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Ontology Summit 2011: Application and Use Cases synthesis     (1)

Ontology, semantics, and knowledge technologies benefit and add value across a very broad spectrum of applications:     (1A)

  • At one end of the solution spectrum, is the low-hanging fruit. Here applications exist with proven benefits, low risk, and real value that can be realized with off-the-shelf tools and solutions used by subject matter experts, business users, and consumers. Simple solutions to the right problem can bring great value.     (1B)
  • In the mid-range we find categories of applications that address bigger needs and have characteristics best addressed with knowledge-centric approaches. Case examples exist that show solution patterns delivering substantial value. Multiple providers of technologies (products, services) and services have expertise in these areas. Mid-spectrum applications often involve:     (1C)
    • Discovering, connecting, integrating and interpreting information of different kinds, from multiple sources, and at varying scales     (1C1)
    • Aggregating, connecting and orchestrating services, applications, processes, and families of systems     (1C2)
    • Modeling and automating decision rules and decision-making processes     (1C3)
    • Methodologies that realize value from different stages of the solution life cycle.     (1C4)
  • At the other end of the spectrum are cutting edge opportunities, tough problems, and grand challenges that cannot be solved with current information technology approaches. At the frontier, problem characteristics, including issues of scale, systemics, autonomics, security, dynamics, extent and depth of knowledge, and complexity of reasoning as well as time and mission criticality, etc. demand capabilities only possible or economically feasible with knowledge technologies, semantics, and ontologies.     (1D)

When using case examples to make the case for ontology, place emphasis first and foremost on communicating the value to the business. Our template for summarizing the value delivered includes the following topics:     (1E)

  • Challenge �� What is the problem or opportunity being addressed? What is its significance?     (1F)
  • Solution �� What is the solution? What role do knowledge technologies, semantics, and ontology play in it?     (1G)
  • Screen shot and key features �� Give a flavor of the use of ontology in this case.     (1H)
  • Benefits �� What measures were used to demonstrate the value delivered?     (1I)
  • Resource �� What commercial or non-profit entities provided technology products and services used in the case example?     (1J)

Case examples reveal that ontologies solve a broad range of real-world problems. Summit sessions presented twenty case examples of mainstream applications where knowledge technologies, semantics, and ontology are being applied and delivering benefits. This represents only a small sample of available deployments.     (1K)

The following lists the case examples documented in the supporting materials, and highlights value delivered..     (1L)

Apple SIRI     (1M)

Ontology-driven virtual assistant: as a next generation assistance paradigm for smart consumer applications: Ontology-based UI focuses is on task completion, with intent understanding via conversation in context, and where the virtual assistant learns and applies personal information. Key roles for ontology include interface intelligence, unification of domain and declarative task models, semantic autocompletion, and service orchestration.     (1N)

BeInformed     (1O)

Smart knowledge-driven citizen-centric services �� separate the know from the flow, data, and function; everything is a knowledge model and a business rule, no exceptions; business users create rules, application modelers create and configure services, administrators deploy applications. The design is the model, is the application, is the documentation. Fast, lean development. dynamic context-aware processes, operational savings, flexibility, and greatly improved life cycle economics     (1P)

Cambridge Semantics     (1Q)

Do it yourself data exploration and analytics �� a semantic collaboration platform for operational intelligence and advanced analytics; deploys rapidly, integrates with tools that professionals already use, and enable teams to quickly connect all of the data sources and model the workflow needed to and analyze data, investigate processes, and answer questions; empowering subject matter experts and business users leads to 2-10x improvement in time to solution plus flexibility to evolve it rapidly and cost-effectively.     (1R)

Connotate     (1S)

Do-it-yourself semantic agents to discover, aggregate, analyze & report information; anything pointed to in a browser, you can teach a semantic agent to monitor and intelligently process. Agents ��speak�� HTML, XML, RSS, RDF, PDF, database and Excel. Mash-ups create new data by element and schema, in time periods, across sources and time periods, and put data into context. Productivity increases can exceed 2X.     (1T)

Department of Homeland Security     (1U)

(a) DHS infrastructure taxonomy; (b) Complex event modeling, simulation and analysis (CEMSA)     (1V)

Ontologies resolve semantic differences across sources of information and domains allowing reasoning and inference �� to identify for example, for a given emergency situation, default actions, resources, roles/ responsibilities of relevant agencies.     (1W)

EDM Council     (1X)

Standardization of terms and definitions for financial services and a pilot test of the semantic resource as applied to mortgage-backed securities. Automated semantic tagging, indexing and systemic publishing of factual reference data is feasible systemically and vastly more consistent, accurate, and cost-effective than pre-financial meltdown processes. PIlot test demonstrates the viability of tagging financial contracts using standard semantics and identifiers in support of risk analytics.     (1Y)

Franz | AMDOCS     (1Z)

Ontologies for telecom customer relationship management: semantic technology enabled, closed-loop, self-learning system lets customer service see what happens, when it happens, understand what it means to the business, and take action and enforce business policy �� automatically, intelligently and in business real time. Eliminated system and agent diagnosis time; decrease average handling time; improve agent and customer satisfaction.     (1AA)

IBM & U Maryland     (1AB)

Dr. Watson Project �� After Jeopardy, Watson goes to med school: Previous applications of expert systems and AI in medicine have been impressive, but limited. In the post-Watson era potential for broad enhancement of medical practice seems likely, if challenges can be overcome.     (1AC)

Innovative Query     (1AD)

(a) Content intelligence and smart applications; ontology integrates structured and unstructured information, improves search, discovery and collaboration; and filters information to user need and context. (b) Semantic BI for blogging: ontology used to semantically index information from structured and unstructured sources, both internal and external, enabling custom alerts, and more precise and rapid response to social media.     (1AE)

Mayo Clinic     (1AF)

Relationships among biomedical ontologies and classifications: Ontologies bridging and interrelating medical and scientific disciplines will play an integral role in the evolution of medicine from practice-based evidence to evidence-based practice.     (1AG)

Model-driven Development     (1AH)

Architectures and ontologies for business value: Architectures and ontologies are mutually supportive.     (1AI)

Recognos Financial     (1AJ)

Better information access with semantics for search, navigation, query & question answering �� case example from the mutual fund industry: Concept-based, faceted navigation uses semantic analysis of content to reduce cognitive burden for users including extract specific data from tables (e.g., the amount of a specific type of fee). Question answering allows users to express questions in their own words and get the right answer. Automated semantic indexing and analysis is more consistent, accurate, and cost-effective than comparable manual methods. Since, 80% of all data in organizations is unstructured, semantic applications within government and industry are massive.     (1AK)

Revelytix     (1AL)

DoD knowledge-centric information webs & process interoperability: DoD attempted to build a data warehouse to connect HR systems and information across the Department. After 11 years and $1B dollars expended, had nothing to show for it. After everything else had failed, they decided to build a semantic information web to connect existing systems of record using a common domain ontology connected to relational mapping and source (metadata) ontologies. After 9 months (and very modest dollars expended), DoD demonstrated a solution.     (1AM)

Sallie Mae     (1AN)

Integration of multiple systems from multiple companies: ontology provides unifying model across diverse systems while supporting tailored views and facets of this subject matter to different subject matter experts.     (1AO)

Sandpiper     (1AP)

Semantic technology in rental product marketing: ontologies power semantic search and search engine optimization to improve user experience and business outcomes.     (1AQ)

Semantic Arts     (1AR)

Applying semantics to enterprise systems - Proctor and Gamble case study: Ontologies provide a practical way to integrate research findings across disciplines.     (1AS)

Top Quadrant     (1AT)

Valuing the harvest from using ontologies (a medley of case examples): Ontology used for enterprise vocabulary management, semantic-xml message building, data integration, and enterprise architecture. Graph data model (subject, predicate, object) provides canonical data for connecting data silos into information webs.     (1AU)

Trigent Software     (1AV)

(a) Ontology and rules provide rapid natural language understanding; (b) Ontology and rules drive mass customization of vehicles -- Ontology and rules driven configurator and custom manufacturing process identifies best parts, assemblies, availabilities, and plant schedule to meet promised delivery date error-free. Fast rules engine handles 600K rules with average of 24 condition elements and can configure a truck in under 10 seconds on a laptop.     (1AW)

Visual Knowledge     (1AX)

Policy-driven compliance, risk, and change management pilot: captures regulatory mandates, maps them to policy documents, then to semantic models defining schemas, processes, and decision-making rules, to deployed operational systems and procedures, to analytics that track, assess, and report human and system behavior and ensure compliance.     (1AY)

Model-driven framework for process deployment with extreme traceability: Executable knowledge models specify project goals, roles, methods, activities, deliverables, quality, and resources and enable tool interoperability, process automation, and end-to-end traceability at the level of individual concepts. Result can be up to 3-10X faster concept to deployment, with up to 3-10X reduction in project costs.     (1AAA)

Where do case examples show knowledge technologies, semantics, and ontology add value?     (1AAB)

Patterns of value delivery that emerged from these case examples have several dimensions worth noting:     (1AAC)

  • Knowledge-centric approaches and ontology-driven solutions were associated with both development and operational gains in performance and lifecycle economics.     (1AAD)
  • Knowledge technologies and knowledge-centric approaches to knowledge work accelerated the time from idea (or need) to a solution.     (1AAE)
  • Knowledge technologies enabled new methodologies and concepts of operation that resulted in significant productivity gains. Fewer people could accomplish the same work significantly faster and do-it-yourself empowerment of subject matter experts and business users resulted in huge improvements in cycle times and service levels.     (1AAF)
  • Knowledge technologies enabled new solution concepts Knowledge-driven solutions could be documented where the design is self-documenting, the execution is self-explaining, the solution can be easily modified, simulated and updated, and total cost of ownership decreased by more than 50% compared to current IT solutions.     (1AAG)
  • Knowledge technology based solutions were more flexible and agile than IT-based solutions when it comes to making changes and evolving capabilities.     (1AAH)
  • Knowledge technologies and ontology were used to add intelligence to the user interface (UI) in order to increase relevance, helpfulness, utility, and pleasure as experienced by the user.     (1AAI)

Case Study Summaries     (1AAJ)

Each Case Study participant was asked to provide a grid on one slide, outlining the business problem, the solution, key features (or screen shot) and business benefits. The aim of this was to be able to identify what sort of "Ontology" this was in terms of the application framework once this was completed, and what metrics (if any) were avilable to determine the business benefits.     (1AAK)

Each Case Study     (1AAL)

Virtual assistant as a next UI paradigm Apple Siri From the following presentation: Harvesting the Business Value of Ontologies: Recent Case Examples     (1AAQ)

Challenge     (1AAR)

Key Ontology Features     (1AAT)

  • Ontology-driven virtual assistant: as a next generation assistance paradigm for smart consumer applications:     (1AAU)
  • Ontology-based UI focuses is on task completion, with intent understanding via conversation in context, and where the virtual assistant learns and applies personal information.     (1AAV)
  • Key roles for ontology include interface intelligence, unification of domain and declarative task models, semantic autocompletion, and service orchestration     (1AAW)

Business Benefit     (1AAAB)

Challenge     (1AAAG)

  • Multiple systems and sources of knowledge in different parts of the enterprise, owned by different communities of practice.     (1AAAH)
  • Gaining time and commitment from subject matter experts to ensure completeness of the model.     (1AAAI)
  • Different groups see different shades of meaning and application for similar terms, in different contexts.     (1AAAJ)
  • Needs a unifying approach supporting local views     (1AAAK)

Key Ontology Features     (1AAAL)

  • Facilitation of knowledge gathering using ontology engineering methods.     (1AAAN)
  • Formal ontology notation for single ontology, while presenting views and facets of this to subject matter experts.     (1AAAO)
  • Curation of the ontology     (1AAAP)

  • Best use of subject matter experts���� time and resources     (1AAAR)
  • Curatorship of Enterprise Semantic Architect ensures quality, consistency and completeness of the ontology     (1AAAS)
  • Collaboration in industry standardization efforts (e.g. EDM Council), via common semantics     (1AAAT)
  • Ensures that the knowledge captured at Sallie Mae is taken forward to industry-wide standardization efforts which we can then use     (1AAAU)

Challenge     (1AAAW)

  • Industry standardization of terms and definitions     (1AAAX)
  • Integration of multiple sources and feeds into disparate database structures     (1AAAY)
  • Even a small financial firm has 50 ��C 100 separate systems each with its own data model     (1AAAZ)
  • Tried: XML (MDDL); UML data models (ISO 20022)     (1AAAAA)
  • Industry response: ����We need semantics     (1AAAAB)

Key Ontology Features     (1AAAAC)

  • SMEs understood the format and contributed new knowledge on e.g. exotic structured finance     (1AAAAQ)
  • Answered industry call for standardization of meaning     (1AAAAR)
  • Industry applications including mapping, master data models, messaging     (1AAAAS)
  • Atomic building blocks means flexibility in defining novel financial products     (1AAAAT)
  • Traction from regulators, for tagging of documents at source, reporting, systemic risk oversight     (1AAAAU)

Key Ontology Features     (1AAAAZ)

Business Benefit     (1AAAAAH)

Key Ontology Features     (1AAAAAT)

but that is a choice of the application     (1AAAAAV)

pictures, video, et.     (1AAAAAX)

relationships, roles, generalizations, instantiations, characteristics, attributes, and units of measure     (1AAAAAZ)

questions), parse and map the various valid constructs to semantic items in an Ontology (we call this mapping the "meaning" of the text)     (1AAAAAAC)

combinatorics of language constructs that represent the mapping as having an equivalent ����meaning map����     (1AAAAAAE)

identify possible meaning matches     (1AAAAAAG)

relates to the original text along with hyperlinks     (1AAAAAAJ)

Business Benefit     (1AAAAAAK)

return just what you are looking for without the need to read the individual files yourself.     (1AAAAAAQ)

selecting the base model and a wide range of attributes (e.g. vehicle length) and features (e.g. number of exits)     (1AAAAAAU)

assemblies, and locations for a given vehicle ��C Each vehicle off the assembly line can be one-ofa- kind.     (1AAAAAAW)

previously built, identify the best set of parts, assemblies and component locations for the vehicle (the Vehicle Configuration)     (1AAAAAAY)

at different plants at different times. So, need to select a configuration that can be built at a plant prior to the promised delivery date.     (1AAAAAAAA)

Key Ontology Features     (1AAAAAAAB)

both abstractions and instances     (1AAAAAAAI)

the engineers     (1AAAAAAAK)

patented (2008)     (1AAAAAAAN)

Business Benefit     (1AAAAAAAO)

of new variations     (1AAAAAAAQ)

(incl. features and attributes)     (1AAAAAAAS)

take effect immediately (or at designated times and plants)     (1AAAAAAAU)

proven engineering work     (1AAAAAAAW)

to generate insights to improve business outcomes with content.     (1AAAAAAAAA)

Key Ontology Features     (1AAAAAAAAB)

Business Benefit     (1AAAAAAAAF)

publishing applications     (1AAAAAAAAK)

Key Ontology Features     (1AAAAAAAAT)

Business Benefit     (1AAAAAAAAZ)

Key Ontology Features     (1AAAAAAAAAH)

  • Enterprise Vocabulary Management     (1AAAAAAAAAM)
    • Flexible solutions for managing business vocabularies in support of content delivery, search, navigation, data integration and disambiguation of terms     (1AAAAAAAAAM1)
  • Semantic-XML Message Builder Workbench     (1AAAAAAAAAN)
    • Enables XML-based data exchanges that are specific to the local context while remaining compliant with industry and enterprise standards     (1AAAAAAAAAN1)

Business Benefit     (1AAAAAAAAAQ)

  • Business and systems financial architecture for a government agency     (1AAAAAAAAAY)
  • Understand the business needs in terms of business processes, information and business services     (1AAAAAAAAAZ)
  • Specify the data, technology processes and SOA services of the systems to meet business needs     (1AAAAAAAAAAA)

Key Ontology Features     (1AAAAAAAAAAB)

Business Benefit     (1AAAAAAAAAAI)

  • Architectures and ontologies are mutually supportive     (1AAAAAAAAAAJ)
  • Ontological precision and the ability to federate ontologies brings value to architecture     (1AAAAAAAAAAK)
  • Architectural tools can provide a more friendly way to express ontological information to stakeholders     (1AAAAAAAAAAL)
  • Automating parts of systems from models and ontologies using MDA (model driven architecture) provides the much of the value without runtime overhead     (1AAAAAAAAAAM)
  • The strategic opportunity is to bring all of this information into focus for the enterprise ��C we are only starting to do so.     (1AAAAAAAAAAN)

Business Benefit     (1AAAAAAAAAAAH)

Key Ontology Features     (1AAAAAAAAAAAU)

Business Benefit     (1AAAAAAAAAAAAF)

Key Ontology Features     (1AAAAAAAAAAAAL)

Business Benefit     (1AAAAAAAAAAAAR)

Additional Case Studies The Case Studies below come from the following presentation: Harvesting the Business Value of Ontologies: Recent Case Examples     (1AAAAAAAAAAAAW)

Do it Yourself Data Exploration Cambridge Semantics     (1AAAAAAAAAAAAX)

  • When events trigger action, researchers and analysts examine the data. Combining information from multiple spread sheets and databases is tedious and manual. Desktop tools do not know the categories and properties expressed by column (or row) headings. Moreover, for IT to create a new database or data warehouse is time-consuming, costly, and assumes that all requirements are knowable in advance..     (1AAAAAAAAAAAAZ)

Key Ontology Features     (1AAAAAAAAAAAAAA)

Business Benefit     (1AAAAAAAAAAAAAJ)

  • Focuses on ease of use for end-users with tools they know how to use; minimum IT involvement, if at all.     (1AAAAAAAAAAAAAK)
  • Rapid and low-cost to solution (hours/days), vs. slow and time-consuming for RDBMS, data warehouse, or manual     (1AAAAAAAAAAAAAL)
  • Flexibility in the face of inevitable change: rapid, low-cost, incremental modification vs. time-consuming costly, and difficult revision of conventional stores.     (1AAAAAAAAAAAAAM)
  • "Low-hanging fruit" for many agencies and programs     (1AAAAAAAAAAAAAN)

Better access with semantic search, navigation, query & question answering Recognos Financial     (1AAAAAAAAAAAAAO)

  • Mutual fund industry rules change requires consumer friendly interactive access to 250,000 mandated plan documents.     (1AAAAAAAAAAAAAQ)
  • While the industry��s trade association has developed a standard taxonomy for key topics, (a) buyers do not know industry jargon, (b) often related data is not adjacent to topic, and (c) buyer lacks a way to hone in on answers to questions.     (1AAAAAAAAAAAAAR)
  • Conventional DB and CMS approaches are labor intensive, error prone and costly to update.     (1AAAAAAAAAAAAAS)

Key Ontology Features     (1AAAAAAAAAAAAAT)

  • Structuring of unstructured data (specifically, documents in the Edgar database) enhances citizen access to information     (1AAAAAAAAAAAAAU)
  • Example is a Mutual Fund Prospectus that was filed with the SEC.     (1AAAAAAAAAAAAAV)
  • Uses a taxonomy associated with Mutual Funds. It is widely used and was developed by the Investment Company Institute.     (1AAAAAAAAAAAAAW)
  • User screen displays the actual filed document with the selected field value highlighted within the body of the document itself.     (1AAAAAAAAAAAAAX)
  • The taxonomy maps to the point in the document where the topic occurs.     (1AAAAAAAAAAAAAY)
  • This mapping is automated. Semantic processing of the text results in clean data (as received directly from the source of the data) with all standard data elements identified and extracted from the document without being touched by human hands (no data entry). Automated semantic extraction maintains the integrity of the data.     (1AAAAAAAAAAAAAZ)

  • Knowledge-centric solution semantically analyzes and indexes the database corpus using deep linguistics and domain knowledge to extract data, link information to topics, and find answers to questions.     (1AAAAAAAAAAAAAAB)
  • Consumers can navigate by topic (faceted search) pose questions in natural language, and query data contained in documents as though it were a database.     (1AAAAAAAAAAAAAAC)

  • Concept-based faceted navigation uses semantic analysis of content to reduce cognitive burden for users including extract specific data from tables (e.g., the amount of a specific type of fee). Question answering allows users to express questions in their own words and get the right answer.     (1AAAAAAAAAAAAAAE)
  • Automated semantic indexing and analysis is more consistent, accurate, and cost-effective than comparable manual methods. Since, 80% of all data in organizations is unstructured, applications within government and industry are massive.     (1AAAAAAAAAAAAAAF)

Knowledge-centric information webs & process interoperability Revelytix     (1AAAAAAAAAAAAAAG)

  • DoD attempted to build a data warehouse to connect HR systems and information across the Department. After 11 years and $1B dollars expended, had nothing to show for it.     (1AAAAAAAAAAAAAAI)
  • "We��ve tried everything else and failed." - DoD CTO for Business Mission     (1AAAAAAAAAAAAAAJ)

Key Ontology Features     (1AAAAAAAAAAAAAAK)

  • Built a semantic information web that connected existing systems of record using a common domain ontology connected to relational mapping and source (metadata) ontologies     (1AAAAAAAAAAAAAAO)
  • After 9 months (and very modest dollars expended), DoD had demonstrated a solution     (1AAAAAAAAAAAAAAP)

  • Semantic information web ontology patterns enable federated search, information sharing, and SQL-like querying across heterogeneous business databases.     (1AAAAAAAAAAAAAAR)
  • Basic to very complex analytics and reporting across all systems become end-user generated queries that reference analytics ontology(s) connected to the domain ontology.     (1AAAAAAAAAAAAAAS)
  • Development, extension, and upgrades to the ��system of systems�� is rapid, incremental, iterative, non-invasive and low-risk.     (1AAAAAAAAAAAAAAT)

Do-it-yourself semantic agents to discover, aggregate, analyze & report information Connotate     (1AAAAAAAAAAAAAAU)

Key Ontology Features     (1AAAAAAAAAAAAAAZ)

  • Intelligent semantic software agents to access, harvest, tag, and standardize information that are easy to create by anyone and can be shared and reused.     (1AAAAAAAAAAAAAAAF)
  • Train agents to capture site information, content elements, and take action to extract specific data, capture files, define schemas.     (1AAAAAAAAAAAAAAAG)
  • Agents "speak" HTML, XML, RSS, RDF, PDF, database and Excel.     (1AAAAAAAAAAAAAAAH)
  • Mash-ups create new data by element and schema, in time periods, across sources and time periods, and put data into context     (1AAAAAAAAAAAAAAAI)

  • 360 degree views on topics, issues, etc. combining information from internal and external sources including web pages, blogs, local news, message boards, social media, databases, email, intranets, enterprise applications, etc.     (1AAAAAAAAAAAAAAAK)
  • Productivity improvements from automated gathering, monitoring, and alerting for needed information events that is 24/7/365 or other frequency     (1AAAAAAAAAAAAAAAL)

Smart knowledge-driven citizen-centric services BeInformed     (1AAAAAAAAAAAAAAAM)

  • Permitting site synthesizes requirements, processes, and information across multiple jurisdictions and 14 independent institutions into a unified user experience.     (1AAAAAAAAAAAAAAAO)
  • Immigration site helps new arrivals solve varied problems of relocation. It combines information, and decision logic from 12 agencies into an easy to use single point of service delivery.     (1AAAAAAAAAAAAAAAP)

Key Ontology Features     (1AAAAAAAAAAAAAAAQ)

  • Knowledge-centric solution separates the know from the flow and the function to create declarative applications configured by users with semantic models of legislation, knowledge, processes, data, and UI.     (1AAAAAAAAAAAAAAAU)
  • The core infrastructure consists of an ontology, which is enriched with business rules.     (1AAAAAAAAAAAAAAAV)
  • All functions use the same ontology, e.g., semantic search, information access, automated decision making, decision support, and dynamic processes.     (1AAAAAAAAAAAAAAAW)

  • ��Open knowledge as a service�� bridges the gap between government and citizens and facilitates effective cooperation between independent institutions �� both public and private.     (1AAAAAAAAAAAAAAAY)
  • Provides automated decisions and decision support; means for agencies to manage their knowledge / rules; ability to quickly adapt to external events / implement new legislation; improved decision making, guaranteed compliancy, less errors; improved service delivery to the public; and substantial cost reductions.     (1AAAAAAAAAAAAAAAZ)

Policy-driven compliance, risk, and change management Visual Knowledge'     (1AAAAAAAAAAAAAAAAA)

  • Global financial services firm was $600B behind in M&A because it could not keep up with compliance requirements. Knowledge to track and report regulatory mandates comprehensively across the business was fragmented in separate documents, systems, and data stores, thus slow, prone to error, and difficult to change.     (1AAAAAAAAAAAAAAAAC)
  • "Our only solution is to add more belly buttons, which means committing thousands of people to compliance."     (1AAAAAAAAAAAAAAAAD)

Key Ontology Features     (1AAAAAAAAAAAAAAAAE)

  • Risk, compliance and policy-driven processes include situation awareness, exceptions, fraud, case management, emergency response.     (1AAAAAAAAAAAAAAAAF)
  • Semantic technologies map the external (legal) requirements to policies (expressed as documents) to semantic models (defining information structures, processes, and user responsibilities) to system infrastructure, behaviors, and analytics (measures of performance). This provides visibility and traceability from mandate to manifestation to compliance reporting.     (1AAAAAAAAAAAAAAAAG)
  • Uses semantic technologies to specify, interrelate, and manage knowledge at the level of individual concepts and relationships independently of its source artifact, whether this knowledge comes from:     (1AAAAAAAAAAAAAAAAH)
    • Documents via natural language or visual language understanding, and semantic tagging;     (1AAAAAAAAAAAAAAAAH1)
    • Models including file formats, database schema, object models, content semantics, policies, event and process models, context models, domain ontologies, etc.     (1AAAAAAAAAAAAAAAAH2)
    • Behaviors including software source libraries, directories, frameworks, methods, subroutines, objects, semantic agents, and infrastructure standards.     (1AAAAAAAAAAAAAAAAH3)

  • Knowledge centric collaborative solution that captures all of the regulatory mandates, maps them to policy documents, then to semantic models defining schemas, processes, and decision-making rules, to deployed operational systems and procedures, to analytics that track, assess, and report human and system behavior and ensure compliance     (1AAAAAAAAAAAAAAAAJ)

  • Development of knowledge-centric compliance solution requires fewer resources, is more rapid, less costly, quicker to show value.     (1AAAAAAAAAAAAAAAAL)
  • Operation of knowledge-centric solution requires less labor, is more reliable and less error prone.     (1AAAAAAAAAAAAAAAAM)
  • Maintenance and upgrades are less costly and time consuming. Assessing impact of changes on documentation, systems, and procedures is more automated. Change management and version control is automated.     (1AAAAAAAAAAAAAAAAN)

maintained by the Track-2 champions ... please do not edit     (1AAAAAAAAAAAAAAAAP)


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