Ontologies for Semantically Interoperable Systems
| Dr. Leo Obrst | |
| MITRE | |
| Center for Innovative Computing & Informatics | |
| Information Semantics | |
| Lobrst@mitre.org | |
| January 15, 2004 | |
| The Problem | ||
| Tightness of Coupling & Explicit Semantics | ||
| Semantic Integration Implies Semantic Composition | ||
| Dimensions of Interoperability & Integration | ||
| Ontologies | ||
| The Ontology Spectrum | ||
| What are Ontologies? | ||
| Levels of Ontology Representation | ||
| What Problems do Ontologies Help Solve? | ||
| Ontologies for Semantically Interoperable Systems | ||
| Enabling Semantic Interoperability | ||
| Examples | ||
| Visions | ||
| What do We Want the Future to be? | ||
| With the increasing complexity of our systems and our IT needs, and the distance between systems, we need to go toward human level interaction | |
| We need to maximize the amount of semantics we can utilize and make it increasingly explicit | |
| From data and information level, we need to go toward human semantic level interaction |
Tightness of Coupling & Semantic Explicitness
Semantic Interoperability: Tight to Loose Coupling
| Tight coupling: applies to databases, systems | ||
| Same address space, same process space, same operating system, same machine | ||
| Semantic compacts can be made because semantics stays in the minds of the developers who agree | ||
| Loose coupling | ||
| Different platforms, networks, anywhere on Internet | ||
| Semantics must be explicit: agents, programs need to interpret the semantics directly, to interoperate semantically | ||
| Levels: systems of systems, enterprise, community, value chains/pipes | ||
| Ontologies (explicitly represented, logical semantics): increasingly needed the higher you go | ||
Semantic Integration Implies Semantic Composition
Dimensions of Interoperability & Integration
Semantic Interoperability/Integration Definition
| To interoperate is to participate in a common purpose | ||
| Operation sets the context | ||
| Purpose is the intention, the end to which activity is directed | ||
| Semantics is fundamentally interpretation | ||
| Within a particular context | ||
| From a particular point of view | ||
| Semantic Interoperability/Integration is fundamentally driven by communication of purpose | ||
| Participants determined by interpreting capacity to meet operational objectives | ||
| Service obligations and responsibilities explicitly contracted | ||
Architecture: Ontology & Applications
What Problems Do Ontologies Help Solve?
| Heterogeneous database problem | ||
| Different organizational units, Service Needers/Providers have radically different databases | ||
| Different syntactically: what’s the format? | ||
| Different structurally: how are they structured? | ||
| Different semantically: what do they mean? | ||
| They all speak different languages (access, description, schemas, meaning) | ||
| Integration: rather than N2 problem, with single, adequate Ontology reduces to N | ||
| Enterprise-wide system interoperability problem | ||
| Currently: system-of-systems, vertical stovepipes | ||
| Ontologies act as conceptual model representing enterprise consensus semantics | ||
| Relevant document retrieval/question-answering problem | ||
| What is the meaning of your query? | ||
| What is the meaning of documents that would satisfy your query? | ||
| Can you obtain only meaningful, relevant documents? | ||
Enabling Semantic Interoperability
| Semantic Interoperability is enabled through: | ||
| Establishing base semantic representation via ontologies (class level) and their knowledge bases (instance level) | ||
| Defining semantic mappings & transformations among ontologies (and treating these mappings as individual theories just like ontologies) | ||
| Defining algorithms that can determine semantic similarity and employing their output in a semantic mapping facility that uses ontologies | ||
| The use of ontologies & semantic mapping software can reduce the loss of semantics (meaning) in information exchange among heterogeneous applications, such as: | ||
| Web Services | ||
| E-Commerce, E-Business | ||
| Enterprise architectures, infrastructures, and applications | ||
| Complex C4ISR systems-of-systems | ||
| Integrated Intelligence analysis | ||
Semantic Interoperability, Integration: Multiple Semantics
| Multiple contexts, views, application & user perspectives | ||
| Multiple levels of precision, specification, definiteness required | ||
| Multiple levels of semantic model verisimilitude, fidelity, granularity | ||
| Multiple kinds of semantic mappings, transformations needed: | ||
| Entities, Relations, Properties, Ontologies, Model Modules, Namespaces, Meta-Levels, Facets (i.e., properties of properties), Units of Measure, Conversions, etc. | ||
Simple Example: Semantics of Date Across Applications
| System1 Instance of Concept: Date1 | ||
| Attribute: YR = Int 1 | ||
| Attribute: MO = String “Aug” | ||
| Attribute: DY = Int 12 | ||
| System2: Instance of Concept = Date2 | ||
| Attribute: DayOfWeek = Sunday | ||
| Attribute: ActualDate = | ||
| String “12082001” | ||
| Semantically Equivalent? Then How? | ||
Simple Example: Semantics
of Location Across Applications
| System1 Instance of Concept: Location1 | ||
| Attribute: SourceDeadReckoning = A | ||
| Attribute: SourceDRLatitude = B | ||
| Attribute: SourceDRLongitude = C | ||
| Attribute: TargetDRBearingLine = D | ||
| Attribute: TargetDRAltitude = E | ||
| Attribute: ActualMeasuredAltitude = F | ||
| Attribute: PositionLine = G | ||
| System2: Instance of Concept: Location2 | ||
| Attribute: Address = H | ||
| Attribute: City = I | ||
| Attribute: StateProvince = J | ||
| Attribute: Country = K | ||
| Attribute: MailCode = L | ||
Electronic Commerce
Example:
One Company
Emerging XML Stack Architecture for the Semantic Web + Grid + Agents
| An ontology allows for near linear semantic integration (actually 2n-1) rather than near n2 (actually n2 - n) integration | ||
| Each application/database maps to the "lingua franca" of the ontology, rather than to each other | ||
Vision: Semantically Interoperable Systems
What do we want the future to be?
| 2100 A.D: models, models, models | |
| There are no human-programmed programming languages | |
| There are only Models |
| Questions? lobrst@mitre.org | ||
| Shameless Plug: | ||
| The Semantic Web: The Future of XML, Web Services, and Knowledge Management, -- Mike Daconta, Leo Obrst, & Kevin Smith, Wiley, June, 2003 | ||
| http://www.amazon.com/exec/obidos/ASIN/0471432571/qid%3D1050264600/sr%3D11-1/ref%3Dsr%5F11%5F1/103-0725498-4215019 | ||
| Contents: | ||
| What is the Semantic Web? | ||
| The Business Case for the Semantic Web | ||
| Understanding XML and its Impact on the Enterprise | ||
| Understanding Web Services | ||
| Understanding the Resource Description Framework | ||
| Understanding the Rest of the Alphabet Soup | ||
| Understanding Taxonomies | ||
| Understanding Ontologies | ||
| Crafting Your Company’s Roadmap to the Semantic Web | ||