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Semantic Use Cases

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This presentation outlines several business cases for successfully implementing enterprise knowledge graphs that will provide business value and insight. The presentation explores several different types of knowledge graphs that address varying business cases for knowledge and relationships discovery. The use cases range from serving as the basis of a recommendation engine and a future chatbot application, to allowing financial analysts to discover relationships between data fields across multiple financial forms and data series.

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Semantic Use Cases

  1. 1. COMMON SEMANTIC USE CASES Yanko Ivanov Senior KM Consultant November 5, 2018
  2. 2. EK AT A GLANCE 55% 45% STABLE CLIENT BASE 35+ EXPERT CONSULTANTS CommercialFederal 6 AREAS OF EXPERTISE § TAXONOMY & ONTOLOGY DESIGN § STRATEGY & DESIGN § DEVELOPMENT SERVICES § AGILE TRANSFORMATION § CHANGE MANAGEMENT § CONTENT & BRAND STRATEGY BASED IN DC 18+ COUNTRIES GLOBAL REACH WITH CLIENTS IN AWARD-WINNING CONSULTANCY § KMWORLD’S 100 COMPANIES THAT MATTER IN KM (2015, 2016, 2017, 2018) § CIO REVIEW’S 20 MOST PROMISING KM SOLUTION PROVIDERS (2016) § WASHINGTONIAN’S TOP 50 GREAT PLACES TO WORK (2017) § WASHINGTON BUSINESS JOURNAL’S BEST PLACE TO WORK (2017, 2018) § ARLINGTON ECONOMIC DEVELOPMENT’S FAST FOUR AWARD – FASTEST GROWING COMPANY (2016)
  3. 3. COMMON SEMANTIC USE CASES
  4. 4. RECOMMENDATION ENGINE The Problem A large international development bank needed a better way to disseminate information and expertise to all of their staff so that projects would be more efficient and more successful. Sample questions: • What is the latest on education in this country? • How are these types of infrastructure projects tackled in other countries? • What do I need to know on this topic before my next meeting with this country’s representative? The Solution A semantic hub leveraging a knowledge graph for all of the relevant people and content within the organization. • Knowledge graph that collects the content, user information, and activities. • Semantic hub as the central orchestrator of information flow. • This information is used to recommend relevant articles and information when meetings are scheduled, during searches, and eventually to power a chat bot. Taxonomies Ontologies Auto-tagging Knowledge Graph
  5. 5. HIGH LEVEL ARCHITECTURE SOCIAL NETWORK TRENDS Semantic Web App Widget Service Chatbot BANK EMPLOYEE KNOWLEDGE GRAPH Semantic Hub Recommendation Engine
  6. 6. DATA MANAGEMENT & ANALYSIS The Problem A large US Federal Agency needs a better way to manage the thousands of data sets that they use to develop economic models that drive US policy. Currently, there is no way to know what data sets are owned, how often they are used and how different data sets relate to one another. They need a tool that will allow them to better understand the relationships between data elements and how others use the data. The Solution We are working with the agency to design an ontology that captures data sets, data elements, how they relate to one another, and information about how they are used. This information is stored in a triple store that sits behind a custom web application. The result will be a semantic search application that allows economists to see data sets, their data elements and how they relate to one another. We also capture a log of usage so that the agency can better negotiate deals with data vendors. Taxonomies Ontologies Knowledge Graph
  7. 7. AUTO-TAGGING AND SEARCH The Problem The US Veterans Administration needed a better way to manage the content on their PTSD website. The site has thousands of scholarly articles that need to be tagged with a deep medical taxonomy (of over 2000 terms) so that site users can quickly find the information they need to improve their quality of life. The Solution EK worked with the VA team to learn more about their internal processes for collecting and tagging content for search. The new system has a taxonomy and auto- tagging tool that analyzes every piece of content as it is entered into the system. This automation cuts the content management process in half and improves the accuracy of tagging. Taxonomies Auto-tagging
  8. 8. WE’LL BE ANSWERING QUESTIONS NOW Q A& THANKS FOR LISTENING Q & A SESSION
  9. 9. CONTACT ME YANKO IVANOV yivanov@enterprise-knowledge.com https://www.linkedin.com/in/yankoivanov/ @YankoKIvanov

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