OntologySummit2011: Application and Use Cases synthesis    (2LKI)

Ontology, semantics, and knowledge technologies benefit and add value across a very broad spectrum of applications:    (2SD0)

The following lists the case examples documented in the supporting materials, and highlights value delivered..    (2SDE)

Apple SIRI    (2SDF)

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.    (2SDG)

BeInformed    (2SDH)

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    (2SDI)

Cambridge Semantics    (2SDJ)

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.    (2SDK)

Connotate    (2SDL)

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.    (2SDM)

Department of Homeland Security    (2SDN)

(a) DHS infrastructure taxonomy; (b) Complex event modeling, simulation and analysis (CEMSA)    (2SDO)

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.    (2SDP)

EDM Council    (2SDQ)

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.    (2SDR)

Franz | AMDOCS    (2SDS)

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.    (2SDT)

IBM & U Maryland    (2SDU)

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.    (2SDV)

Innovative Query    (2SDW)

(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.    (2SDX)

Mayo Clinic    (2SDY)

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.    (2SDZ)

Model-driven Development    (2SE0)

Architectures and ontologies for business value: Architectures and ontologies are mutually supportive.    (2SE1)

Recognos Financial    (2SE2)

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.    (2SE3)

Revelytix    (2SE4)

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.    (2SE5)

Sallie Mae    (2SE6)

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.    (2SE7)

Sandpiper    (2SE8)

Semantic technology in rental product marketing: ontologies power semantic search and search engine optimization to improve user experience and business outcomes.    (2SE9)

Semantic Arts    (2SEA)

Applying semantics to enterprise systems - Proctor and Gamble case study: Ontologies provide a practical way to integrate research findings across disciplines.    (2SEB)

Top Quadrant    (2SEC)

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.    (2SED)

Trigent Software    (2SEE)

(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.    (2SEF)

Visual Knowledge    (2SEG)

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.    (2SEH)

zAgile    (2SEI)

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.    (2SEJ)

Where do case examples show knowledge technologies, semantics, and ontology add value?    (2SEK)

Patterns of value delivery that emerged from these case examples have several dimensions worth noting:    (2SEL)

Case Study Summaries    (2SES)

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.    (2OR6)

Each Case Study    (2OR7)

Virtual assistant as a next UI paradigm Apple Siri From the following presentation: Harvesting the Business Value of Ontologies: Recent Case Examples    (2SCZ)

Challenge    (2S91)

Key Ontology Features    (2SA2)

Solution    (2S9B)

Business Benefit    (2S9F)

Integration of Multiple Systems from Multiple Companies YefimZhuk, Sallie Mae    (2OT1)

Challenge    (2ORH)

Key Ontology Features    (2ORM)

Solution    (2ORN)

Benefits    (2ORR)

Standardization of Terms and Definitions for Financial Services MikeBennett, EDM Council    (2OT2)

Challenge    (2ORX)

Key Ontology Features    (2OS3)

Solution    (2OS9)

Benefits    (2OSG)

Semantic Tech in Rental Product Marketing JimRhyne, Sandpiper    (2OT3)

Challenge    (2OSN)

Key Ontology Features    (2OSQ)

Solution    (2OSU)

Business Benefit    (2OSY)

Ontology and Rules provide rapid Natural Language Understanding ChuckRehberg, Trigent Software    (2OT5)

Challenge    (2OT6)

Key Ontology Features    (2OTE)

Solution    (2OTJ)

Business Benefit    (2OTP)

Ontology and Rules provide Mass Customization of Vehicles ChuckRehberg, Trigent Software    (2OTY)

Challenge    (2OTZ)

Key Ontology Features    (2OU6)

Solution    (2OU9)

Business Benefit    (2OUL)

Content Intelligence and Smart Applications GregBardwell, Innovative Query Inc.    (2OUR)

Challenge    (2OUS)

Key Ontology Features    (2OUU)

Solution    (2OUW)

Business Benefit    (2OUY)

Semantic BI for Blogging Bardwell - see above    (2OV4)

Challenge    (2OV5)

Key Ontology Features    (2OVB)

Solution    (2OVD)

Business Benefit    (2OVJ)

Valuing the Harvest from using Ontologies RalphHodgson, TopQuadrant    (2OVQ)

Challenge    (2OVR)

Key Ontology Features    (2OVV)

Solution    (2OVZ)

Business Benefit    (2OW8)

Architectures and Ontologies for Business Value CoryCasanave - Model Driven Solutions    (2OWP)

Challenge    (2OWQ)

Key Ontology Features    (2OWZ)

Solution    (2OX2)

Business Benefit    (2OX7)

Model-driven Framework for Process Deployment, eXtreme Traceability SanjivaNath, ZAgile    (2OXI)

Problem    (2OXJ)

Solution    (2OXP)

Technology    (2OXV)

Business Benefit    (2OY1)

Applying Semantics to Enterprise Systems - Proctor and Gamble Case Study DaveMcComb, Semantic Arts    (2OYV)

Challenge    (2OY7)

Key Ontology Features    (2OYE)

Solution    (2OYI)

Business Benefit    (2OYP)

Ontologies and CRM for Telecoms BillGuinn, MikeLurye, SusanMacCall, Amdocs    (2OYX)

Challenge    (2OYY)

Key Ontology Features    (2OZ4)

Solution    (2OZ8)

Business Benefit    (2OZB)

Additional Case Studies The Case Studies below come from the following presentation: Harvesting the Business Value of Ontologies: Recent Case Examples    (2SCY)

Do it Yourself Data Exploration Cambridge Semantics    (2S9L)

Challenge    (2S9M)

Key Ontology Features    (2S9O)

Solution    (2S9S)

Business Benefit    (2S9X)

Better access with semantic search, navigation, query & question answering Recognos Financial    (2SA7)

Challenge    (2SA8)

Key Ontology Features    (2SAC)

Solution    (2SAJ)

Business Benefit    (2SAM)

Knowledge-centric information webs & process interoperability Revelytix    (2SAP)

Challenge    (2SAQ)

Key Ontology Features    (2SAT)

Solution    (2SB1)

Business Benefit    (2SB4)

Do-it-yourself semantic agents to discover, aggregate, analyze & report information Connotate    (2SB8)

Challenge    (2SB9)

Key Ontology Features    (2SBD)

Solution    (2SBQ)

Business Benefit    (2SBV)

Smart knowledge-driven citizen-centric services BeInformed    (2SBY)

Challenge    (2SBZ)

Key Ontology Features    (2SC2)

Solution    (2SCA)

Business Benefit    (2SCE)

Policy-driven compliance, risk, and change management ''Visual Knowledge'    (2SCH)

Challenge    (2SCI)

Key Ontology Features    (2SCL)

Solution    (2SCS)

Business Benefit    (2SCU)

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