1 / 32

Design

Design. R Chawuthai. Community Knowledge.

dillon-mcconnell
Télécharger la présentation

Design

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Design R Chawuthai

  2. Community Knowledge • information preservation, refers to the ability to understand the rendered object at any time, i.e., to be able to understand its content by understanding the terms, concepts or other information that appears in it, by placing it in its correct context • Another important observation that can be made here is that the need for preservation can appear in both space and time dimensions. • The “space” dimension refers to the fact that different people have different background knowledge and, consequently, may have trouble understanding each other’s documents (e.g., an astronomer may have trouble understanding a scientific paper on computer software). • Similarly, the “time” dimension refers to the fact that different people in different times Terminology and Wish List for a Formal Theory of Preservation

  3. Community Entity Time intervals Contextual Knowledge Community Knowledge Spaces

  4. Time prefixtlhttp://purl.org/NET/c4dm/timeline.owl# Contextual Knowledge xsd:dateTime tl:endAtDateTime tl:beginAtDateTime tl:Interval interval Community Knowledge Spaces

  5. Space prefixtlhttp://purl.org/NET/c4dm/timeline.owl# Contextual Knowledge xsd:dateTime tl:endAtDateTime tl:beginAtDateTime tl:Interval interval Community Knowledge sharedBy soic:Community

  6. Provenance • A contextmaybe a rich object that hasdescriptions about its properties (such as provenances) and relations to other contexts. • the provenanceof a context, including the aspects of temporal (when), spatial (where), agent (who), casual (why) and other properties. http://www.cs.rpi.edu/~baojie/pub/2010-03-25_context_websci.pdf Context Representation for the Semantic Web

  7. Community Entity Contextual Knowledge Time intervals Community Knowledge Spaces Provenance

  8. Reference • Barwise and Seligman (1992) use natural regularities to study the role of context in categorization. An example regularity from Seligman (1993) is, “Swans are white.” This is atypical natural regularity in the sense that it is both reliable and fallible. Natural regularities are reliable because they are needed to explain successful representation, knowledge, truth, and correct reference. They are fallible because they are needed to account for misinterpretation, error, false statements, and defeasible reference . Steps toward Formalizing Context VarolAkman and Mehmet Surav http://www.cs.bilkent.edu.tr/~akman/jour-papers/aimag/aimag1996.pdf

  9. Provenance prefixtlhttp://purl.org/NET/c4dm/timeline.owl# Contextual Knowledge xsd:dateTime contain tl:endAtDateTime tl:beginAtDateTime tl:Interval period Community Knowledge perceivedBy foaf:Group, soic:Community bibo:performer reference reporter Book, thing foaf:Agent

  10. Community ( as a Context) Knowledge • In order to achieve the above goal, we need to encode the dynamics of languages, that is to be able to describe (formally) the differences between the producer’s and consumer’s UCKs. This “delta” could represent the evolution of knowledge over time, or it could represent the differences in understanding and terminology between two people with different backgrounds. Terminology and Wish List for a Formal Theory of Preservation

  11. Community ( as a Context) Knowledge • The extension of concept mapping into full conceptual knowledge structures to facilitate collaborative knowledge evolution. Towards Virtual Community Knowledge Evolution ***

  12. Community ( as a Context) Knowledge • a Knowledge Representation point of view RDF statements in general are context-free, and thus follow a notion of universal truth, while documents contain context sensitive information i.e., information whose interpretation depends on the context in which the document is written. This way to proceed can easily generate contradictory statements such that for instance “Silvio Berlusconi is the Prime minister of Italy” and “Romano Prodi is the Prime minister of Italy” as the result of articles written at different point in time. • A statement is true only under a certain set of conditions, which will help us store information in the KB that would cause contradictions or inconsistencies in a plain RDF A-Box. Introducing Context into RDF Knowledge Bases⋆ http://ceur-ws.org/Vol-166/70.pdf

  13. Community ( as a Context) Knowledge • information preservation, refers to the ability to understand the rendered object at any time, i.e., to be able to understand its content by understanding the terms, concepts or other information that appears in it, by placing it in its correct context Terminology and Wish List for a Formal Theory of Preservation

  14. Knowledge Evolution Contextual Knowledge xsd:dateTime Knowledge Evolution contain tl:endAtDateTime tl:beginAtDateTime assure tl:Interval period Community Knowledge perceivedBy foaf:Group, soic:Community bibo:performer reference reporter Book, thing foaf:Agent

  15. Knowledge Evolution denyConcept Concept Evolution rdfs:Resource Type assertConcept is a evoluationType denyStatement is a Knowledge Evolution Statement Evolution rdf:Statement assertStatement assure Community Knowledge T P S

  16. Adding Named Graph • In this paper we present a syntax and storage format based on named graphs to express temporal RDF • The temporal RDF approach vastly reduces the number of triples by eliminating redundancies resulting in an increased performance for processing and querying • Each time interval is represented by exactly one named graph, where all triplesbelonging to this graph share the same validity period • To express the same amount of information, reification would consume 15 times more triples than named graphs seriously questioning the scalabilityboth in terms of storage space and, more importantly, in terms of retrieval run-time. Applied Temporal RDF: Efficient Temporal Querying of RDF Data with SPARQL http://www.ifi.uzh.ch/pax/uploads/pdf/publication/1004/tappolet09applied.pdf

  17. Knowledge Evolution denyConcept Concept Evolution rdfs:Resource Type assertConcept is a evoluationType denyStatement is a Knowledge Evolution Statement Evolution rdf:Statement assertStatement is a assure denyGraph Graph Evolution sd:NamedGraph Community Knowledge assertGraph T P S prefix sd: <http://www.w3.org/ns/sparql-service-description#>

  18. Conceptualization • The conceptualizationis the couching of knowledge about the worldin termsof entities(things, the relationshipsthey hold and the constraints between them). The specification is the concrete representation of this conceptualization. • A conceptrepresents a set or classof entities or ‘things’ within a domain Ontology-based knowledge representation for bioinformatics

  19. Conceptualization • Relationsdescribe the interactions between concepts or a concept’s properties. Relations also fall into two broad kinds: • Taxonomiesthat organize concepts into sub-super-concept tree structures. The most common are • Specialization relationship • Partitiverelationship • Associativerelationships that relate concepts across tree structures. Commonly found examples include • Component/Objectrelationship • Member/Collection relationship • Portion/Mass relationship • Stuff/Object relationship • Feature/Activity relationship • Place/Area relationship A Taxonomy of Part-Whole Relations http://csjarchive.cogsci.rpi.edu/1987v11/i04/p0417p0444/MAIN.PDF Ontology-based knowledge representation for bioinformatics

  20. Conceptualization • Our contributions are the following: • (i) we consider a conceptual model enhanced with the notion of a lifetimeof a class, individual and relationship, • (ii) we further extend the temporal model [9] with consistency conditions and additional constructs to model merges, splits, and other forms (joins, detaches, becomes) of evolution among class-level and instance-level concepts; Ontology-based knowledge representation for bioinformatics

  21. Evolution Type DRAFT

  22. Evolution Type DRAFT

  23. Scenario 1 1800 - 1900 1950 – … TH US [type=create] <assert> x:newyear :date ”13Apr”. <assert> y:newyear :date “1Jan”. 1900 - … Thai 1950- … [type=replace] <deny> x:newyear :date “13Apr”. <assert> x:newyear :date “1Jan”. Global [type=similar] <assert> x:newyear :sameAs y:newyear .

  24. Scenario 1 2010 US … ……. play water together on new year day ………… … … .. .. … . 1805 TH x:newyear

  25. Scenario 1 • Compare TH-1805 with TH-2010 • x:newyearTH-1805:date “13Apr”deny • x:newyearTH-2010 :date “1Jan”assert • Compare TH-2010 with Global-2010 • x:newyearTH-2010 = y:newyearGlobal-2010 • Compare Global-2010 with US-2010 • y:newyearGlobal-2010= y:newyearUS-2010 Since 1900, Thai new year day “1 Jan” has replaced “13 Apr”. Currently, Thai new year is same as US new year.

  26. How to query • Scan change of concept of writer’s UCK from concept’s date until now • Change of object with same concept (as subj) and predicate. • Change of subject with same predicate and concept (as obj) . • Change of predicate with same concept (as subj) and object. • Change of predicate with same subject and concept (as obj). • Scan change for all similar concept (as 1) • Check all concept and similar concept with Global • Check all similar concept from Global with reader’s UCK • Scan change of concept of reader’s UCK (as 1)

  27. Q • Interpret requires ____, ____, _____, ____, and ____. • Inference? • Complex scenario? • Example query? • Information model attached with document?

  28. A Logical Model Of Digital Archives Genus “Babu” and Genus “Nyctea” merged into name “Babu” … result to … Name “BabuScandaicus” replaces name “NycteaScandaica” … result to … Species “BabuScandaicus” is synonym of “NycteaScandaica”

  29. A Logical Model Of Digital Archives Species “BabuScandaicus” is synonym of “NycteaScandaica” … because … Name “BabuScandaicus” replaces name “NycteaScandaica” … because … Genus “Babu” and Genus “Nyctea” merged into name “Babu”

  30. A Logical Model Of Digital Archives Genus “Babu” and Genus “Nyctea” merged into name “Babu” Name “BabuScandaicus” replaces name “NycteaScandaica” :assure :assure Species “BabuScandaicus” is synonym of “NycteaScandaica” Year 1999 tl:beginAtDateTime :assure ex:wink :interval bibo:performer ex:ctx1 (Community Knowledge) ex:heidrich bibo:performer :sharedBy ex:richard_c bibo:issuer ex:NII dcterms:source <paper-isbn-123456>

  31. A Logical Model Of Digital Archives Type: MergeConcept  Type: DescribeConcept :Nyctea :follows   :Babu :Babu1 :type :Genus   :Babu1 :name “Babu” :Babu1 :isCausedBy Type: DefineSynonym  :Babu1 :sameAs :Nyctea :isCausedBy  :Babu1 :sameAs:Babu Genus Type: DescribeConcept Type: ReplaceName follows   :Babu_scandiacus :type :Species :Nyctea_scandiaca   :Babu_scandiacus :genus :Babu1 :Babu_scandiacus Type: DefineSynonym  :Babu_scandiacus :sameAs :Nyctea_scandiaca :isCausedBy Species

  32. A Logical Model Of Digital Archives :denyConcept :EvolutionType :Concept Evolution rdfs:Resource :evolutionType :assertConcept is a :causeOf ? :associate :denyStatement :Knowledge Evolution is a :Statement Evolution rdf:Statement :partOf :assertStatement is a :assure xsd:dateTime :denyGraph :Graph Evolution sd:NamedGraph :assertGraph tl:endAtDateTime tl:beginAtDateTime :interval tl:Interval dcterms:source :Community Knowledge Thing bibo:issuer bibo:performer :sharedBy foaf:Agent soic:Community

More Related