Social Emergent Semantics for Personal Data Management

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Abstract. In order use our personal data within our day to day activities, we need to manage it in a way that is easy to consume, which currently is not an easy task. People have found their own ways to organize their personal data, such as categorizing files in folders, labeling emails etc. This is acceptable to a certain degree, since we have to deal with have some (human) difficulties such as our limited capacity of categorization and our incapacity of maintaining highly structured artifacts for long periods of time. We believe that to organize this great amount of personal data, we need the help of our communities. In this work, we apply the emergent semantics field to personal data management, aiming to decrease our cognitive efforts spent in simple tasks, handling semantic evolution in conjunction with our close peers.

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Social Emergent Semantics for Personal Data Management

  1. 1. Social Emergent Semanticsfor Personal Data Management Cristian Vasquez ( cvasquez[at]vub.ac.be ) Semantics Technology and Applications Research Lab Vrije Universiteit Brussel
  2. 2. Agenda:● Motivation ● Personal Data management ● Use case● Shared Ontology Views● Blackboard anatomy● Experiment dynamics● Summary
  3. 3. Motivation Use case Lets suppose.... … that in a far away country... A bar that is frequently visited by sailors... And they exchange experiences...
  4. 4. Motivation Use case These sailors enjoy talking about: Practical things: And not so practical things: ● Geographical information ● Histories about their trips ● Journey advice ● Gossip ● Weather ● Big sea monsters ● hazards... ● Phantom ships ● Mermaids...
  5. 5. Motivation Use case These sailors would like to share information such as ● Maps ● Drawings ● Travel logs etc Which are useful to their community of sailors.
  6. 6. Motivation Lets suppose that.... They count with: Advanced technological devices, And they use them to record and store movies,photographs, sound, geographical information etc. On all their journeys.
  7. 7. Motivation These sailors would like to share information with other sailors. The problem: ● Every sailor has its own way of organizing its information ● Its already difficult for them to find their own information... since the volume is huge. ● Data is not well structured
  8. 8. Motivation Current solutions: To Attach pieces of information (structured or not) to other pieces of information, in order to find and manage them. Metadata Measurements, Models, (I.e: taxonomies) (I.e: coordinates) Written symbols (I.e: tags)
  9. 9. Motivation Sharing information is easier with the help of: ● Structured meta-data ● Artifacts that reflect our agreements (ontologies) ● But to come up with agreements, is already a difficult task.
  10. 10. Motivation Sharing information is easier with the help of: ● Structured meta-data ● Artifacts that reflect our agreements (ontologies) ● But to come up with agreements, is already a difficult task. ● Example: ● Tree of our sailors want to share the pictures and position of the mermaids that they have seen ● Sailor 1 (Greek) Mermaids appear in the folklore of many cultures including east, europe, china ● Sailor 2 (British isles) and india, they are usually considered dangerous, and are associated with ● Sailor 3 (Slavic) floods storms, shiprecks and drownings. However in other folk traditions, they can be benevolent and can fall in love with humans
  11. 11. Motivation Sailor 1 (greek): - These creatures are called seirines - They live in the sea - They are woman - They have beautiful and long hair. - They have enchanting voices
  12. 12. Motivation Sailor 1 (greek): - These creatures are called seirines - They live in the sea - They are woman - They have beautiful and long hair. - They have enchanting voices Sailor 2 (british isles): - These creatures are called mermaids - They live in the sea - They are woman - They can be giant - They dont have inmortal souls
  13. 13. Motivation Sailor 1 (greek): - These creatures are called seirines - They live in the sea - They are woman - They have beautiful and long hair. - They have enchanting voices Sailor 2 (british isles): - These creatures are called mermaids - They live in the sea - They are woman - They can be giant - They dont have inmortal souls For sailor 1 & 2, is direct to share artifacts about woman that live in the sea...
  14. 14. Motivation Sailor 1 (greek): - These creatures are called seirines - They live in the sea - They are woman - They have beautiful and long hair. - They have enchanting voices Sailor 2 (british isles): - These creatures are called mermaids - They live in the sea - They are woman - They can be giant - They dont have inmortal souls Sailor 3 (Slavic): - These creatures are called Rusalkas - They live in the sea - They are woman - They do not have a fish-like tail - They are beautiful young women with long green hair
  15. 15. Motivation Sailor 1 (greek): - These creatures are called seirines - They live in the sea - They are woman - They have beautiful and long hair. - They have enchanting voices - They DO have a fish-like tail Sailor 2 (british isles): - These creatures are called mermaids - They live in the sea - They are woman - They can be giant - They dont have inmortal souls - They DO have a fish-like tail Sailor 3 (Slavic): - These creatures are called Rusalkas Sailors learn gradually from the - They live in the sea conceptualizations of others..... - They are woman - They do not have a fish-like tail - They are beautiful young women with long green hair
  16. 16. Motivation ● To make agreements can be easier for some domains than for others. ● Example: can be easy for these sailors to agree about: ● System of coordinates for the islands. ● Weather conditions (distinct types of weather). ● Price of a good. ● But it may be difficult to come up with agreements about personal (custom) data. Example: How we can store, classify and annotate digital data about? ● Sailor 1 (Greek) seirines ● Sailor 2 (British isles) Mermaids ● Sailor 3 (Slavic):Rusalkas In order to share it?
  17. 17. Proposal: Blackboard networks ● Users interact through multiple canvas or blackboards, in order to build semantic bridges ● These networks are constructed incrementally, and organically. ● Network objective: To build and represent local agreements, collaboratively. Application seirines is a Mermaid Part of Tail Application
  18. 18. State of art ● Essential components: ● Semantic desktop (I.e [1] Nepomuk Framework) ● Personal Information Model (PIMO) a local ontology to annotate our personal data. [1] http://nepomuk.semanticdesktop.org/nepomuk/
  19. 19. State of art ● How to elicit custom ontologies? ● Ontology views An ontology view is not just a portion of a complete ontology. Rather is a collection of concepts and relationships that allows a unique representation by some participants of a certain domain. In the same way as ontologies, ontology views may be described using metadata representation languages such as RDF, RDFs and OWL among others. They evolve using change operators that allow coherent ontology view mutations.Mermaids Ontology Variant Sailor 1 seirines Sailor 2 Ontology Variant Shared Ontology ServiceElizabeth Chang, Tharam S Dillon, and Ling Feng. Modeling OntologyViews : An Abstract View Model for Semantic Web. Proceedings ofthe First International IFIP/WG12.5Working Conference on IndustrialApplications for Semantic Web (IASW), pages 227–246, 2005. Example of elicitation of local ontology
  20. 20. Ontology views in the Web: We want to describe our referents, to Be used by computers ● Structured descriptions ● Identified referents (observed subjects) Conceptualization Thought + Observer ●These 3 components cannot be separated! Referent (observed subject) Symbolsseirines
  21. 21. Ontology views in the Web + personal dataspaces Sailor 1(british) Shared Sailor 2(greek) Entity URI Ontology View Mermaids Ie: rdf schema seirines (terminology) (terminology) Sailor 2s Sailor 1s Perspective personal dataspace
  22. 22. Ontology views in the Web + personal dataspaces.How to manage them? Research proposal: Web blackboardsBlackboards can be seen as extensions of a semanticwiki web page, where participants collaborativelydescribe a subject using distinct description mechanismsand formalisms. A participant is allowed to subscribe tomultiple blackboards, contributing content in order toconverge into acceptable conceptualizations. Theblackboards collected by an user constitute a networkwhat he can bind directly with his own Personal data(extending his Personal Information Model)
  23. 23. Anatomy of a blackboard Blackboard as a playground ● Multiple of observers ● Multiple representation layers Conceptualization Conceptualization Thought + Observer A Web Observer B + Thought Blackboard (Public space) Observers Blackboards Metadata Referent (observed subject) Symbols Symbols Representation Layer
  24. 24. Anatomy of a blackboard Multi Layer Blackboard variant Example Conceptualization Conceptualization Thought + Observer A Web Observer B + Thought Blackboard (Public space) Observers Blackboards Language Metadata RDF (practical) Referent (observed subject) Measures (empirical) Semantic layer Controlled Vocabulary Empirical layer Models Natural (ontology) Language Pragmatical layer Observer B private space
  25. 25. Anatomy of a blackboard Multi Layer Blackboard variant Example Conceptualization Conceptualization Thought + Observer A Web Observer B + Thought Blackboard (Public space) Observers Blackboards Language Metadata RDF (practical) Referent (observed subject) Measures (empirical) Semantic layer Controlled Vocabulary Empirical layer Models Natural (ontology) Language Pragmatical layer Observer B private space Written symbols (I.e: tags)
  26. 26. Anatomy of a blackboard Multi Layer Blackboard variant Example Conceptualization Conceptualization Thought + Observer A Web Observer B + Thought Blackboard (Public space) Observers Blackboards Language Metadata RDF (practical) Referent (observed subject) Measures (empirical) Semantic layer Controlled Vocabulary Empirical layer Models Natural (ontology) Language Pragmatical layer Observer B private space Measurements, (I.e: coordinates)
  27. 27. Anatomy of a blackboard Multi Layer Blackboard variant Example Conceptualization Conceptualization Thought + Observer A Web Observer B + Thought Blackboard (Public space) Observers Blackboards Language Metadata RDF (practical) Referent (observed subject) Measures (empirical) Semantic layer Controlled Vocabulary Empirical layer Models Natural (ontology) Language Pragmatical layer Observer B private space Models, (I.e: taxonomies)
  28. 28. Anatomy of a blackboard Multi Layer Blackboard variant Example Conceptualization Conceptualization Thought + Observer A Web Observer B + Thought Blackboard (Public space) Observers Blackboards Language Metadata RDF (practical) Referent (observed subject) Measures (empirical) Semantic layer Controlled Vocabulary Empirical layer Models Natural (ontology) Language Pragmatical layer Observer B private space
  29. 29. Anatomy of a blackboard Blackboards as a network ● Relations to other blackboards (links) ● Wiki paradigm variant Conceptualization Thought + Observer A Web Web Blackboard Blackboard (Public space) (Public space) Observers Observers Blackboards Is related to Blackboards Metadata Metadata Referent (observed subject) Referent (observed subject) Symbols Representation Representation Layer Layer ● Sailor 1 (Greek) seirines ● Sailor 3 (Slavic):Rusalkas ● Sailor 2 (British isles) Mermaids With tail Without tail
  30. 30. Anatomy of a blackboard Blackboard networks • Users can relate blackboards using relationships such as causality, location function etc. forming a network. Pattern analysis is used then to provide feedback to the communities, increasing their awareness. • During the interplay within a blackboard, there will be cases where some participants disagree with others regarding some representation. Thus agreement mechanisms can be used in order to reach convergence. • If the distinct participants views become irreconcilable, then the blackboard itself may diverge into distinct variants, intended to capture distinct semantics.
  31. 31. Anatomy of a blackboard Blackboard networks ● user constructs a perspective via selecting distinct blackboard variants ● are decentralized ● are constructed incrementally in an organic way (emerging) Application seirines is a Mermaid Part of Tail Application
  32. 32. Anatomy of a blackboard Blackboard networks ● Since one user only have a partial view of the blackboard network, ● We need mechanisms to promote awareness ● One possibility is pattern recognition Application “Is-a” relationship cycle is a is a is a Application
  33. 33. Anatomy of a blackboard Blackboard networks ● Since one user only have a partial view of the blackboard network, ● We need mechanisms to promote awareness ● One possibility is pattern recognition Application Part of Part of Part of Application “part of” Relationship pattern
  34. 34. Anatomy of a blackboard Application: An user augments their own Personal Information Model Ontology (PIMO) by means of binding their own concepts to the subjects described within the blackboards User context Application Application
  35. 35. Anatomy of a blackboard User context Application Application: An user links elements from Linked Open Data to their own view of blackboards, creating bridges to query for example using local terminology. Application LOD cloud
  36. 36. Blackboard dynamics Web Blackboard (Public space) Observers Blackboards Metadata Referent (observed subject) Semantic layer Empirical layer Pragmatical layer
  37. 37. Blackboard dynamics Delta based versioning Web V1 V2 V3 Snapshot Blackboard (Public space) Observers 1 2 Blackboards Metadata 1 2 3 4 Referent (observed subject) Semantic layer 1 2 Empirical layer 1 Pragmatical layer 1
  38. 38. Blackboard dynamicsSnapshot based versioningAll the layers are versioned together forming a snapshot that is identified as a whole (With an URI). Web V1 V2 V3 Snapshot Blackboard (Public space) Observers O O O O 1 1 2 2 Blackboards M M M M Metadata 1 2 3 4 Referent (observed subject) Semantic layer S S S S 0 1 1 2 Empirical layer E E E E 0 0 1 1 Pragmatical layer P P P P 1 1 1 1
  39. 39. Blackboard dynamicsSnapshot based versioning Users interacts selecting some blackboards and pulling them to their local spaces, where they can augment or use the blackboards. Sailors Local Sailors Staging Blackboard if they make contributions then Working space area clone they have to push them through multiple stages. Web V1 V2 V3 Snapshot Blackboard (Public space) Observers O O O O 1 1 2 2 Blackboards M M M M Metadata 1 2 3 4 Referent (observed subject) Semantic layer S S S S 0 1 1 2 Empirical layer E E E E 0 0 1 1 Pragmatical layer P P P P 1 1 1 1
  40. 40. Blackboard dynamicsSnapshot based versioning Expect consistency & Users interacts selecting some A draft space or some degree of blackboards and pulling them to playground with agreement the no constraints local community their local spaces, where they can augment or use the blackboards. Sailors Local Sailors Staging Blackboard if they make contributions then Working space area clone they have to push them through multiple stages. Web V1 V2 V3 Snapshot Blackboard (Public space) Observers O O O O 1 1 2 2 Blackboards M M M M Metadata 1 2 3 4 Referent (observed subject) Semantic layer S S S S 0 1 1 2 Empirical layer E E E E 0 0 1 1 Pragmatical layer P P P P 1 1 1 1
  41. 41. Blackboard dynamics Example: ● Managing inconsistency Mermaid Web Blackboard ● Sailor 1 (Greek) Seirines Observers ● Sailor 2 (British isles) Mermaids Blackboards Metadata Referent (observed subject) Semantic layer ● They live in the sea Empirical layer ● They are woman Pragmatical layer
  42. 42. Blackboard dynamics Example: ● Managing inconsistency Mermaid ● Sailor 1 (Greek) Seirines Web Blackboard ● Sailor 2 (British isles) Mermaids ● Sailor 3 (Slavic):Rusalkas Observers - They DO have a fish-like tail Blackboards Metadata Variant B Referent (observed subject) 0 Semantic layer Empirical layer Variant A 0 Pragmatical layer - They do NOT have a fish-like tail
  43. 43. Blackboard dynamics Example: ● Managing inconsistency Mermaid ● Sailor 1 (Greek) Seirines Web Blackboard ● Sailor 2 (British isles) Mermaids ● Sailor 3 (Slavic):Rusalkas Observers Blackboards Metadata Variant Why divergence is useful? B Referent (observed subject) 0 ● Irreconcilable world views ● Practical reasons Semantic layer ● (I.e distinct degrees of complexity needed) Empirical layer Variant A 0 Pragmatical layer Sometimes we dont want global Interoperability Our scope is our community.
  44. 44. Blackboard dynamics Root Web Blackboard Variants mutate Observers independently Blackboards Metadata Variant Variant B B Referent (observed subject) 0 1 Semantic layer Empirical layer Variant Variant A A 0 0 Pragmatical layer
  45. 45. Blackboard dynamics Root Web Blackboard Observers Blackboards Metadata Variant Variant Variant B B B Referent (observed subject) 0 1 1 Semantic layer Empirical layer Variant Variant Variant A A A 0 0 1 Pragmatical layer
  46. 46. Blackboard dynamics Convergence example: Root Web ● Sailor 1 (Greek) Seirines Blackboard ● Sailor 2 (British isles) Mermaids ● Sailor 3 (Slavic):Rusalkas Observers Blackboards Metadata Variant Variant Variant Variant B B B B Referent (observed subject) 0 1 1 1 Semantic layer Variant - MAY have a fish-like tail C 0 Empirical layer Variant Variant Variant Variant A A A A 0 0 1 1 Pragmatical layer How can we support convergence? B3 With computer aided support: ● B4 B2 ● I.E: Relationship pattern recognition ● Seirines & Mermaids very similar to Rusalkas B1 → suggest MAY have a fish-like tail
  47. 47. Blackboard dynamics Service layer example: Why versioning and Root convergence is useful? Web Blackboard ●Its easier to construct and Observers maintain services Blackboards Metadata Variant Variant Variant Variant B B B B Referent (observed subject) 0 1 1 1 Variant Semantic layer C 0 Empirical layer Variant Variant Variant Variant A A A A 0 0 1 1 Pragmatical layer Service layer Services Services 0 1
  48. 48. The experiment
  49. 49. The experimentNepomuk Framework to ● Local metadata-extraction ● PIMO management
  50. 50. The experimentNepomuk Framework to ● Local metadata-extraction ● PIMO management Semantic media Wiki + iMapping ● Blackboard description interface (This is under evaluation)
  51. 51. The experimentNepomuk Framework to ● Local metadata-extraction ● PIMO management JGIT Semantic media Wiki + iMapping ● Dataspace versioning ● Blackboard description interface ●Convergence and divergence capability (This is under evaluation)
  52. 52. The experiment RDF as representation model ● Fundamental glue to put all the pieces together ● Straightforward possibility to use the Web as publishing and distribution mechanism.
  53. 53. Summary• This framework explores notions such as personal context andemergent semantics, making use of artifacts such as blackboards that candiverge and converge in order to support meaning evolution, in order toimprove our personal data management capabilities.• In this work we dont aim to distill global semantics. Instead we wantour own semantics, taking as hypothesis that they are incrementallyconstructed by our close communities.
  54. 54. Questions?
  55. 55. Blackboard network traceability, Things to look at: ● Concept Emergence - Removal ● Concept abstraction - Specialization ● Semantic Distance ( Hops between concepts ) ● Concept resistance and speed of change.C4 C5 C3 C2 C1
  56. 56. Example: Proselytizing Indicator that counts how concepts are propagated transversally through two branchesC4 C5 B3 B4 C3 C2 B2 C1 B1
  57. 57. First prototype

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