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DSC Ontology Work

DSC Ontology Work

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DSC Ontology Work

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  1. 11:00 Self-Introductions11:15 Report on ontology-based data integration work in DCGS-A--- Goals and methodology--- Practical experience  and results so far--- Risks and problems12:15 Lunch (Brown Bag Lunch?)13:00 Discussion of NSA work and goals  (to be expanded)--- ontology alignment / management--- OMaaS (Object Management as a Service)14:00-15:30 Discussion of how collaborative ontology effort (e.g. betweenI2WD and NSA)  will work in practice--- how to ensure consistency  (architectural and content)--- how to address governance --- business development and work flow

  2. DSC Ontology Work Goals and methodology Practical experience  and results so far Challenges 02/05/13

  3. Goal: To realize Horizontal Integration(HI) of intelligence data HI =Def. the ability to exploit multiple data sources as if they are one • Problem: the data coming onstream are out of our control • Any strategy for HI must be agilein the sense that it can be quickly extended to new zones of emerging data according to need • Ontology can provide the needed agility and (incremental approach to) comprehensiveness

  4. The Business Case • Huge resources are wasted as multiple different agencies create lexicons, glossaries, data models, messaging and exchange standards with the same or closely overlapping coverage. • Additional resources are wasted in creating mappings between these artifacts, and in maintaining them in light of new needs and challenges. These mappings always fail. • A sensible solution must incorporate an evolutionary process which will ensure that artifacts used to manage data converge over time.

  5. Methodology: Create an agile strategy for building ontologies within a Shared Semantic Resource (SSR) and apply and extend these ontologies to annotate new source data as they come onstream • Strategy pioneered in biomedical and other scientific fields: leaves data as they are, and incrementally tags data sources with terms from a growing, consistent, non-redundant set of ontologies • Problem: Given the immense and growing variety of data sources, the development methodology must be applied by multiple different groups • How to manage collaboration? This is what this meeting is about

  6. Current State 2010-2011 • Architectural implementation (DRIF) to create the Dataspace (a cloud of intelligence data) = lossless representation of sources with their native semantics • Initiated Semantic Enhancement (SE) = using ontologies to annotate these native semantics • Demonstration of use of SE to index and query the content of the Dataspace 2012 • Created methodology and architecture for ontology development • Initiated Shared Semantic Resource (SSR); created suite of prototype ontologies enabling SE also outside the Dataspace • CUBRC: demonstrated ability to leverage SE for analytics • Priming potential users of SE (in DoD CIO, NGA, JIEDDO, TRADOC …) 2013- • Milportal • Event Reporting Application • Initial negotiations with other agencies and groups

  7. Ontologies We Have (Feb. 2013) • Physical Artifact, including Infrastructure, Facility, Vehicle, Weapon • Information Artifact, including Report, Image, Map • Event, including Military, Criminal, Economic, Political, Religious, Social • Human Physical Characteristics • Agent, including Person, Organization, Social Network • Geospatial • Time

  8. MilPortal http://milportal.ncor.buffalo.edu

  9. Next step slide from Margaret Storey

  10. slide from Margaret Storey

  11. Work plans 1: For DSC Cloud (on-going) • Perform needs analysis: Review DCGS-A Logical Data Model and schemas of other DSCG-A data sets; in each case, examine content and establish what terms are needed to ensure sufficient coverage for SE; • Where these terms already exist within the SSR, check that definitions exist and that these definitions are adequate • Where the terms do not exist within the SSR, create new terms (or new ontologies), with appropriate definitions as necessary to fill gaps. • Use the terms in 2. and 3. to annotate the corresponding entries in the data models to effect horizontal integration; • With each new expansion in scope of DSGS-A data sets, iterate the above as needed. • In addition, we are engaging in documentation of the methodology as here described, and in dissemination and training.

  12. Work plans 2: For interagency collaboration Step 1: Initiating interagency collaboration in the service of horizontal integration of intelligence data Identify candidate teams / agencies Establish collaboration with one or more specific teams • Formulate and ratify agreements as appropriate • Create work plan and identify funding needs • Perform risk assessment Step 2: Establishment of the inter-agency ontology development process Examples of types of work to be performed would include: • Create governance infrastructure • Establish needed technological support • Implement workflow • Conduct training in methodology where needed • Explore opportunities for inter-agency HI of data • Begin application to relevant agency data models of the SE strategy • Dissemination of results in a form which will allow improved systems to perform enhanced analytics exploiting semantic interoperability.

  13. The SSR methodology and governance is neutral as to how SSR-annotations are used • Currently SE of DSC enhancement only integration through improved indexing/search capability • On CUBRC project, we have much more, including ontology-based reasoning. • In the future we will have for DSC enhancement applied • to multiple models such as LDM • to unstructured text • see the various methodology documents provided so far

  14. Event Reporting Application (EvR) • Aggregates content from various report generating applications (CPOF, TIGR, TIGR with MAPHT extension) • The underlying data model contains nearly 500 terms (e.g. ReportName, EventName, DeclassificationEvent) • The semantics of the data model seems similar to that of a relational model

  15. EvR Data Model as Relational Database • Hierarchies of events and units are referenced by EventType and UnitEchelon columns, but this alone provides no capability for traversing these hierarchies • A report is related to both a report name and a report unit by the inclusion of appropriately named columns, but what is the difference between these relationships? • Our approach shows how to express the difference, e.g. between report-to-event and event-to unit relationships

  16. Enhancing the vocabulary of the EvR Data Model • Semantic enhancement (SE) amounts to associating a database field to a whole knowledge system enabling machines to process data … • “vertically” e.g. by traversing the echelon command hierarchy and • “horizontally” e.g. by following specified relations between units and events. • SE separates semantics from structure, reducing maintenance costs as source databases no longer have to be modified each time we improve our understanding of reality • The ontology tags move with the data …

  17. Enhancing the vocabulary of the EvR Data Model Progress to Date • Two techniques of SE are available • Partial enhancement (now being used in the DSC) associating ontology label terms with EvR terms • Full enhancement (not yet implemented in the DSC) aligning terms from the EvR to assertions using terms and relations from SSR ontologies • At present the current ontologies… • provide 70% coverage for partial enhancement • provide 38% coverage for full enhancement • plan for extending the ontologies to raise the coverage for the EvR to 90% and 60% respectively by end of February

  18. Enhancing the vocabulary of the EvR Data Model Event Reporting Term Full Enhancement via Ontology Assertion(s) Partial Enhancement via Ontology Label

  19. Enhanced EvR Data as a Graph Event ID Event Type Unit Identification Code Report ID is_subclass_of designates part_of designates designates Event describes Report participates_in Unit has_output designates controls designates Act of Reporting Report Name Command Unit affiliated_with Unit Name agent_in Report Unit Service shows how full annotation provides the semantics missing from the EvR relational model described above

  20. Event ID Event Type Unit Identification Code Report ID is_subclass_of designates part_of designates designates Event describes Report participates_in Unit has_output designates controls designates Act of Reporting Report Name Command Unit affiliated_with Unit Name agent_in Report Unit Service • relationships between • event type and echelon • report name and reporting unit • are made distinct and machine processable

  21. Challenges • Challenges to Horizontal Integration in general • Too many lexicons • The scope of the domain: signal, sensor, image, … intelligence about the whole world • Our solution • Incremental extraction and sanitization, and creation of content • Distributed collaborative development • Strong methodology • Challenges for our solution • Governance and management of ontology development to ensure consistent evolution • Lack of expertise • For ontology development and management • For annotation • Success will breed failure

  22. We have these precursor ontologies • The prototype ontologies in the existing SE (Malyuta-Salmen) helped indexing • The ontologies we now have are much better and more than these prototypes • How should we implement them re Event Reporting Ontology, global graph, etc. …? • Next steps with I2WD

  23. Our ideas are being heard • 2 classes of collaborator: observers and partners • companies such as DataTactics are realizing that in case of success this provides huge potential business benefits. • (DT invited our team to talk at the Ontology Summit they have called on Feb. 12, devoted to the development of shared semantics)

  24. Peter Morosoff and Bill Mandrick • How to create a strategy?