Design

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DESIGNR CHAWUTHAI

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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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COMMUNITY ENTITY

Spaces

Time intervalsContextual Knowledge

Community Knowledge

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Spaces

Contextual Knowledge

TIME

Community Knowledge

tl:Interval period

xsd:dateTime

tl:beginAtDateTime tl:endAtDateTime

contain

prefix tl http://purl.org/NET/c4dm/timeline.owl#

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SPACE

Community Knowledge

tl:Interval period

xsd:dateTime


contain

foaf:Groupsoic:Community

perceivedBy


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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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Provenance

COMMUNITY ENTITY

Spaces

Time intervals


Community Knowledge

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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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PROVENANCE

Community Knowledge

tl:Interval period

xsd:dateTime


contain

foaf:Group,soic:Community

perceivedBy


foaf:Agent

bibo:perform

er

Book, thing

reference

report

er

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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.


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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


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KNOWLEDGE EVOLUTION

Community Knowledge

tl:Interval period

xsd:dateTime


contain

foaf:Group,soic:Community

perceivedBy

foaf:Agent

bibo:perform

er

Book, thing

reference

report

er

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


A Taxonomy of Part-Whole Relationshttp://csjarchive.cogsci.rpi.edu/1987v11/i04/p0417p0444/MAIN.PDF

http://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;


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EVOLUTION TYPEC

once

pt

ConceptName

AltName

Taxonomy

SpecializationBroader

Narrower

Partition PartOf

Association MemberOf

DRAFT

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EVOLUTION TYPEEv

olut

ion

Type

Concept

CreateDelete

ReplaceSimilar

SplitMerge

Taxonomy

SpecializationDerive

UnderiveChangeClass

PartitionBelongApart

ChangePartAssociation

JoinDetachMove

DRAFT

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SCENARIO 11800 - 1900TH[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 ………… … … .. .. … .

1805THx:newyear

2010US

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SCENARIO 11. 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 QUERY1. 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 Global4. Check all similar concept from Global with reader’s UCK5. Scan change of concept of reader’s UCK (as 1)

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Q1. Interpret requires ____, ____, _____, ____, and ____.2. Inference?3. Complex scenario?4. Example query?5. Information model attached with document?

Design

Documents

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