? Ontology Real World Delete_Class(…) Pull_Up_Class(…) Rename_Class(…) … Change Detection Algorithm

W3C Invited Talk 16/09/2009Giorgos Flouris12 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 differences  Efficient storage and/or communication  Evolution history  Record changes as they happen (manual or automatic)  Error-prone, difficult (often impossible) V1V1 V2V2 V3V3 V4V4 V5V5 C1C1 C2C2 C3C3 C4C4

W3C Invited Talk 16/09/2009Giorgos Flouris13 Sample Evolution Persistent Event Onset Birth Stuff Actor started_on participants Version 1 (V 1 ) Version 2 (V 2 ) Period Actor Event Onset Existing Stuff Birth started_on participants G_Birth Giorgos participants instantiation subsumption instantiation subsumption G_Birth Giorgos participants Evolution

W3C Invited Talk 16/09/2009Giorgos Flouris14 Analyzing the Evolution (Using Triples)  Triples in V 1 (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 V 2 (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] …

W3C Invited Talk 16/09/2009Giorgos Flouris15 Low-Level Delta  Triples in V 2 but not in V 1 (added triples) [Event domain participants] [Persistent type Class] [Stuff subclass Persistent] [started_on domain Persistent] [Birth subclass Event]  Triples in V 1 but not in V 2 (deleted triples) [Period type Class] [Event subclass Period] [participants domain Onset] [Existing type Class] [Stuff subclass Existing] [started_on domain Existing] [Birth subclass Onset] Low-Level Delta Add([Event domain participants]) Add([Persistent type Class]) … Del([Period type Class]) …

W3C Invited Talk 16/09/2009Giorgos Flouris16 Analyzing the Evolution (Visually) Persistent Event Onset Birth Stuff Actor started_on participants Version 1 (V 1 ) Version 2 (V 2 ) Period Actor Event Onset Existing Stuff Birth started_on participants G_Birth Giorgos participants instantiation subsumption G_Birth Giorgos participants Evolution 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)

W3C Invited Talk 16/09/2009Giorgos Flouris17 Comparing the Deltas Persistent Event Onset Birth Stuff Actor started_on participants Version 1 (V 1 ) Version 2 (V 2 ) Period Actor Event Onset Existing Stuff Birth started_on participants G_Birth Giorgos participants instantiation subsumption G_Birth Giorgos participants 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 deltaHigh-level delta Del([Period type Class]) Del([Event subclass Period]) Delete_Class (Period,Ø,{Event},Ø,Ø,Ø,Ø)

W3C Invited Talk 16/09/2009Giorgos Flouris18 Associations (Partitioning) Low-Level ChangesAssociated 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])

W3C Invited Talk 16/09/2009Giorgos Flouris19 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

W3C Invited Talk 16/09/2009Giorgos Flouris20 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-level  Must 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 changes  Must be coarse-grained enough to be concise

W3C Invited Talk 16/09/2009Giorgos Flouris21 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 V 1 and/or V 2  Generalize_Domain(P, X, Y)  Del([P domain X])  Add([P domain Y])  P existing property in both V 1, V 2  X, Y existing classes in both V 1, V 2  X subclass of Y in both V 1, V 2  Generalize_Domain(participants, Onset, Event): detectable  Similarly for the other changes in L (about 120 in total)

W3C Invited Talk 16/09/2009Giorgos Flouris22 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  Problems with determinism  One evolution could correspond to different sets of basic/composite changes  Priorities in detection  Heuristic  Composite  Basic

W3C Invited Talk 16/09/2009Giorgos Flouris23 Results on L: Types of Changes Changes Low-LevelHigh-Level BasicCompositeHeuristic Add Del Delete_Subclass Delete_Domain Pull_Up_Class Change_Domain Rename_Class Split_Class

W3C Invited Talk 16/09/2009Giorgos Flouris24 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 (V 1, V 2 ) 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 (V 1, V 2 ) irresolvable

W3C Invited Talk 16/09/2009Giorgos Flouris25 Results on L: Application Persistent Event Onset Birth Stuff Actor started_on participants Version 1 (V 1 ) Version 2 (V 2 ) Period Actor Event Onset Existing Stuff Birth started_on participants G_Birth Giorgos participants G_Birth Giorgos participants Detect C Apply C Apply C -1

W3C Invited Talk 16/09/2009Giorgos Flouris26 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 V1V1 V2V2 V3V3 V4V4 V5V5 C1C1 C2C2 C3C3 C4C4

W3C Invited Talk 16/09/2009Giorgos Flouris27 Calculate Low-Level Delta Detection Algorithm for L (1/2) Triples in V 1 (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 V 2 (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 is matched with Compute Heuristic Changes Heuristic Changes Rename_Class(Existing, Persistent)

W3C Invited Talk 16/09/2009Giorgos Flouris28 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) Generalize_Domain(participants, Onset, Event) Detection Algorithm for L (2/2) Triples in V 1 (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 V 2 (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]) Find Associated Change Generalize_Domain(participants, Onset, Event) 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) Generalize_Domain(participants, Onset, Event) ? ? ?

W3C Invited Talk 16/09/2009Giorgos Flouris29 Find Associated Change Del([participants domain Onset]) Required in Low-Level DeltaPotentially 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) …… Operations Pull_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]

W3C Invited Talk 16/09/2009Giorgos Flouris30 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, …

W3C Invited Talk 16/09/2009Giorgos Flouris31 Performance  Complexity: O(max{N 1,N 2,N 2 })  Linear average-case  Highly dependent on the detected changes (type, number)

W3C Invited Talk 16/09/2009Giorgos Flouris32 Evaluation: Usefulness and Intuitiveness  L is well-defined (changes used in practice)  GO: add/delete class, comments changing  CIDOC: add/delete/rename properties  Results confirmed by literature/editor notes

W3C Invited Talk 16/09/2009Giorgos Flouris33 Evaluation: Conciseness  Basic ≈ Low-Level  Basic+Composite+Heuristic << Low-Level

W3C Invited Talk 16/09/2009Giorgos Flouris34 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: 13  Specialize_Domain: 1  Generalize_Range: 14  Specialize_Range: 1  Change_Range: 1

W3C Invited Talk 16/09/2009Giorgos Flouris35 Conclusion  High-level change detection  A posteriori detection (input: V 1, V 2 )  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

W3C Invited Talk 16/09/2009Giorgos Flouris36 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, 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