design
DESCRIPTION
Design. R Chawuthai. Community Knowledge. - PowerPoint PPT PresentationTRANSCRIPT
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COMMUNITY KNOWLEDGE1. 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
2. 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
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Spaces
Contextual Knowledge
TIME
Community Knowledge
tl:Interval interval
xsd:dateTime
tl:beginAtDateTime tl:endAtDateTime
prefix tl http://purl.org/NET/c4dm/timeline.owl#
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Contextual Knowledge
SPACE
Community Knowledge
tl:Interval interval
xsd:dateTime
tl:beginAtDateTime tl:endAtDateTime
soic:Community
sharedBy
prefix tl http://purl.org/NET/c4dm/timeline.owl#
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PROVENANCE• A context maybe a rich object that has descriptions about its
properties (such as provenances) and relations to other contexts.
• the provenance of a context, including the aspects of temporal (when), spatial (where), agent (who), casual (why) and other properties.
Context Representation for the Semantic Web
http://www.cs.rpi.edu/~baojie/pub/2010-03-25_context_websci.pdf
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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 .
http://www.cs.bilkent.edu.tr/~akman/jour-papers/aimag/aimag1996.pdf
Steps toward Formalizing ContextVarol Akman and Mehmet Surav
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Contextual Knowledge
PROVENANCE
Community Knowledge
tl:Interval period
xsd:dateTime
tl:beginAtDateTime tl:endAtDateTime
contain
foaf:Group,soic:Community
perceivedBy
prefix tl http://purl.org/NET/c4dm/timeline.owl#
foaf:Agent
bibo:perform
er
Book, thing
reference
repo
rter
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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
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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 ***
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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 di erent point in time.ff
• 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 KnowledgeBases⋆
http://ceur-ws.org/Vol-166/70.pdf
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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
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Contextual Knowledge
KNOWLEDGE EVOLUTION
Community Knowledge
tl:Interval period
xsd:dateTime
tl:beginAtDateTime tl:endAtDateTime
contain
foaf:Group,soic:Community
perceivedBy
foaf:Agent
bibo:perform
er
Book, thing
reference
repo
rter
Knowledge Evolution
assure
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PST
KNOWLEDGE EVOLUTION
rdfs:Resource
assertConcept
denyConcept
Type
evoluationType
Concept Evolution
Statement Evolution
rdf:Statement
denyStatement
assertStatement
is a
is a
Community Knowledge
Knowledge Evolution
assure
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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 triples belonging 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 scalability both in terms of storage space and, more importantly, in terms of retrieval run-time.
Applied Temporal RDF: E cient TemporalffiQuerying of RDF Data with SPARQL
http://www.ifi.uzh.ch/pax/uploads/pdf/publication/1004/tappolet09applied.pdf
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PST
KNOWLEDGE EVOLUTION
rdfs:Resource
assertConcept
denyConcept
Type
evoluationType
Concept Evolution
Statement Evolution
rdf:Statement
denyStatement
assertStatement
is a
is a
Community Knowledge
Knowledge Evolution
assure
GraphEvolution
is a
sd:NamedGraph
denyGraph
assertGraph
prefix sd: <http://www.w3.org/ns/sparql-service-description#>
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CONCEPTUALIZATION• The conceptualization is the couching of knowledge about
the world in terms of entities (things, the relationships they hold and the constraints between them). The specification is the concrete representation of this conceptualization.
• A concept represents a set or class of entities or ‘things’ within a domain
Ontology-based knowledge representation for bioinformatics
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CONCEPTUALIZATION• Relations describe the interactions between concepts or a
concept’s properties. Relations also fall into two broad kinds:
• Taxonomies that organize concepts into sub-super-concept tree structures. The most common are
• Specialization relationship• Partitive relationship
• Associative relationships that relate concepts across tree structures. Commonly found examples include
• Component/Object relationship• Member/Collection relationship• Portion/Mass relationship• Stuff/Object relationship• Feature/Activity relationship• Place/Area relationship
Ontology-based knowledge representation for bioinformatics
A Taxonomy of Part-Whole Relationshttp://csjarchive.cogsci.rpi.edu/1987v11/i04/p0417p0444/MAIN.PDF
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CONCEPTUALIZATION• Our contributions are the following:
• (i) we consider a conceptual model enhanced with the notion of a lifetime of 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
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EVOLUTION TYPEC
on
cep
t
ConceptName
AltName
Taxonomy
SpecializationBroader
Narrower
Partition PartOf
Association MemberOf
DRAFT
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EVOLUTION TYPEE
vo
luti
on
Ty
pe
Concept
Create
Delete
Replace
Similar
Split
Merge
Taxonomy
Specialization
Derive
Underive
ChangeClass
Partition
Belong
Apart
ChangePart
Association
Join
Detach
Move
DRAFT
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SCENARIO 11800 - 1900
TH
[type=create]<assert>x:newyear :date ”13Apr”.
1950- …
Global
1950 – …
US
<assert>y:newyear :date “1Jan”.
[type=similar]<assert>x:newyear :sameAs y:newyear .
1900 - …
Thai
[type=replace]<deny> x:newyear :date “13Apr”.<assert> x:newyear :date “1Jan”.
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SCENARIO 1
… ……. play water together on new year day ………… … … .. .. … .
1805
TH
x:newyear
2010
US
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SCENARIO 1
1. Compare TH-1805 with TH-2010
• x:newyearTH-1805 :date “13Apr” deny
• x:newyearTH-2010 :date “1Jan” assert
2. Compare TH-2010 with Global-2010
• x:newyearTH-2010 = y:newyearGlobal-2010
3. 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.
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HOW TO QUERY
1. 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).
2. Scan change for all similar concept (as 1)
3. Check all concept and similar concept with Global
4. Check all similar concept from Global with reader’s UCK
5. Scan change of concept of reader’s UCK (as 1)
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Q
1. Interpret requires ____, ____, _____, ____, and ____.
2. Inference?
3. Complex scenario?
4. Example query?
5. Information model attached with document?
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A LOGICAL MODEL OF DIGITAL ARCHIVES
Genus “Babu” and Genus “Nyctea” merged into name “Babu”
Species “Babu Scandaicus” is synonym of “Nyctea Scandaica”
Name “Babu Scandaicus” replaces name “Nyctea Scandaica”
… result to …
… result to …
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A LOGICAL MODEL OF DIGITAL ARCHIVES
Genus “Babu” and Genus “Nyctea” merged into name “Babu”
Species “Babu Scandaicus” is synonym of “Nyctea Scandaica”
Name “Babu Scandaicus” replaces name “Nyctea Scandaica”
… because …
… because …
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A LOGICAL MODEL OF DIGITAL ARCHIVES
Genus “Babu” and Genus “Nyctea” merged into name “Babu”
Name “Babu Scandaicus” replaces name “Nyctea Scandaica”
Species “Babu Scandaicus” is synonym of “Nyctea Scandaica”
:assure
:assure
:assure
Year 1999
tl:beginAtDateTime
ex:ctx1 (CommunityKnowledge)
ex:NII
:interval
:sharedBy
ex:wink
ex:richard_c
<paper-isbn-123456>
ex:heidrichbibo:performer
dcterms:source
bibo:issuer
bibo:perform
er
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A LOGICAL MODEL OF DIGITAL ARCHIVES
:Nyctea
:Babu1
:Babu
Type: MergeConcept
:Babu1 :type :GenusType: DescribeConcept
:Babu1 :name “Babu”
:follows
:Babu1 :sameAs :NycteaType: DefineSynonym
:Babu1 :sameAs :Babu
:isCausedBy
:Babu_scandiacus :type :SpeciesType: DescribeConcept
:Babu_scandiacus :genus :Babu1
follows
:Babu_scandiacus :sameAs :Nyctea_scandiaca
Type: DefineSynonym
:isCausedBy
:Nyctea_scandiaca
:Babu_scandiacus
Type: ReplaceName
:isCausedBy
Genus
Species
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A LOGICAL MODEL OF DIGITAL ARCHIVES
:Community Knowledge
tl:Interval :interval
xsd:dateTime
tl:beginAtDateTime tl:endAtDateTime
soic:Community
:sharedBy
foaf:Agent
bibo:performer
Thingdcterms:source
bibo:issuer
rdfs:Resource
:assertConcept
:denyConcept:EvolutionType
:evolutionType
:Concept Evolution
:Statement Evolution
rdf:Statement
:denyStatement
:assertStatement
is a
is a
:Knowledge Evolution
:assure:Graph
Evolution
is a
sd:NamedGraph
:denyGraph
:assertGraph
:causeOf:associate
:partOf?