Semantic Interoperability Net Centric Perspective Presented to SICoP Team

1. Semantic InteroperabilityNet Centric PerspectivePresented to SICoP Team John A. Yanosy Jr. Chair NCOIC SII-WG August 15, 2006 Semantic Interoperability

2. “Many of the problems we have identified can be categorized as “information gaps” – or at least problems with information-related implications, or failures to act decisively because information was sketchy at best. Better information would have been an optimal weapon against Katrina. Information sent to the right people at the right place at the right time. Information moved within agencies, across departments, and between jurisdictions of government as well. Seamlessly. Securely. Efficiently.” The Final Report of the Select Bipartisan Committee to Investigate the Preparation for and Response to Hurricane Katrina Semantic Interoperability

3. Semantic Interoperability Overview Semantic Interoperability

4. Networking Tension Semantic Interoperability

5. Semantic Interoperability Context Hypothesis- Semantic errors due to mutual misinterpretation cause unintended consequences in system interactions 1 2 2 System System 4 Semantic errors due to mutual misinterpretation cause unintended consequences in system interactions People People 5 5 3 3 1 H-H 2 H-S 3 H-W 4 S-S 5 S-W Each has potential for semantic breakdown World Semantic Interoperability

6. Semantic Interoperability Intersection Knowledge Information Actions/Services Goals Context Communications Semantic Interoperability

7. Conceptual Framework of Frameworks Semantic Autonomic Management (?) Semantic Policy Framework (SWRL?) Semantic Context Framework (?) Semantic Information Framework (OWL, NIEM, DRM C2IEDM) Semantic Collaboration Framework (FIPA, ACL) Semantic Content Framework (MPEG) Semantic Services Framework (OWL-S) Semantic Communication Framework (SOAP) Semantic Interoperability

8. Semantic Frameworks • Semantic Net Centric Interoperability Framework – architectural gereral layers and elements enabling mutually consistent interpretation of interactions • Semantic Information Framework - layered model defining hierarchical semantic knowledge and structures • Semantic Context Framework – situational context model • Semantic SOA Framework - layered model web services including Service Discovery Profiles Semantic Metadata, Semantic Service Descriptions, Taxonomic Metadata enabling linking of service dependency relationships, metadata linking service descriptions to domain specific semantic data models, common services supporting capabilities required by all services • Command and Control Semantic Adaptive Policy Control Framework - semantic framework enabling overall policy constraints on the Semantic Information Framework and the Semantic SOA Framework - provides command and control across all layers representing unique constraints by various COIs • Semantic Autonomic Management Framework- provides semantic models enabling self management supporting operations (Self Configuration, Self Healing, Self Optimization, Self Security) • Semantic Collaboration Framework - Intelligent Agent based Framework that can support adaptive mediation between all of the other frameworks and that provides dynamic collaborative formation of agents • Semantic Communication Framework - framework encompassing support for metadata descriptions of message based communications, metadata representation of message content, models relating data elements of messages to Semantic Information Framework semantic data models, models defining semantic intention of message • Semantic Media and Content Framework - framework enabling the semantic representation of the nature of the media content type, music, voice, image, etc. in such a way that adaptation can be provided to modify content for end to end and device adapation purposes - many meatdata standards already exist Semantic Interoperability

9. Semantic Interoperability Issues Semantic Interoperability

10. Semantic Incompatibility Issues • COIs, Social, Organizational, Cultural Assumptions and Policies • Domain Knowledge • Ontology Relationships and Harmonization • Logic(s) DL, FOL, Intensional, SWRL • Context Dependency • Semantic Expressibility, • Semantic Web – URI Networking, Discovery • Implementation Technology Semantic Interoperability

11. Varying Semantic Representations Explicit Semantic Web Relevant, Discoverable, Understandable Semantic Knowledge UDDI, Domain, Ontologies Context Ontologies, Cognitive Agents, Mediation NCO Service & Data Tenets Context, Upper Ontology Semantic Interoperability Across Ontologies Domain Ontology C-OWL COIN IEEE SUMO IFF Upper CYC SWRL Semantic Knowledge Model & Logic Taxonomies Organized Hierarchical Classifications OWL-S Metadata Domain Vocabulary, Schemas OWL X RDFS DoD Taxonomy UDDI DOM XMLschema WSDL Objects RDF Syntax Structure Dublin Core DDMS TML C2IEDM ebXML Wordnet CYC FoaF Data XML UML MIF Untyped Data Link 16 ASCII Signals Implicit Emerging Networks Net Centric Current Systems Emerging Systems Semantic Interoperability

12. Semantic Interoperability Problems • Shared Knowledge - Semantic interpretation of shared information between systems and also between systems and people is interpreted by humans in a collaborative manner when designing and developing systems, but typically the results of these collaborative semantic interpretations are not explicitly represented in the solution, rather they are implicit in the solution. This results in possible semantic misinterpretations for different system implementations that are supposed to have a common semantic interpretation of shared information. • Situational Context – Without understanding context of a particular user’s situation, the user bears the burden and complexities of discovering and selecting appropriate system capabilities and desired information. In contrast context knowledge defining the relevant information required for a specific situation and perspective can be used to personalize a system’s response more appropriate to the user in a current situation. It also defines the situation and the domain knowledge important to it. Context theory and context aware applications are being developed to enable adaptation of system behavior to a participant’s context. • Closed Semantic Network - assumes implicit semantics achieved through human interpreted specifications and related design activities. Systems within closed environments interoperate reasonably well as long as the operating environment, the expected use of the systems, and the system definitions themselves are consistent over time with little change. If any of these conditions are modified than the original semantic interpretations about system functionality, information exchanged, and expected behaviors have to be reevaluated. • Open Semantic Network – allows for heterogeneous semantic environment with capability to extend additional semantic definitions. Problems of ontology harmonization, varying levels of expressibility, different models, different intentions and context Semantic Interoperability

13. Client –web services interactions (Client interprets <SellStock> as post offer to sell, while web service interprets as sell at any price.) App – App operations and data exchanges (AppX interprets <Stock> as symbol of stock, while App Y interprets <Stock> as CurrPrice) Use of API interfaces (API labels passed argument for operation as “Stock”, with no semantic definition, object implementing API interprets “Stock” as quantity of items available in inventory.) Interpretation of Network protocols by network elements (Each NE interprets the protocol message according to the role it has in a Closed World Network and a shared protocol specification – Semantic intent of messages across Closed World Networks require reinterpretation in gateways) App interpretation of database information (Subtle misinterpretation of database meaning by new App results in inconsistent database state due to inappropriate updates by new app e.g., HealthcareProvider updates PatientStatus due to diagnostics, while FinanceAdministration updates PatientStatus due to InsuranceConstraints. In this case PatientStatus was originally used for health status, not Insurance status. Web services search – UDDI service profiles have no associated schemas or ontologies, resulting in semantic misinterpretation of keyword searches for services Information search – Without taxonomies of knowledge domain profiles searches will rely on data mining algorithms with too many non-relevant results Information integration and merging across apps, systems and databases – Biggest problem of semantic interoperability since multiple specifications and enterprise purposes are involved, as well different syntax information structures and constraints. Exchanged XML documents – only contains simple or complex data element definitions, no relationships between data elements or constraints about when data element instances can be created WSDL web service specifications - no semantics associated with WSDL service definitions, such that applications would have to be written to each WSDl service vocabulary, even when in the same application domain. Semantic Interoperability Errors Occur Everywhere Semantic Interoperability

14. Closed Solutions(Implicit Semantics) • Closed solutions are characterized by static aspects with implicit semantic interoperation between like systems due to: • explicit semantics defined in the requirements and design stage, • implementations having weak traceability to requirements and design semantics • Results in brittle and complex semantic interoperability specifications not easily modifiable for interoperation with other systems, nor easily evolvable with changing requirements Semantic Interoperability

15. Open Solutions(Explicit Semantics) • Open solutions are characterized by dynamic aspects that enable explicit semantic interoperation at multiple levels of interaction between different systems due to: • explicit semantics defined and accessible in all phases • sharing of intensional semantic knowledge about context, intentions, actions, capabilities, commitments and environment • simple universal communications speech acts enabling collaboration between systems • separation of semantic concerns and explicit representations of knowledge and system actions or services • ability to extend the universe of explicit knowledge used by systems as new requirements and capabilities are desired • ability to discover, access, and share explicit knowledge in multiple domains (context, capabilities, environment perspective, commitments, …) • ability to dynamically marshal resources to broker semantics • Results in extensible and robust interoperability solutions resulting from dynamic integration of disparate systems within a common semantic interoperability framework Semantic Interoperability

16. A Universal Semantic Interoperability Framework Semantic Interoperability

17. Universal Semantic Interoperability Model ENVIRONMENT ENVIRONMENT Goals Collaboration, Role Goals Reasoning Reasoning Reasoning Reasoning COGNITIVE Context Context Perspective, Situation Perception Perception Knowledge Knowledge Shared Domain Knowledge Intentions, Services Intentions, Services Request, Committment REACTIVE World Modifying Actions World Modifying Actions Speech Acts Speech Acts Purposeful Communications ENVIRONMENT ENVIRONMENT Semantic Interoperability

18. Transformation from Implicit to Explicit Semantic Interoperability Full Semantic interoperability is enabled by embedding and sharing explict semantic representations of agent, system and environment goals, context, intentions, actions, available services, domain knowledge, and speech acts Semantic Interoperability

19. Semantic Interoperibility Model • SIOPM = <SM, WFF>, set of semantic models and well formed expressions entailed by each model • SM = <SM1, …,SMn>, set of semantic models used by agents 1, …, n • SM = <D, G, V, I, L, A, wff>, semantic model tuple D = Domain and individuals in domain G = Grammar defining syntax of well formed expressions, wff V = domain vocabulary for domain I = Interpretation function mapping domain vocabulary terms to domain individuals L = Logic defining rules of reference and entailment for wff A = Axioms predefined in model SM |= wff , wff entailed by Model M, ( |= Entailment operator) Semantic Interoperability

20. Semantic Interoperibility Model • Mutual Semantic Entailment Between Pairs of Agents Ai and Aj • Mi  Mj   • Mi  Mj |= wff Mutual Semantic Entailment • Non-Mutual Semantic Entailment Between Pairs of Agents Ai and Aj • Mi  Mj  • (Mi|= wff)  (Mj|= wff) • Mi  Mj   • Mi  Mj |  wff Semantic Interoperability

21. Semantic Interoperability Intersection Knowledge Information Actions/Services Goals Context Communications Semantic Interoperability

22. Semantic Interoperability Principles • Interoperability between systems and agents is purposeful and informed by goals, contexts, and shared semantic domain knowledge models(whether explicit or implied). • actual world modifications are achieved through intentional actions . • sharing of semantic environment knowledge provides a ‘situated real world’ perception to enable better decisions about what actions or services are required to achieve goals (mapping of sensed data to perception concepts) • Goals guide selection of intentions and execution of actions • An extensible network of semantic services with explicit semantic representations enables interoperability independent of platforms and technology implementations, and provides a foundation for intentional actions within a purposeful, cognitive interoperable framework • Communications occurs within few universal intentional categories (Speech Acts – request knowledge, commit to action, request action, … ) • Context defines relevant domain knowledge for a specific situation • Useful Knowledge is organized in semantic domain models Semantic Interoperability

23. Cognitive Semantic Interoperability Model ENVIRONMENT ENVIRONMENT Goals Collaboration, Role Goals Reasoning Reasoning Reasoning Reasoning COGNITIVE Context Context Perspective, Situation Perception Perception Knowledge Knowledge Shared Domain Knowledge Intentions, Services Intentions, Services Request, Committment REACTIVE World Modifying Actions World Modifying Actions Speech Acts Speech Acts Purposeful Communications ENVIRONMENT ENVIRONMENT Semantic Interoperability

24. Agent Cognitive Semantic Model Goals (Objectives, Guidance) Context (Situational Knowledge, Constraints) Intensional Logical Reasoning (Decisions, Inferences) Intentions (Tasks, Workflows, Services) Semantic Knowledge Perceptions Atomic Actions Communicating Speech Acts Environment Data, Sensors Communicating Speech Acts World Modifying Actions Semantic Interoperability

25. Implicit Semantic Knowledge Goals Collaboration, Role Goals Never explicitly defined in system, only implicitly by requirements COGNITIVE Never explicitly defined in system, only implicitly by requirements Context Context Perspective, Situation Implicit semantic models by system designer, at most explicit data element structure. Knowledge Knowledge Shared Knowledge Usually defined by very few app specific msg types, not universal Intention Intention Request, Committment REACTIVE Speech Acts Purposeful Communications Speech Acts Typically implemented via app specific protocols, not universal Semantic Interoperability

26. Explicit Services and Universal Speech Acts, No Explicit Semantics ENVIRONMENT ENVIRONMENT Goals Collaboration, Role Goals Reasoning Reasoning Reasoning Reasoning COGNITIVE Context Context Perspective, Situation Perception Perception Knowledge Knowledge Shared Knowledge Intentions, Services Intentions, Services Request, Committment REACTIVE World Modifying Actions World Modifying Actions Speech Acts Speech Acts Purposeful Communications ENVIRONMENT ENVIRONMENT Semantic Interoperability

27. Explicit Semantic Knowledge, Services, and Speech Acts ENVIRONMENT ENVIRONMENT Goals Collaboration, Role Goals Reasoning Reasoning Reasoning Reasoning COGNITIVE Context Context Perspective, Situation Perception Perception Knowledge Knowledge Shared Knowledge Intentions, Services Intentions, Services Request, Committment REACTIVE World Modifying Actions World Modifying Actions Speech Acts Speech Acts Purposeful Communications ENVIRONMENT ENVIRONMENT Semantic Interoperability

28. Explicit Context Knowledge ENVIRONMENT ENVIRONMENT Goals Collaboration, Role Goals Reasoning Reasoning Reasoning Reasoning COGNITIVE Context Context Perspective, Situation Perception Perception Knowledge Knowledge Shared Knowledge Intentions, Services Intentions, Services Request, Committment REACTIVE World Modifying Actions World Modifying Actions Speech Acts Speech Acts Purposeful Communications ENVIRONMENT ENVIRONMENT Semantic Interoperability

29. Explicit Goal Knowledge ENVIRONMENT ENVIRONMENT Goals Collaboration, Role Goals Reasoning Reasoning Reasoning Reasoning COGNITIVE Context Context Perspective, Situation Perception Perception Knowledge Knowledge Shared Knowledge Intentions, Services Intentions, Services Request, Committment REACTIVE World Modifying Actions World Modifying Actions Speech Acts Speech Acts Purposeful Communications ENVIRONMENT ENVIRONMENT Semantic Interoperability

30. NCOIC Integrated Ontology Semantic Interoperability

31. NCOIC Integrated Knowledge Base – An NCOIC Ontology • Create an integrated NCOIC knowledge Base that can be used by customers and member companies • Create an NCOIC ontology that can be constructed from FT and WG ontologies to unify the NCOIC work products • Provide a map of Network Centric Operation aspects • Incorporate NCOIC Lexicon • Capture descriptive knowledge about NCO aspects • Map current NCOIC efforts against NCOIC ontology • To provide a context for research efforts and discussion • To identify shortcomings and candidate areas for research • Enable evaluation of Customer Requirements and force initiatives against Net Centric Aspects and NCOIC work products • Identify Specify Interoperability patterns, their structural solutions and their relationship to NCOIC work products • Enable characterization of each solution using NCO evaluative and descriptive models • Create manageable and scalable NCOIC ontology that can evolve • Expand to capture and influence Customer requirements specifications • Capture the operational space Semantic Interoperability

32. NCOIC SII WG Work ProductKnowledge base • Each WG Product has a document and associated Ontology to enable incorporation into a larger model • SII WG Product Concepts • SII Integrated Ontology enable dependent relationships to be made between: • NCO Tenets, Reference Models, • NCO SCOPE Model and its Descriptive Dimensions • Interoperability Causes • Interoperability Patterns – Focus on Service and Information • PFCs • Customer Requirements and Capabilities • Open Standards • Able to be component part of NCOIC Level Integrated Knowledge Base and Ontology Semantic Interoperability

33. Approach Govt and Member Companies, Prod Vendors Tools (Industry, Vendor, Govt.) NCOIC Integrated KB and Ontologies Customers - ETE Capabilities • Architects • - Patterns • SCOPE Model • Interop Problems • Engineers • PFCs • Profiles Vendors - COTs/Gots FT & WGs Semantic Interoperability

34. NCOIC Product Map (SII WG Perspective) NCOIC Lexicon NCOIC Integrated Ontology NCOIC Integrated Knowledge Base Open Standards • Customer Reqts • DAR,CADM, DAP • DoDAF to DRL • JCIDS • PIM • NCOW RM • Capital Planning • PPBE • Acquisition • BCIDS • Net Ready KPPs • KIPs • DISRonlineProfiles Open Standards Ontology IA Mobility NCAT SII Integrated Ontology SII Integrated Knowledge Base • SII WG • Interoperability • Patterns • -Information Exchange, Semantics • Services, Mediation • Msg Content Transformation • Collaboration, Workflow • Discovery, Context Awareness • Autonomicity, Management PFCs PFC Ontology Cust Reqts Ontology NCO Interop Pattern Ontology Interoperability Causes Document NCOIC SCOPE Document NCO Tenets Ontology Interop Ontology SCOPE Ontology NCO Tenet Ontology Semantic Interoperability

35. Integrated NCOIC Product Map (Proposal) NCOIC Lexicon Building Blocks NCOIC Integrated Ontology NCOIC Integrated Knowledge Base Open Standards Building Blocks Ontology • Customer Reqts • DAR,CADM, DAP • DoDAF to DRL • JCIDS • PIM • NCOW RM • Capital Planning • PPBE • Acquisition • BCIDS • Net Ready KPPs • KIPs • DISRonlineProfiles Open Standards Ontology NCAT • Interoperability • Patterns • -Information Exchange, Semantics • Services, Mediation • Msg Content Transformation • Collaboration, Workflow • Discovery, Context Awareness • Autonomicity, Management PFCs Mobility PFC Ontology IA Cust Reqts Ontology NCO Interop Pattern Ontology Interoperability Causes Document NCOIC SCOPE Document NCO Tenets Ontology Interop Ontology SCOPE Ontology NCO Tenet Ontology Interactions between WGs And FTs not defined here Semantic Interoperability

36. Recommendations • Unify the NCOIC knowledge and products using the NCOIC KB and Ontologies • Align with similar customer efforts in KB • Foundation for collaborative engineering • Establish a group to manage the NCOIC Ontology and KB • Each WG has one focal person for input and vetting • currently being done by SII WG • Training for Semantic Information Capture • Tools and commercial hosting platforms for NCOIC ontology (Infrastructure Recommendation) • Assist NCOIC marketing efforts • Assist engineering efforts • Budget (plan to follow) Semantic Interoperability

37. Emergency Disaster Response Information Coordination Semantics Semantic Interoperability

38. “Many of the problems we have identified can be categorized as “information gaps” – or at least problems with information-related implications, or failures to act decisively because information was sketchy at best. Better information would have been an optimal weapon against Katrina. Information sent to the right people at the right place at the right time. Information moved within agencies, across departments, and between jurisdictions of government as well. Seamlessly. Securely. Efficiently.” The Final Report of the Select Bipartisan Committee to Investigate the Preparation for and Response to Hurricane Katrina Semantic Interoperability

39. Coordination Problems • Lack of organized information focusing on coordination activities and status: • Resources • Participants • Incident resolution • No Common Operating Picture relating evolving overall coordination situation. • Inability to plan specific coordination activities for different disaster scenarios Semantic Interoperability

40. Information and Communication Problems • Focus on data elements rather than model structure and domain • Messages only related to each other by message ID; i.e. patterns of coordination not readily apparent • Semantic descriptions of data elements in message schemas inadequate • No standards used to represent higher levels of semantic expressiveness in data model, e.g. RDF, OWL • Architecture does not specify how information sharing takes place among responders in any dynamic or adaptive manner • No directory structure exists within DMIS to enable service discovery Semantic Interoperability

41. Project • Research and Development of Emergency Disaster Response Information Coordination Semantic (ED-RICS) framework to improve emergency response coordination • Focus on semantic architectural model that creates common operating picture (COP) of evolving emergency response coordination situation • Represent COP as set of discrete semantic coordination patterns (SCP) derived from XML emergency messages • Ontology based network coordination situation analysis identifying coordination anomalies, completion states, resource commitments, and incident focus problems • Technologies include: • EDXL and CAP alert message standards • DHS NRP scenarios • Protégé 2000 ontology tool with OWL plugin • Domain ontologies with non-programmatic concept inferences • Web services • Concepts from knowledge representation and descriptive logic Semantic Interoperability

42. DHS-NRP Scenario Analysis • Scenario 1: Nuclear Detonation – 10-Kiloton Improvised Nuclear Device • Scenario 2: Biological Attack – Aerosol Anthrax • Scenario 3: Biological Disease Outbreak – Pandemic Influenza • Scenario 4: Biological Attack – Plague • Scenario 5: Chemical Attack – Blister Agent • Scenario 6: Chemical Attack – Toxic Industrial Chemicals • Scenario 7: Chemical Attack – Nerve Agent • Scenario 8: Chemical Attack – Chlorine Tank Explosion • Scenario 10: Natural Disaster – Major Hurricane • Scenario 11: Radiological Attack – Radiological Dispersal Devices • Scenario 12: Explosives Attack – Bombing Using Improvised Explosive Devices • Scenario 13: Biological Attack – Food Contamination • Scenario 14: Biological Attack – Foreign Animal Disease (Foot and Mouth Disease) Semantic Interoperability

43. Semantic Interoperability

44. Current Emergency Disaster Response Information Interoperability Network Disaster Management Interoperability Services (DMIS) Disaster Management Interoperability Service (DMIS) Services Emergency Provider Access Directory (EPAD) Emergency Provider Access Directory (EPAD) SOAP, WSDL, HTTP Emergency Data Exchange Language - EDXL Messages Common Alerting Protocol - CAP National Information Exchange Model (NIEM) Data Model Semantic Interoperability

45. Emergency Messaging Languages • CAP: emergency messaging standard used to alert responders and public in general of emergency situations as they occur. • EDXL-RM: messaging standard used to convey information regarding emergency specific resources. • EDXL-DE: emergency messaging standard used as container for CAP and EDXL-RM messages. EDXL-DE may also contain emergency data not otherwise included in CAP or EDXL-RM messages. Semantic Interoperability

46. ED-RICS Capabilities Provides universal shared information analysis through creation of common operating picture (COP) of all coordination activities, including: • Committed resources • Responder locations with respect to incident area • Coordination activity completion status • Anomaly analysis, such as overcommitted resources, etc. • Interactive execution environment between knowledge framework and responders, response managers, messaging systems, databases, and other personnel and systems Semantic Interoperability

47. Semantic Interoperability

48. Semantic Interoperability

49. Coordination Intersection Information Activities Resources Messages ED-RICS Semantic Interoperability

50. Emergency Disaster Response Services and Information Framework Semantic Web Services Data Models • Emergency Situational • Information • Service • Plan Management • Status Monitoring • Situation Analysis • Anomalies Identification • Semantic Data Model for • Emergency Disaster • Planning, Monitoring, Analysis New • EDXL-DE OWL CAP OWL EDXL-RM OWL (DMIS) Current NIEM (EPADS) Emergency Data Exchange Language - EDXL SOAP, WSDL, HTTP Common Alerting Protocol - CAP Semantic Interoperability