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Dynamically Synthesizing Systems of Systems
© Synsyta 2006. All Rights Reserved.
Synsyta and Neological Proprietary
Living Systems &Living, Liaising Languages
Key to Netcentric Interoperability
January 24, 2006
Erick Von SchweberCTO and Chief ArchitectSynsytaerick@synsyta.com
2© Synsyta 2006. All Rights Reserved.
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Riding the Waves
First WaveUniprocessing
Second WaveParallel & Distributed
Processing
IBMOn-Demand
Sun N1
HP AdaptiveEnterprise
BladeServersSMTA &
Multicore
WebGrids
SemanticWeb
SOA
P2P
Web 2.0
AJAX
RSS
MDA
Globus
Model DrivenSemantic GridWireless
MeshAd Hoc
Networks
WebServices
Third WaveComputing
Fabrics
RDFOWL
Living, Organic Architecture Systems
into EcosystemsCultural Co-Evolution
SymbiosisSynsytia Surveying
Living Liaising Languages
Semantic Index GridManaged Logic
ccNUMA clusters
Reconfigurable Logic
FPGAs
XML
The challenge of our eraTransforming clockwork mechanism into living
technology
3© Synsyta 2006. All Rights Reserved.
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Outline
• Riding the waves• Biologically and culturally inspired
interoperation• Foundations• Roadmap• Next steps
4© Synsyta 2006. All Rights Reserved.
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A Human+Machine culture ofLiving, Liaising Languages
5© Synsyta 2006. All Rights Reserved.
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Machine-Machine vs. Human–Human Communication
Machine-Machine Human–HumanThey are programmed in advance of interoperation
We learn each other’s lingos as we communicate
They require instructions We point to examples
Ambiguity simply does not compute
We use analogy, metaphor and conceptual blending
They stonewall We negotiate
They are static and rigid We self-adapt
Their languages change with periodic releases and revisions
Our languages are in constant flux
They must be reengineered We culturally evolve
6© Synsyta 2006. All Rights Reserved.
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RoadmapSecond Stage
Collaborative ontologies
andComposable languages
Third Stage
Living, liaising language hubs
First Stage
Automated interoperation of heterogeneous languages and
ontologies
Fourth Stage
Culturalco-evolution
& symbiosis of living, liaising
languages
Composable theories extend collaborative classifications
Increasing collaboration and generality
7© Synsyta 2006. All Rights Reserved.
Synsyta and Neological Proprietary
Foundations
• NeoLogical SURVEYOR, 1983 – present• Computing Fabrics, 1996 - present• “Software Services Grid” workshop, July 2001• “Model Driven Semantic Grid” GGF2, July 2001• “Information Representation & Transformation” for DARPA TIA
– “Model Driven Semantic Grid, and Beyond” workshop, Jan. 2003– “How the Stacks Stack Up” workshop, June 2003– “Integrated Semantics” workshop, August 2003
• “Managed Logic worked example” white paper 2004• “Roadmap for Semantics in netcentric Enterprise Architecture” white paper
for GSA, August 2005 - present
• Ken Baclawski• Desmond D’Souza• Dave Frankel• Elisa Kendal• Robert Kent
• Deb McGuinness• Sheila McIlraith• Jeff Smith• John Sowa
Cast of Characters(frequently appearing)
• Erick Von Schweber• Linda Von Schweber• Cory Casanave• Joseph Goguen
8© Synsyta 2006. All Rights Reserved.
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Surveying via unknown dimensions and attributes
supported by the ability to learn, decide and adapt
A knowledge horizon is the boundary of what we can knowgiven what we already know and the capabilities we possess.
It is as far as our knowledge and tools will permit us to see.
Searchingvia known
dimensions and attributes
Browsingvia links
Retrieving via unique identifiers
(a link or key)
Knowledge already in
your head or system
9© Synsyta 2006. All Rights Reserved.
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SURVEYOR zeros in on the optimal knowledge context & focuseven when this lies beyond the knowledge horizon
Context Focus
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Information Flow & InteroperabilityStudy on Intercommunity Intelligence Information Flow (I3F)DARPA IAO 2003
A community of service components afloat in a sea of representations
IDEFx PSLExpress
BPELBPML
FOL HOL
Modal TemporalProbabilisticNon-Monotonic
ZedSlang
Category Theory
KIF
CGsCycL
CL RuleML
OWL
RDF(S)DAML+OIL
Topic Maps
XML
NETLFRLOKBC UML
SQL
MOF
CWM
MDSOC
ORMCMESIAM
EELD
11© Synsyta 2006. All Rights Reserved.
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Objective:
Deliver the expressivity and reasoning power of Logic based Languages in a modular fashion with MDA’s modeling, transformation, and management support and the Semantic Web’s markup
– Represent each formalism as a componentized metamodel
– Define an open formal structure of metamodels & reusable metamodel components: structural, logical & semantic
– Support composition & weaving of metamodel components
– Exploit MDA machinery to conduct transformations between the metamodels and to conduct mappings to and from concrete syntaxes (syntax agnosticism)
– Formally but agnostically ground MDA
– Integrate modeling tools, repositories, transformation machinery, generators, inference engines, …, and the communities
The MDA Stack
MOF2(MDA Common Core)
UML
UML Models
UML Instances
CWM
CWMModels
CWMInstances
XML, XML Schema, &
XMLns
RDF(S)(Notation 3)
OWL(~DAML+OIL)
Rich Logics & Reasoning
The Semantic Web Stack
OWL
FOL
Common Logic
KIF
C G s
HOL
CycL
CL-X
CGIF
CLML
The Logic Based Language Stack
The Information Flow Framework
Top Metalevel
Upper Metalevel
Lower Metalevel
Category Theory(meta) Ontology
Upper Classification(meta) Ontology
Upper Core(meta) Ontology
Top Core(meta) Ontology
Lower Core(meta) Ontology
Lower Classification(meta) Ontology
Ontology(meta) Ontology
Model Theory(meta) Ontology
Algebraic Theory(meta) Ontology
How the Stacks Stack Up June 2003Study on Intercommunity Intelligence Information Flow (I3F) DARPA IAO
StacksTasks
MDA Semantic Web
CL IFF
Modeling, Transformation & Management Machinery
MOF XMI UML CWM
Tractable Expressivity & Markup
OWL-DLRDF(S) XML
Maximal Expressivity, Reasoning & Formality
KIF -> CL
Formal Grounding, Interoperability & Composition
CT IFFFCA
No single formalism will do, so how do we Integrate the Stacks?
12© Synsyta 2006. All Rights Reserved.
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Roadmap – First StageSecond Stage
Collaborative ontologies
andComposable languages
Third Stage
Living, liaising language hubs
First Stage
Automated interoperation of heterogeneous languages and
ontologies
• OWL-RA as meta language Relative metamodeling Architecture RA language idioms Powertypes Term-Concept map• Semantic Core language hub• IF classifications & infomorphisms• Chu Spaces & Transforms• Galois Lattices & Formal Concept Analysis• Relational DBMS
• Progressive & Collaborative mapping using SURVEYOR Think of “formal Flicker”• Formal instances for hub languages & ontologies• Language concern dimensions ontology Like IFF LoT• ε-Connections between language ontology components
• Chu Spaces/Transforms lifted to Institutions & Institution Morphisms in two phases• Institutionalized language concern dimension ontology
Fourth Stage
Culturalco-evolution
& symbiosis of living, liaising
languages
• Lifting of Institutions to Charters & Parchments• Model theoretic semantics as a composable concern dimension• Open-ended collection of multiple meta-mathematics
Composable theories extend collaborative classifications
Increasing collaboration and generality
13© Synsyta 2006. All Rights Reserved.
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The Example ProblemUML OWL-DL
OBI UBL<<ontologyClass>>
RosettaNet OBIOrder Item.:class
<<ontologyClass>>PO101 Assigned Identification:slotRelation
<<ontologyClass>>PO107 Product/ServiceID:slotRelation
<<ontologyClass>>PO104 Unit Price:slotRelation
<<ontologyClass>>PO102 Quantity Ordered:slotRelation
<<ontologyClass>>RosettaNet OBIOrder Item.:class
<<ontologyClass>>RosettaNet OBIOrder Item.:class
<<ontologyClass>>PO101 Assigned Identification:slotRelation
<<ontologyClass>>PO101 Assigned Identification:slotRelation
<<ontologyClass>>PO107 Product/ServiceID:slotRelation
<<ontologyClass>>PO107 Product/ServiceID:slotRelation
<<ontologyClass>>PO104 Unit Price:slotRelation
<<ontologyClass>>PO104 Unit Price:slotRelation
<<ontologyClass>>PO102 Quantity Ordered:slotRelation
<<ontologyClass>>PO102 Quantity Ordered:slotRelation
<<ontologyClass>>UBL Order Line Item.:class
<<ontologyClass>>BuyersID:slotRelation
<<ontologyClass>>ID:slotRelation
<<ontologyClass>>Description:slotRelation
<<ontologyClass>>Quantity:slotRelation
<<ontologyClass>>PriceAmount:slotRelation
<<ontologyClass>>LineExtensionAmount:function
<<ontologyClass>>UBL Order Line Item.:class
<<ontologyClass>>UBL Order Line Item.:class
<<ontologyClass>>BuyersID:slotRelation
<<ontologyClass>>BuyersID:slotRelation
<<ontologyClass>>ID:slotRelation
<<ontologyClass>>ID:slotRelation
<<ontologyClass>>Description:slotRelation
<<ontologyClass>>Description:slotRelation
<<ontologyClass>>Quantity:slotRelation
<<ontologyClass>>Quantity:slotRelation
<<ontologyClass>>PriceAmount:slotRelation
<<ontologyClass>>PriceAmount:slotRelation
<<ontologyClass>>LineExtensionAmount:function
<<ontologyClass>>LineExtensionAmount:function
RosettaNet_OBI_Item_ODMxUBL_Item_ODM:TransformationMap
ItemMap:ClassifierMap
ItemDetailsMap:ClassifierMap
OwnedElemnt
OwnedElement
BuyersID = PO101Assigned Identification;ID = PO107 Product/ServiceID;
Quantity = PO102 Quantity Ordered;PriceAmount = PO104 Unit Price:
ProcedureExpression
functionsource
source
source
source
source target
target
target
target
target
target
target
<<ontologyClass>>RosettaNet OBIOrder Item.:class
<<ontologyClass>>RosettaNet OBIOrder Item.:class
<<ontologyClass>>PO101 Assigned Identification:slotRelation
<<ontologyClass>>PO101 Assigned Identification:slotRelation
<<ontologyClass>>PO107 Product/ServiceID:slotRelation
<<ontologyClass>>PO107 Product/ServiceID:slotRelation
<<ontologyClass>>PO104 Unit Price:slotRelation
<<ontologyClass>>PO104 Unit Price:slotRelation
<<ontologyClass>>PO102 Quantity Ordered:slotRelation
<<ontologyClass>>PO102 Quantity Ordered:slotRelation
<<ontologyClass>>RosettaNet OBIOrder Item.:class
<<ontologyClass>>RosettaNet OBIOrder Item.:class
<<ontologyClass>>PO101 Assigned Identification:slotRelation
<<ontologyClass>>PO101 Assigned Identification:slotRelation
<<ontologyClass>>PO107 Product/ServiceID:slotRelation
<<ontologyClass>>PO107 Product/ServiceID:slotRelation
<<ontologyClass>>PO104 Unit Price:slotRelation
<<ontologyClass>>PO104 Unit Price:slotRelation
<<ontologyClass>>PO102 Quantity Ordered:slotRelation
<<ontologyClass>>PO102 Quantity Ordered:slotRelation
Heterogeneous• Schemas• Models• Ontologies• Schema languages• Modeling languages• Ontology languages
14© Synsyta 2006. All Rights Reserved.
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Automated Interoperation of Heterogeneous Languages and Ontologies
Meta Language• OWL-RA as meta language
– Relative metamodeling Architecture• Language idioms
– Powertypes– Term-Concept map
Language hub
• Semantic Core
Mathematics• Information Flow (IF)
– Classifications and infomorphisms• Chu Spaces and Chu Transforms• Galois Lattices and Formal Concept
Analysis
RDBMS, SQL &NeoLogical SURVEYOR
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Isomorphic Mathematical Approaches
Types
Tokens
=
Types
Tokens
=
Classification A Classification B
Infomorphism:a pair of adjoint
functionsInformation Flow
(IF)
States
Objects
Chu Space A
States
Objects
Chu Space B
Chu TransformChu Spaces
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Language MapChu Spaces for UML Class and OWL-DL
Source Chu Space UML_Class UML_Attribute U_is_related_to
PO 1 0 0POLineItem 1 0 0POLineItem_Attribute 0 1 0POLineItem is_related_to
POLineItem_Attribute0 0 1
Target Chu Space OWL-DL_Class OWL-DL_Property O_is_related_to
PO 1 0 0POLineItem 1 0 0POLineItem_Attribute 1 0 0POLineItem is_related_to
POLineItem_Attribute0 1 1
17© Synsyta 2006. All Rights Reserved.
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Language MapMerged Chu Space: UML Class and OWL-DL
Merged Chu Space: UML Class and OWL-DL
UML_Class
UML_Attribute
U_is_related_to
OWL-DL_Class
OWL-DLProperty
O_is_related_to
PO 1 0 0 1 0 0POLineItem 1 0 0 1 0 0POLineItem_Attribut
e0 1 0 1 0 0
POLineItem is related to POLineItem_Attribute
0 0 1 0 1 1
Merge duplicate columnsMerge duplicate rows
Triangulate
Reduced Chu Space OWL-DL_Class
UML_Class
UML_Attribute
U_is_related_to
OWL-DL_Property
O_is_related_toPOPOLineItem
1 1 0 0
POLineItem_Attribute 1 0 1 0
POLineItemis related to POLineItem_Attribute
0 0 0 1
18© Synsyta 2006. All Rights Reserved.
Synsyta and Neological ProprietaryLanguage MapHasse diagram of the Galois latticeover the merged Chu Space
I
OWL-DL_ClassOWL-DL_PropertyU_is_related_toO_is_related_to
UML_Class UML_Attribute
Φ
POLineItem is_related_to POLineItem_Attribute
POLineItem_Attribute
POPOLineItem
POLineItem_Attribute
POPOLineItem
Also called aConcept Lattice by
Formal Concept Analysis (FCA)
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Language MapLanguage Map of the Chu Transform
Language MapCLTup : UML_Class OWL-DL_ClassCLTup : UML_Attribute OWL-DL_ClassCLTup : U_is_related_to(UML_Class, UML_Attribute) O_related_to(OWL-DL_Class,
OWL-DL_Class) {where type(O_related_to) = OWL-DL_Property
The language map is one of the pair of contravariant functions that constitute
an infomorphism inInformation Flow (IF)
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Language MapChu Transform as a Chu Space
Generalized source Chu Space for UML class
UML_Class
UML_Attribute
owns
S1, i.e., a UML class
1 0 0
S2, i.e., a UML attribute
0 1 0
S3, i.e., the owning of a UML attribute by a UML class
0 0 1
Generalized Chu Transform UML to OWL-DL(as Chu Space)
OWL-DL_Class
OWL-DL_Property
S1, i.e., a UML class 1 0
S2, i.e., a UML attribute
1 0
S3, i.e., the owning of a UML attribute by a UML class
0 1
Generalized targetChu Space for OWL-
DL class & property
OWL-DL_Class
OWL-DL_Property
T1, i.e., an OWL-DL ontology class
1 0
T2, i.e., an OWL-DL property
0 1
21© Synsyta 2006. All Rights Reserved.
Synsyta and Neological ProprietaryDomain MapUML class model of RosettaNet Open Buying onInternet (OBI) Order Item and instance
OBI Order Item
PO101 Assigned IdentifierPO107 Product/ServiceIDPO104 Unit PricePO102 Quantity Ordered
PO1138_1 : OBI Order Item
PO101 Assigned Identifier = Cust_123PO107 Product/ServiceID = SKU_abcPO104 Unit Price = $10PO102 Quantity Ordered = 5 units
22© Synsyta 2006. All Rights Reserved.
Synsyta and Neological ProprietaryDomain MapOWL-DL ontology of Universal Business Language(UBL) Order Line Item and instances
UBL Order Line Item BuyersID
ID
Description
Quantity
PriceAmount
LineExtensionAmount
has_element
<rdf:Description rdf:about="PO1138_1"><rdf:type rdf:resource="UBL_Order_Line_Item"/></rdf:Description><rdf:Description rdf:about="Cust_123"><rdf:type rdf:resource="BuyersID"/></rdf:Description><rdf:Description rdf:about="SKU_abc"><rdf:type rdf:resource="ID"/></rdf:Description><rdf:Description rdf:about="$10"><rdf:type rdf:resource="Quantity"/></rdf:Description><rdf:Description rdf:about="5 units"><rdf:type rdf:resource="PriceAmount"/></rdf:Description><UBLOrder_Line_Item rdf:about="PO1138_1> <has_element rdf:about="Cust_123"/> <has_element rdf:about="SKU_abc"/> <has_element rdf:about="$10"/> <has_element rdf:about="5 units"/></UBLOrder_Line_Item>
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Domain map in the context of the Language map
• The language map creates two partitions for the Domain map– The class-attribute/class partition– The owns / property partition
• The Domain map consequently must have two component maps– A component map for Classes and Attributes– A component map for relations
24© Synsyta 2006. All Rights Reserved.
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Domain MapChu Spaces for OBI and UBL Classes & Attributes
Chu Space of OBIClasses & Attributes
OBIOrderItem
PO10AssignedIdentifier
PO107Product/ServiceID
PO104 UnitPrice
PO102QuantityOrdered
PO1138_1 1 0 0 0 0Cust_123 0 1 0 0 0SKU_abc 0 0 1 0 0$10 0 0 0 1 05 units 0 0 0 0 1
Chu Space of UBL
Classes
UBLOrderLineItem
BuyersID ID Description PriceAmount
Quantity LineExtensionAmount
PO1138_1 1 0 0 0 0 0 0Cust_123 0 1 0 0 0 0 0
SKU_abc 0 0 1 0 0 0 0
$10 0 0 0 0 1 0 0
5 units 0 0 0 0 0 1 0
25© Synsyta 2006. All Rights Reserved.
Synsyta and Neological Proprietary
Domain MapChu Spaces for OBI and UBL relations
Chu Space of OBI UML class – attribute relations
owns
(PO1138_1, Cust_123) 1
(PO1138_1, SKU_abc) 1
(PO1138_1, $10) 1
(PO1138_1, 5 units) 1
Chu Space of UBLOWL-DL property instances
has_element
(PO1138_1, Cust_123) 1
(PO1138_1, SKU_abc) 1
(PO1138_1, $10) 1
(PO1138_1, 5 units) 1
26© Synsyta 2006. All Rights Reserved.
Synsyta and Neological ProprietaryDomain MapMerged Chu Spaces for OBI and UBLclasses & attributes
MergedClass-attr/ClassChuSpace
OBIOrderItem
PO101AssignedIdentifier
PO107Product/ServiceID
PO104UnitPrice
PO102QuantityOrdered
UBLOrderLineItem
BuyersID ID PriceAmount
Quantity
PO1138_1 1 0 0 0 0 1 0 0 0 0
Cust_123 0 1 0 0 0 0 1 0 0 0SKU_abc 0 0 1 0 0 0 0 1 0 0$10 0 0 0 1 0 0 0 0 1 05 units 0 0 0 0 1 0 0 0 0 1
27© Synsyta 2006. All Rights Reserved.
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Domain MapChu Spaces for OBI and UBL relations
Merged owns/property Chu Space owns has_element
(PO1138_1, Cust_123) 1 1(PO1138_1, SKU_abc) 1 1(PO1138_1, $10) 1 1(PO1138_1, 5 units) 1 1
28© Synsyta 2006. All Rights Reserved.
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Domain MapHasse diagram of the Class-Attribute Lattice
Φ
I
OBI Order Item
UBL Order Line
Item
PO104 Unit Price
Price Amount
PO107 Product/ServiceID
ID
PO101 AssignedIdentifier
BuyersId
PO102 QuantityOrdered
Quantity
29© Synsyta 2006. All Rights Reserved.
Synsyta and Neological Proprietary
Domain MapHasse diagram of the Relation Lattice
Φ
ownshas_element
I
30© Synsyta 2006. All Rights Reserved.
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Language and Domain Maps
Domain MapCDTup : OBI Order Item UBL Order Line ItemCDTup : PO101Assigned Identifier BuyersID CDTup : PO107 Product/ServiceID ID CDTup : PO102 Quantity Ordered Quantity CDTup : PO104 Unit Price PriceAmount CDTup : owns has_element
Language MapCLTup : UML_Class OWL-DL_ClassCLTup : UML_Attribute OWL-DL_ClassCLTup : U_is_related_to(UML_Class, UML_Attribute)
O_related_to(OWL-DL_Class, OWL-DL_Class) {where type(O_related_to) = OWL-DL_Property
CLT:= Chu Language TransformCDT:= Chu Domain Transform (up:= type to type map)
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Extending the Example Mapping Scenario
Language 1 Language 2
Ontology 1a Ontology 2a
Model 1ai Model 2ai
Data 1ai Data 2ai
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Hub & Spoke Scenarios also Supported
SpokeLanguage
HubLanguage
SpokeLanguage
SpokeOntology
HubOntology
SpokeOntologySpoke
OntologySpokeOntology
HubOntologyHub
OntologySpoke
OntologySpokeOntology
SpokeModel
SpokeModelSpoke
ModelSpokeModelSpoke
ModelSpokeModelSpoke
ModelSpokeModelSpoke
ModelSpokeModelSpoke
ModelSpokeModel
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Challenges of the Second Stage
• BIG ontologies• Varying ontologies• Collaborative ontology development• “Our” ontology as a dynamic merging of “our” ontologies • Ontologies that come and go without notice• Acquiring common ontology instances across ontologies
(motivated by the methodology of the First Stage)
Ontology mapping as a routine, integral,
every day practice
34© Synsyta 2006. All Rights Reserved.
Synsyta and Neological Proprietary
Roadmap - Second StageSecond Stage
Collaborative ontologies
andComposable languages
Third Stage
Living, liaising language hubs
First Stage
Automated interoperation of heterogeneous languages and
ontologies
• OWL-RA as meta language Relative metamodeling Architecture RA language idioms Powertypes Term-Concept map• Semantic Core language hub• IF classifications & infomorphisms• Chu Spaces & Transforms• Galois Lattices & Formal Concept Analysis• Relational DBMS
• Progressive & Collaborative mapping using SURVEYOR Think of “formal Flicker”• Formal instances for mapping hub languages & ontologies• Ontology of language concern dimensions (like IFF LoT at the language definition level)• ε-Connections between language ontology components
• Chu Spaces/Transforms lifted to Institutions & Institution Morphisms in two phases• Institutionalized language concern dimension ontology
Fourth Stage
Culturalco-evolution
& symbiosis of living, liaising
languages
• Lifting of Institutions to Charters & Parchments• Model theoretic semantics as a composable concern dimension• Open-ended collection of multiple meta-mathematics
Composable theories extend collaborative classifications
Increasing collaboration and generality
35© Synsyta 2006. All Rights Reserved.
Synsyta and Neological Proprietary
Second Stage
Collaborative ontologiesand
Composable languages
• Progressive & Collaborative mapping using SURVEYORTM
Think of “formal Flicker”• Formal instances for mapping hub languages & ontologies• Ontology of language concern dimensions (like IFF LoT at the language definition level)• ε-Connections between language ontology components
36© Synsyta 2006. All Rights Reserved.
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Overlapping knowledgeCommon knowledge horizon
Overlapping knowledgeOverlapping knowledge
horizons
Disjoint knowledgeOverlapping knowledge horizons
Other is beyond knowledge horizon
Disjoint knowledgeDisjoint knowledge horizons
Disjoint knowledgeOverlapping knowledge
horizonsOther is partly within knowledge horizon
Concentric knowledge
Disjoint knowledgeOverlapping knowledge
horizons
Concentric knowledgeOther is partly beyond
knowledge horizon
Knowledge Horizons in a systems context
Overlapping knowledgeOverlapping knowledge
horizonsOther is partly beyond
knowledge horizon
CurrentKnowledg
e
Knowledge
Horizon
Surveyor. © 2002-6 by Infomaniacs/Neological. All Rights Reserved. 36
37© Synsyta 2006. All Rights Reserved.
Synsyta and Neological Proprietary
Roadmap - Third StageSecond Stage
Collaborative ontologies
andComposable languages
Third Stage
Living, liaising language hubs
First Stage
Automated interoperation of heterogeneous languages and
ontologies
• OWL-RA as meta language Relative metamodeling Architecture RA language idioms Powertypes Term-Concept map• Semantic Core language hub• IF classifications & infomorphisms• Chu Spaces & Transforms• Galois Lattices & Formal Concept Analysis• Relational DBMS
• Progressive & Collaborative mapping using SURVEYOR Think of “formal Flicker”• Formal instances for hub languages & ontologies• Language concern dimensions ontology Like IFF LoT• ε-Connections between language ontology components
• Chu Spaces/Transforms lifted to Institutions & Institution Morphisms in two phases• Institutionalized language concern dimension ontology
Fourth Stage
Culturalco-evolution
& symbiosis of living, liaising
languages
• Lifting of Institutions to Charters & Parchments• Model theoretic semantics as a composable concern dimension• Open-ended collection of multiple meta-mathematics
Composable theories extend collaborative classifications
Increasing collaboration and generality
38© Synsyta 2006. All Rights Reserved.
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MAGIC - Managed Logic
Automates heterogeneous interoperability between:• New Activities by defining end-points with MAGIC • Existing Activities by redefining end-points with MAGICTasks/communities may be uncoupled or loosely coupled
Producer defines task & community specificlanguagewith MAGIC
MAGIC
Consumer defines distinct task &
community specificlanguage
with MAGIC
MAGIC
Information flows overbridge generated andmanaged by MAGIC
MAGIC MAGIC
MAGIC is not yet another language - it is a language machine
39© Synsyta 2006. All Rights Reserved.
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Managed LogicIn
form
atio
n Fl
ow
MAGIC - Managed LogicLifecycle Management of Synthetic Language Systems and their Interrelations
• XML• RDF(S)• Topic Maps• DAML+OIL & OWL• OKBC• CycL• KIF• Common Logic & SCL• Conceptual Graphs• UML• DSLs• IDEFx & Express• SQL• BPML and BPEL• VHDL & Verilog• ADLs
Example SLS
Core Capabilities
• Formally Define SLS– Componentize existing SLS– Compose new general
purpose and domain specific SLS
– Modify & hybridize SLS to meet new and changing requirements
• Coordinate Multiple SLS– Synergistically employ
multiple, diverse SLS (transoperate)
– Interoperate between distinct or versioned SLSLanguage
Components
VerificationFormal
LanguageDefinitions
Syntactic / SemanticLanguage
Transformations
Formal Composition
Automated Production
Composition
Query/Browse/Trace
Define/Modify/Verify
Transformation
Federation
Import/Export
Code Generation
Code Deployment
Code Sustainment
ServicesDecomposition
Version Mgt.
Services encapsulate formal underpinnings
Informal& Formal
SLSSpecs
Formal Aspect
Oriented Decompositio
n• Modeling• Specification• Knowledge Representation• Learning
• Engineering• Ontology• Reasoning• Integration & Interchange
SLS are used for …
The facility does not require an intermediate canonical
language;the library spans
complementary language components in the spirit of
Sowa’s Knowledge Soup and Kent’s Information Flow
Framework.
Enables Flow
SLS (Synthetic Language Systems) are logics, representational formalisms, formal languages and domain specific languages taken together with their
associated products, e.g., expressions, instances, models and ontologies
40© Synsyta 2006. All Rights Reserved.
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Institution Example
Collie DogFido Dog
Fido Collie
FidoDog
Collie
Functor injects logical symbols and guarantees well-formedness of sentences, i.e., grammar
Sets of ModelsSentences
Signatures (syntax)
DogCollie[Fido]
This example applies the theory of Institutions at an ontological level; Institutions are customarily used to abstract logics and languages
∩∩
∩
=
States
Objects
States
ObjectsChu Transform
Chu Space A Chu Space BTypes
Tokens
==
Types
Tokens
==
Classification B
Infomorphism:a pair of adjoint
functions
Classification A IF ChuFor comparison
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Synsyta and Neological Proprietary
Roadmap – Fourth StageSecond Stage
Collaborative ontologies
andComposable languages
Third Stage
Living, liaising language hubs
First Stage
Automated interoperation of heterogeneous languages and
ontologies
• OWL-RA as meta language Relative metamodeling Architecture RA language idioms Powertypes Term-Concept map• Semantic Core language hub• IF classifications & infomorphisms• Chu Spaces & Transforms• Galois Lattices & Formal Concept Analysis• Relational DBMS
• Progressive & Collaborative mapping using SURVEYOR Think of “formal Flicker”• Formal instances for hub languages & ontologies• Language concern dimensions ontology Like IFF LoT• ε-Connections between language ontology components
• Chu Spaces/Transforms lifted to Institutions & Institution Morphisms in two phases• Institutionalized language concern dimension ontology
Fourth Stage
Culturalco-evolution
& symbiosis of living, liaising
languages
• Lifting of Institutions to Charters & Parchments• Model theoretic semantics as a composable concern dimension• Open-ended collection of multiple meta-mathematics
Composable theories extend collaborative classifications
Increasing collaboration and generality
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Synsyta and Neological Proprietary
Next steps
• First Stage - Proof of Concept– Run real mapping examples in RDBMS using SURVEYOR and
advanced maths• Second Stage - R&D
– Collaborative, progressive ontology mapping using SURVEYOR and Web 2.0 technologies• Communities of ontologies; emergent “consensus” semantics
– Ontology of language concern dimensions as meta ontology to Semantic Core• Composable languages
43© Synsyta 2006. All Rights Reserved.
Synsyta and Neological Proprietary
SURVEYORDemo
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