Von Schweber Living Systems

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  • Von Schweber Living Systems

    1. 1. Living Systems & Living, Liaising Languages Key to Netcentric Interoperability January 24, 2006 Erick Von Schweber CTO and Chief Architect Synsyta [email_address]
    2. 2. Riding the Waves The challenge of our era Transforming clockwork mechanism into living technology First Wave Uniprocessing Second Wave Parallel & Distributed Processing IBM On-Demand Sun N1 HP Adaptive Enterprise Blade Servers SMTA & Multicore Web Grids Semantic Web SOA P2P Web 2.0 AJAX RSS MDA Globus Model Driven Semantic Grid Wireless Mesh Ad Hoc Networks Web Services Third Wave Computing Fabrics RDF OWL Living, Organic Architecture Systems into Ecosystems Cultural Co-Evolution Symbiosis Synsytia Surveying Living Liaising Languages Semantic Index Grid Managed Logic ccNUMA clusters Reconfigurable Logic FPGAs XML
    3. 3. Outline <ul><li>Riding the waves </li></ul><ul><li>Biologically and culturally inspired interoperation </li></ul><ul><li>Foundations </li></ul><ul><li>Roadmap </li></ul><ul><li>Next steps </li></ul>
    4. 4. A Human+Machine culture of Living, Liaising Languages
    5. 5. Machine-Machine vs. Human–Human Communication We culturally evolve They must be reengineered Our languages are in constant flux Their languages change with periodic releases and revisions We self-adapt They are static and rigid We negotiate They stonewall We use analogy, metaphor and conceptual blending Ambiguity simply does not compute We point to examples They require instructions We learn each other’s lingos as we communicate They are programmed in advance of interoperation Human–Human Machine-Machine
    6. 6. Roadmap Second Stage Collaborative ontologies and Composable languages Third Stage Living, liaising language hubs First Stage Automated interoperation of heterogeneous languages and ontologies Fourth Stage Cultural co-evolution & symbiosis of living, liaising languages Composable theories extend collaborative classifications Increasing collaboration and generality
    7. 7. Foundations <ul><li>NeoLogical SURVEYOR, 1983 – present </li></ul><ul><li>Computing Fabrics, 1996 - present </li></ul><ul><li>“ Software Services Grid” workshop, July 2001 </li></ul><ul><li>“ Model Driven Semantic Grid” GGF2, July 2001 </li></ul><ul><li>“ Information Representation & Transformation” for DARPA TIA </li></ul><ul><ul><li>“ Model Driven Semantic Grid, and Beyond” workshop, Jan. 2003 </li></ul></ul><ul><ul><li>“ How the Stacks Stack Up” workshop, June 2003 </li></ul></ul><ul><ul><li>“ Integrated Semantics” workshop, August 2003 </li></ul></ul><ul><li>“ Managed Logic worked example” white paper 2004 </li></ul><ul><li>“ Roadmap for Semantics in netcentric Enterprise Architecture” white paper for GSA, August 2005 - present </li></ul><ul><li>Ken Baclawski </li></ul><ul><li>Desmond D’Souza </li></ul><ul><li>Dave Frankel </li></ul><ul><li>Elisa Kendal </li></ul><ul><li>Robert Kent </li></ul><ul><li>Deb McGuinness </li></ul><ul><li>Sheila McIlraith </li></ul><ul><li>Jeff Smith </li></ul><ul><li>John Sowa </li></ul>Cast of Characters (frequently appearing) <ul><li>Erick Von Schweber </li></ul><ul><li>Linda Von Schweber </li></ul><ul><li>Cory Casanave </li></ul><ul><li>Joseph Goguen </li></ul>
    8. 8. A knowledge horizon is the boundary of what we can know given what we already know and the capabilities we possess. It is as far as our knowledge and tools will permit us to see. Surveying via unknown dimensions and attributes supported by the ability to learn, decide and adapt Searching via known dimensions and attributes Browsing via links Retrieving via unique identifiers (a link or key) Knowledge already in your head or system
    9. 9. SURVEYOR zeros in on the optimal knowledge context & focus even when this lies beyond the knowledge horizon Context Focus
    10. 10. Information Flow & Interoperability Study on Intercommunity Intelligence Information Flow (I 3 F) DARPA IAO 2003 <ul><li>A community of service components afloat in a sea of representations </li></ul>IDEFx PSL Express BPEL BPML FOL HOL Modal Temporal Probabilistic Non-Monotonic Zed Slang Category Theory KIF CGs CycL CL RuleML OWL RDF(S) DAML+OIL Topic Maps XML NETL FRL OKBC UML SQL MOF CWM MDSOC ORM CME SIAM EELD
    11. 11. <ul><li>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 </li></ul><ul><ul><li>Represent each formalism as a componentized metamodel </li></ul></ul><ul><ul><li>Define an open formal structure of metamodels & reusable metamodel components: structural, logical & semantic </li></ul></ul><ul><ul><li>Support composition & weaving of metamodel components </li></ul></ul><ul><ul><li>Exploit MDA machinery to conduct transformations between the metamodels and to conduct mappings to and from concrete syntaxes (syntax agnosticism) </li></ul></ul><ul><ul><li>Formally but agnostically ground MDA </li></ul></ul><ul><ul><li>Integrate modeling tools, repositories, transformation machinery, generators, inference engines, …, and the communities </li></ul></ul>How the Stacks Stack Up June 2003 Study on Intercommunity Intelligence Information Flow (I 3 F) DARPA IAO No single formalism will do, so how do we Integrate the Stacks? The MDA Stack MOF2 (MDA Common Core) UML UML Models UML Instances CWM CWM Models CWM Instances XML, XML Schema, & XMLns RDF(S) (Notation 3) OWL (~DAML+OIL) Rich Logics & Reasoning The Semantic Web Stack O W L FOL Common Logic K I F C G s HOL CycL CL-X C G I F C L M L 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 MOF XMI UML CWM Modeling, Transformation & Management Machinery CT IFF FCA Formal Grounding, Interoperability & Composition KIF -> CL Maximal Expressivity, Reasoning & Formality OWL-DL RDF(S) XML Tractable Expressivity & Markup IFF CL Semantic Web MDA Stacks Tasks
    12. 12. Roadmap – First Stage Second Stage Collaborative ontologies and Composable languages Third Stage Living, liaising language hubs First Stage Automated interoperation of heterogeneous languages and ontologies <ul><li>OWL-RA as meta language </li></ul><ul><li>Relative metamodeling </li></ul><ul><li>Architecture </li></ul><ul><li>RA language idioms </li></ul><ul><li>Powertypes </li></ul><ul><li>Term-Concept map </li></ul><ul><li>Semantic Core language hub </li></ul><ul><li>IF classifications & infomorphisms </li></ul><ul><li>Chu Spaces & Transforms </li></ul><ul><li>Galois Lattices & Formal Concept Analysis </li></ul><ul><li>Relational DBMS </li></ul><ul><li>Progressive & Collaborative mapping using SURVEYOR </li></ul><ul><li>Think of “formal Flicker” </li></ul><ul><li>Formal instances for hub languages & ontologies </li></ul><ul><li>Language concern dimensions ontology </li></ul><ul><li>Like IFF LoT </li></ul><ul><li>ε -Connections between language ontology components </li></ul><ul><li>Chu Spaces/Transforms lifted to Institutions & Institution Morphisms </li></ul><ul><li>in two phases </li></ul><ul><li>Institutionalized language concern dimension ontology </li></ul>Fourth Stage Cultural co-evolution & symbiosis of living, liaising languages <ul><li>Lifting of Institutions to Charters & Parchments </li></ul><ul><li>Model theoretic semantics as a composable concern dimension </li></ul><ul><li>Open-ended collection of multiple meta-mathematics </li></ul>Composable theories extend collaborative classifications Increasing collaboration and generality
    13. 13. The Example Problem UML OWL-DL OBI UBL <ul><li>Heterogeneous </li></ul><ul><li>Schemas </li></ul><ul><li>Models </li></ul><ul><li>Ontologies </li></ul><ul><li>Schema languages </li></ul><ul><li>Modeling languages </li></ul><ul><li>Ontology languages </li></ul>
    14. 14. Automated Interoperation of Heterogeneous Languages and Ontologies <ul><li>Meta Language </li></ul><ul><li>OWL-RA as meta language </li></ul><ul><ul><li>Relative metamodeling Architecture </li></ul></ul><ul><li>Language idioms </li></ul><ul><ul><li>Powertypes </li></ul></ul><ul><ul><li>Term-Concept map </li></ul></ul><ul><li>Language hub </li></ul><ul><li>Semantic Core </li></ul><ul><li>Mathematics </li></ul><ul><li>Information Flow (IF) </li></ul><ul><ul><li>Classifications and infomorphisms </li></ul></ul><ul><li>Chu Spaces and Chu Transforms </li></ul><ul><li>Galois Lattices and Formal Concept Analysis </li></ul><ul><ul><li>RDBMS, SQL & NeoLogical SURVEYOR </li></ul></ul>
    15. 15. Isomorphic Mathematical Approaches Types Tokens Types Tokens Classification A Classification B Infomorphism: a pair of adjoint functions Information Flow (IF) States Objects Chu Space A States Objects Chu Space B Chu Transform Chu Spaces = =
    16. 16. Language Map Chu Spaces for UML Class and OWL-DL 1 0 0 POLineItem is_related_to POLineItem_Attribute 0 1 0 POLineItem_Attribute 0 0 1 POLineItem 0 0 1 PO U_is_related_to UML_Attribute UML_Class Source Chu Space 1 1 0 POLineItem is_related_to POLineItem_Attribute 0 0 1 POLineItem_Attribute 0 0 1 POLineItem 0 0 1 PO O_is_related_to OWL-DL_Property OWL-DL_Class Target Chu Space
    17. 17. Language Map Merged Chu Space: UML Class and OWL-DL Merge duplicate columns Merge duplicate rows Triangulate 1 1 0 1 0 0 POLineItem is related to POLineItem_Attribute 0 0 1 0 1 0 POLineItem_Attribute 0 0 1 0 0 1 POLineItem 0 0 1 0 0 1 PO O_is_ related_to OWL-DLProperty OWL-DL_ Class U_is_ related_to UML_ Attribute UML_ Class Merged Chu Space: UML Class and OWL-DL 1 0 0 0 POLineItem is related to POLineItem_ Attribute 0 1 0 1 POLineItem_Attribute 0 0 1 1 PO POLineItem U_is_related_to OWL-DL_Property O_is_related_to UML_ Attribute UML_ Class OWL- DL_Class Reduced Chu Space
    18. 18. Language Map Hasse diagram of the Galois lattice over the merged Chu Space I OWL-DL_Class OWL-DL_Property U_is_related_to O_is_related_to UML_Class UML_Attribute Φ POLineItem is_related_to POLineItem_Attribute POLineItem_Attribute PO POLineItem POLineItem_Attribute PO POLineItem Also called a Concept Lattice by Formal Concept Analysis (FCA)
    19. 19. Language Map Language Map of the Chu Transform The language map is one of the pair of contravariant functions that constitute an infomorphism in Information Flow (IF) Language Map CLT up : UML_Class  OWL-DL_Class CLT up : UML_Attribute  OWL-DL_Class CLT up : 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
    20. 20. Language Map Chu Transform as a Chu Space 1 0 0 S3, i.e., the owning of a UML attribute by a UML class 0 1 0 S2, i.e., a UML attribute 0 0 1 S1, i.e., a UML class owns UML_ Attribute UML_ Class Generalized source Chu Space for UML class 1 0 S3, i.e., the owning of a UML attribute by a UML class 0 1 S2, i.e., a UML attribute 0 1 S1, i.e., a UML class OWL-DL_ Property OWL-DL_ Class Generalized Chu Transform UML to OWL-DL (as Chu Space) 1 0 T2, i.e., an OWL-DL property 0 1 T1, i.e., an OWL-DL ontology class OWL- DL_Property OWL- DL_Class Generalized target Chu Space for OWL-DL class & property
    21. 21. Domain Map UML class model of RosettaNet Open Buying on Internet (OBI) Order Item and instance OBI Order Item PO101 Assigned Identifier PO107 Product/ServiceID PO104 Unit Price PO102 Quantity Ordered PO1138_1 : OBI Order Item PO101 Assigned Identifier = Cust_123 PO107 Product/ServiceID = SKU_abc PO104 Unit Price = $10 PO102 Quantity Ordered = 5 units
    22. 22. Domain Map OWL-DL ontology of Universal Business Language (UBL) Order Line Item and instances UBL Order Line Item BuyersID ID Description Quantity PriceAmount LineExtension Amount has_element <rdf:Description rdf:about=&quot;PO1138_1&quot;> <rdf:type rdf:resource=&quot;UBL_Order_Line_Item&quot;/> </rdf:Description> <rdf:Description rdf:about=&quot;Cust_123&quot;> <rdf:type rdf:resource=&quot;BuyersID&quot;/> </rdf:Description> <rdf:Description rdf:about=&quot;SKU_abc&quot;> <rdf:type rdf:resource=&quot;ID&quot;/> </rdf:Description> <rdf:Description rdf:about=&quot;$10&quot;> <rdf:type rdf:resource=&quot;Quantity&quot;/> </rdf:Description> <rdf:Description rdf:about=&quot;5 units&quot;> <rdf:type rdf:resource=&quot;PriceAmount&quot;/> </rdf:Description> <UBLOrder_Line_Item rdf:about=&quot;PO1138_1> <has_element rdf:about=&quot;Cust_123&quot;/> <has_element rdf:about=&quot;SKU_abc&quot;/> <has_element rdf:about=&quot;$10&quot;/> <has_element rdf:about=&quot;5 units&quot;/> </UBLOrder_Line_Item>
    23. 23. Domain map in the context of the Language map <ul><li>The language map creates two partitions for the Domain map </li></ul><ul><ul><li>The class-attribute/class partition </li></ul></ul><ul><ul><li>The owns / property partition </li></ul></ul><ul><li>The Domain map consequently must have two component maps </li></ul><ul><ul><li>A component map for Classes and Attributes </li></ul></ul><ul><ul><li>A component map for relations </li></ul></ul>
    24. 24. Domain Map Chu Spaces for OBI and UBL Classes & Attributes 1 0 0 0 0 5 units 0 1 0 0 0 $10 0 0 1 0 0 SKU_abc 0 0 0 1 0 Cust_123 0 0 0 0 1 PO1138_1 PO102 Quantity Ordered PO104 Unit Price PO107 Product/ ServiceID PO10 Assigned Identifier OBI Order Item Chu Space of OBI Classes & Attributes 0 1 0 0 0 0 0 5 units 0 0 1 0 0 0 0 $10 0 0 0 0 1 0 0 SKU_abc 0 0 0 0 0 1 0 Cust_123 0 0 0 0 0 0 1 PO1138_1 Line Extension Amount Quantity Price Amount Description ID BuyersID UBL Order Line Item Chu Space of UBL Classes
    25. 25. Domain Map Chu Spaces for OBI and UBL relations 1 (PO1138_1, 5 units) 1 (PO1138_1, $10) 1 (PO1138_1, SKU_abc) 1 (PO1138_1, Cust_123) owns Chu Space of OBI UML class – attribute relations 1 (PO1138_1, 5 units) 1 (PO1138_1, $10) 1 (PO1138_1, SKU_abc) 1 (PO1138_1, Cust_123) has_element Chu Space of UBL OWL-DL property instances
    26. 26. Domain Map Merged Chu Spaces for OBI and UBL classes & attributes 1 0 0 0 0 1 0 0 0 0 5 units 0 1 0 0 0 0 1 0 0 0 $10 0 0 1 0 0 0 0 1 0 0 SKU_abc 0 0 0 1 0 0 0 0 1 0 Cust_123 0 0 0 0 1 0 0 0 0 1 PO1138_1 Quantity Price Amount ID BuyersID UBL Order Line Item PO102 Quantity Ordered PO104 Unit Price PO107 Product/ ServiceID PO101 Assigned Identifier OBI Order Item Merged Class-attr/ Class Chu Space
    27. 27. Domain Map Chu Spaces for OBI and UBL relations 1 1 (PO1138_1, 5 units) 1 1 (PO1138_1, $10) 1 1 (PO1138_1, SKU_abc) 1 1 (PO1138_1, Cust_123) has_element owns Merged owns/property Chu Space
    28. 28. Domain Map Hasse 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 Assigned Identifier BuyersId PO102 Quantity Ordered Quantity
    29. 29. Domain Map Hasse diagram of the Relation Lattice Φ owns has_element I
    30. 30. Language and Domain Maps CLT:= Chu Language Transform CDT:= Chu Domain Transform (up:= type to type map) Domain Map CDT up : OBI Order Item  UBL Order Line Item CDT up : PO101Assigned Identifier  BuyersID CDT up : PO107 Product/ServiceID  ID CDT up : PO102 Quantity Ordered  Quantity CDT up : PO104 Unit Price  PriceAmount CDT up : owns  has_element Language Map CLT up : UML_Class  OWL-DL_Class CLT up : UML_Attribute  OWL-DL_Class CLT up : 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
    31. 31. Extending the Example Mapping Scenario Language 1 Language 2 Ontology 1a Ontology 2a Model 1ai Model 2ai Data 1ai Data 2ai
    32. 32. Hub & Spoke Scenarios also Supported Spoke Language Hub Language Spoke Language Spoke Ontology Hub Ontology Spoke Ontology Spoke Ontology Spoke Ontology Hub Ontology Hub Ontology Spoke Ontology Spoke Ontology Spoke Model Spoke Model Spoke Model Spoke Model Spoke Model Spoke Model Spoke Model Spoke Model Spoke Model Spoke Model Spoke Model Spoke Model
    33. 33. Challenges of the Second Stage <ul><li>BIG ontologies </li></ul><ul><li>Varying ontologies </li></ul><ul><li>Collaborative ontology development </li></ul><ul><li>“Our” ontology as a dynamic merging of “our” ontologies </li></ul><ul><li>Ontologies that come and go without notice </li></ul><ul><li>Acquiring common ontology instances across ontologies (motivated by the methodology of the First Stage) </li></ul>Ontology mapping as a routine, integral, every day practice
    34. 34. Roadmap - Second Stage Second Stage Collaborative ontologies and Composable languages Third Stage Living, liaising language hubs First Stage Automated interoperation of heterogeneous languages and ontologies <ul><li>OWL-RA as meta language </li></ul><ul><li>Relative metamodeling </li></ul><ul><li>Architecture </li></ul><ul><li>RA language idioms </li></ul><ul><li>Powertypes </li></ul><ul><li>Term-Concept map </li></ul><ul><li>Semantic Core language hub </li></ul><ul><li>IF classifications & infomorphisms </li></ul><ul><li>Chu Spaces & Transforms </li></ul><ul><li>Galois Lattices & Formal Concept Analysis </li></ul><ul><li>Relational DBMS </li></ul><ul><li>Progressive & Collaborative mapping using SURVEYOR </li></ul><ul><li>Think of “formal Flicker” </li></ul><ul><li>Formal instances for mapping hub languages & ontologies </li></ul><ul><li>Ontology of language concern dimensions </li></ul><ul><li>(like IFF LoT at the language definition level) </li></ul><ul><li>ε -Connections between language ontology components </li></ul><ul><li>Chu Spaces/Transforms lifted to Institutions & Institution Morphisms </li></ul><ul><li>in two phases </li></ul><ul><li>Institutionalized language concern dimension ontology </li></ul>Fourth Stage Cultural co-evolution & symbiosis of living, liaising languages <ul><li>Lifting of Institutions to Charters & Parchments </li></ul><ul><li>Model theoretic semantics as a composable concern dimension </li></ul><ul><li>Open-ended collection of multiple meta-mathematics </li></ul>Composable theories extend collaborative classifications Increasing collaboration and generality
    35. 35. Second Stage Collaborative ontologies and Composable languages <ul><li>Progressive & Collaborative mapping using SURVEYOR TM </li></ul><ul><li>Think of “formal Flicker” </li></ul><ul><li>Formal instances for mapping hub languages & ontologies </li></ul><ul><li>Ontology of language concern dimensions </li></ul><ul><li>(like IFF LoT at the language definition level) </li></ul><ul><li>ε -Connections between language ontology components </li></ul>
    36. 36. Knowledge Horizons in a systems context Overlapping knowledge Common knowledge horizon Overlapping knowledge Overlapping knowledge horizons Disjoint knowledge Overlapping knowledge horizons Other is beyond knowledge horizon Disjoint knowledge Disjoint knowledge horizons Disjoint knowledge Overlapping knowledge horizons Other is partly within knowledge horizon Concentric knowledge Disjoint knowledge Overlapping knowledge horizons Concentric knowledge Other is partly beyond knowledge horizon Overlapping knowledge Overlapping knowledge horizons Other is partly beyond knowledge horizon Current Knowledge Knowledge Horizon Surveyor . © 2002-6 by Infomaniacs/Neological. All Rights Reserved.
    37. 37. Roadmap - Third Stage Second Stage Collaborative ontologies and Composable languages Third Stage Living, liaising language hubs First Stage Automated interoperation of heterogeneous languages and ontologies <ul><li>OWL-RA as meta language </li></ul><ul><li>Relative metamodeling </li></ul><ul><li>Architecture </li></ul><ul><li>RA language idioms </li></ul><ul><li>Powertypes </li></ul><ul><li>Term-Concept map </li></ul><ul><li>Semantic Core language hub </li></ul><ul><li>IF classifications & infomorphisms </li></ul><ul><li>Chu Spaces & Transforms </li></ul><ul><li>Galois Lattices & Formal Concept Analysis </li></ul><ul><li>Relational DBMS </li></ul><ul><li>Progressive & Collaborative mapping using SURVEYOR </li></ul><ul><li>Think of “formal Flicker” </li></ul><ul><li>Formal instances for hub languages & ontologies </li></ul><ul><li>Language concern dimensions ontology </li></ul><ul><li>Like IFF LoT </li></ul><ul><li>ε -Connections between language ontology components </li></ul><ul><li>Chu Spaces/Transforms lifted to Institutions & Institution Morphisms </li></ul><ul><li>in two phases </li></ul><ul><li>Institutionalized language concern dimension ontology </li></ul>Fourth Stage Cultural co-evolution & symbiosis of living, liaising languages <ul><li>Lifting of Institutions to Charters & Parchments </li></ul><ul><li>Model theoretic semantics as a composable concern dimension </li></ul><ul><li>Open-ended collection of multiple meta-mathematics </li></ul>Composable theories extend collaborative classifications Increasing collaboration and generality
    38. 38. MAGIC - Managed Logic <ul><li>Automates heterogeneous interoperability between: </li></ul><ul><li>New Activities by defining end-points with MAGIC </li></ul><ul><li>Existing Activities by redefining end-points with MAGIC </li></ul><ul><li>Tasks/communities may be uncoupled or loosely coupled </li></ul>Producer defines task & community specific language with MAGIC MAGIC Consumer defines distinct task & community specific language with MAGIC MAGIC Information flows over bridge generated and managed by MAGIC MAGIC is not yet another language - it is a language machine MAGIC MAGIC
    39. 39. MAGIC - Managed Logic Lifecycle Management of Synthetic Language Systems and their Interrelations <ul><li>Core Capabilities </li></ul><ul><li>Formally Define SLS </li></ul><ul><ul><li>Componentize existing SLS </li></ul></ul><ul><ul><li>Compose new general purpose and domain specific SLS </li></ul></ul><ul><ul><li>Modify & hybridize SLS to meet new and changing requirements </li></ul></ul><ul><li>Coordinate Multiple SLS </li></ul><ul><ul><li>Synergistically employ multiple, diverse SLS (transoperate) </li></ul></ul><ul><ul><li>Interoperate between distinct or versioned SLS </li></ul></ul>Services encapsulate formal underpinnings 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. 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 Managed Logic Information Flow <ul><li>XML </li></ul><ul><li>RDF(S) </li></ul><ul><li>Topic Maps </li></ul><ul><li>DAML+OIL & OWL </li></ul><ul><li>OKBC </li></ul><ul><li>CycL </li></ul><ul><li>KIF </li></ul><ul><li>Common Logic & SCL </li></ul><ul><li>Conceptual Graphs </li></ul><ul><li>UML </li></ul><ul><li>DSLs </li></ul><ul><li>IDEFx & Express </li></ul><ul><li>SQL </li></ul><ul><li>BPML and BPEL </li></ul><ul><li>VHDL & Verilog </li></ul><ul><li>ADLs </li></ul>Example SLS Language Components Verification Formal Language Definitions Syntactic / Semantic Language Transformations Formal Composition Automated Production Composition Query/Browse/Trace Define/Modify/Verify Transformation Federation Import/Export Code Generation Code Deployment Code Sustainment Services Decomposition Version Mgt. Informal & Formal SLS Specs Formal Aspect Oriented Decomposition <ul><li>Modeling </li></ul><ul><li>Specification </li></ul><ul><li>Knowledge Representation </li></ul><ul><li>Learning </li></ul><ul><li>Engineering </li></ul><ul><li>Ontology </li></ul><ul><li>Reasoning </li></ul><ul><li>Integration & Interchange </li></ul>SLS are used for … Enables Flow
    40. 40. Institution Example Collie Dog Fido Dog Fido Collie Fido Dog Collie Functor injects logical symbols and guarantees well-formedness of sentences, i.e., grammar Sets of Models Sentences Signatures (syntax) Dog Collie [Fido] This example applies the theory of Institutions at an ontological level; Institutions are customarily used to abstract logics and languages IF Chu For comparison ∩ ∩ ∩ =
    41. 41. Roadmap – Fourth Stage Second Stage Collaborative ontologies and Composable languages Third Stage Living, liaising language hubs First Stage Automated interoperation of heterogeneous languages and ontologies <ul><li>OWL-RA as meta language </li></ul><ul><li>Relative metamodeling </li></ul><ul><li>Architecture </li></ul><ul><li>RA language idioms </li></ul><ul><li>Powertypes </li></ul><ul><li>Term-Concept map </li></ul><ul><li>Semantic Core language hub </li></ul><ul><li>IF classifications & infomorphisms </li></ul><ul><li>Chu Spaces & Transforms </li></ul><ul><li>Galois Lattices & Formal Concept Analysis </li></ul><ul><li>Relational DBMS </li></ul><ul><li>Progressive & Collaborative mapping using SURVEYOR </li></ul><ul><li>Think of “formal Flicker” </li></ul><ul><li>Formal instances for hub languages & ontologies </li></ul><ul><li>Language concern dimensions ontology </li></ul><ul><li>Like IFF LoT </li></ul><ul><li>ε -Connections between language ontology components </li></ul><ul><li>Chu Spaces/Transforms lifted to Institutions & Institution Morphisms </li></ul><ul><li>in two phases </li></ul><ul><li>Institutionalized language concern dimension ontology </li></ul>Fourth Stage Cultural co-evolution & symbiosis of living, liaising languages <ul><li>Lifting of Institutions to Charters & Parchments </li></ul><ul><li>Model theoretic semantics as a composable concern dimension </li></ul><ul><li>Open-ended collection of multiple meta-mathematics </li></ul>Composable theories extend collaborative classifications Increasing collaboration and generality
    42. 42. Next steps <ul><li>First Stage - Proof of Concept </li></ul><ul><ul><li>Run real mapping examples in RDBMS using SURVEYOR and advanced maths </li></ul></ul><ul><li>Second Stage - R&D </li></ul><ul><ul><li>Collaborative, progressive ontology mapping using SURVEYOR and Web 2.0 technologies </li></ul></ul><ul><ul><ul><li>Communities of ontologies; emergent “consensus” semantics </li></ul></ul></ul><ul><ul><li>Ontology of language concern dimensions as meta ontology to Semantic Core </li></ul></ul><ul><ul><ul><li>Composable languages </li></ul></ul></ul>
    43. 43. <ul><li>SURVEYOR </li></ul><ul><li>Demo </li></ul>

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