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Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph

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Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph

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Tatiana Baquero Cakici, Senior KM Consultant, and Jennifer Doughty, Senior Solution Consultant from Enterprise Knowledge’s Data and Information Management (DIME) Division presented at the Taxonomy Boot Camp (KMWorld 2022) on November 17, 2022. KMWorld is the world’s leading knowledge management event that takes place every year in Washington, DC.

Their presentation “Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph” focused on how ontologies have gained momentum as a strong foundation for resolving business challenges through semantic search solutions, recommendation engines, and AI strategies. Cakici and Doughty explained that taxonomists are now faced with the challenge of gaining knowledge and experience in designing and documenting complex solutions that involve the integration of taxonomies, ontologies, and knowledge graphs. They also emphasized that taxonomists are well poised to learn how to design user-centric ontologies, analyze and map data from various systems, and understand the technological architecture of knowledge graph solutions. After describing the key roles and responsibilities needed for a team to successfully implement Knowledge Graph projects, Cakici and Doughty shared practical ontology design considerations and best practices based on their own experience. Lastly, Cakici and Doughty reviewed the most common use cases for knowledge graphs and presented real world applications through a case study that illustrated ontology design and the value of knowledge graphs.

Tatiana Baquero Cakici, Senior KM Consultant, and Jennifer Doughty, Senior Solution Consultant from Enterprise Knowledge’s Data and Information Management (DIME) Division presented at the Taxonomy Boot Camp (KMWorld 2022) on November 17, 2022. KMWorld is the world’s leading knowledge management event that takes place every year in Washington, DC.

Their presentation “Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph” focused on how ontologies have gained momentum as a strong foundation for resolving business challenges through semantic search solutions, recommendation engines, and AI strategies. Cakici and Doughty explained that taxonomists are now faced with the challenge of gaining knowledge and experience in designing and documenting complex solutions that involve the integration of taxonomies, ontologies, and knowledge graphs. They also emphasized that taxonomists are well poised to learn how to design user-centric ontologies, analyze and map data from various systems, and understand the technological architecture of knowledge graph solutions. After describing the key roles and responsibilities needed for a team to successfully implement Knowledge Graph projects, Cakici and Doughty shared practical ontology design considerations and best practices based on their own experience. Lastly, Cakici and Doughty reviewed the most common use cases for knowledge graphs and presented real world applications through a case study that illustrated ontology design and the value of knowledge graphs.

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Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph

  1. 1. Climbing Ontology Mountain to Achieve a Successful Knowledge Graph Taxonomy Boot Camp 2022 November 7, 2022
  2. 2. Agenda Federal The Value of Knowledge Graphs 1 2 Key Roles for Knowledge Graph Projects 3 Ontology Design Approach 4 Knowledge Graph Case Studies
  3. 3. ENTERPRISE KNOWLEDGE 10 AREAS OF EXPERTISE KM STRATEGY & DESIGN TAXONOMY & ONTOLOGY DESIGN AGILE, DESIGN THINKING & FACILITATION CONTENT & DATA STRATEGY KNOWLEDGE GRAPHS, DATA MODELING, & AI ENTERPRISE SEARCH INTEGRATED CHANGE MANAGEMENT ENTERPRISE LEARNING CONTENT AND DATA MANAGEMENT ENTERPRISE AI Clients in 25+ Countries Across Multiple Industries Meet Enterprise Knowledge HEADQUARTERED IN ARLINGTON, VIRGINIA, USA GLOBAL OFFICE IN BRUSSELS, BELGIUM Top Implementer of Leading Knowledge and Data Management Tools 400+ Thought Leadership Pieces Published Jenni Doughty Senior Consultant, EK Tatiana Cakici Senior Consultant, EK
  4. 4. ENTERPRISE KNOWLEDGE The Value of Knowledge Graphs
  5. 5. FOLKSONOMY CONTROLLED LIST TAXONOMY ONTOLOGY KNOWLEDGE GRAPH ARTIFICIAL INTELLIGENCE Free-text tags. List of predefined terms. Improves consistency. Predefined terms & synonyms. Hierarchical relationships. Improves consistency. Allows for parent/child content relationships. Predefined classes & properties. Expanded relationships types. Increased expressiveness. Semantics. Inference. Capture related data. Integration of structured and unstructured information. Linked data store. Architecture and data models to enable machine learning and other AI capabilities. Drive efficient and intelligent data and information management solutions. @EKCONSULTING
  6. 6. Taxonomy Ontology ● What content covers certain concepts? ● What is a more specific/general version of the concept? ● What are related pieces of content based on shared concepts? ● What are other names for the same concept? Types of questions we can answer: ● Who wrote book A? ● Which books were published by Publisher X? ● Which books were published after 1995 by authors from the UK? ● Which author worked with the most publishers? Types of questions we can answer: @EKCONSULTING
  7. 7. Taxonomy Ontology Knowledge Graph How It All Fits Together @EKCONSULTING
  8. 8. Business Questions Knowledge Graphs Answer DATA FINDABILITY FOUNDATIONS FOR AI Can users find the right information at the right time? Does your organization need to unify data silos to capitalize on the relationships between organizational data resources? Is your data organized to support the cutting-edge AI and cognitive computing solutions that will maintain your organization’s competitive edge? DATA GOVERNANCE Do data resources make it clear to users what information they contain? Do current data procedures support your organization’s business success? DATA AGILITY AND SCALABILITY Does your organization need more flexibility from its data architecture to rapidly iterate and grow new products and services for its users? Do new use cases, legacy data models, and the scale of the data ecosystem cause delays and challenges? @EKCONSULTING
  9. 9. ENTERPRISE KNOWLEDGE Semantic Capabilities Personalization & Insights NLP Applications Identification of Risks & Opportunities Recommendation Logic Data/Content Aggregation Reasoning Disambiguation Reporting & Decision-Making Entity Recognition Inferencing Auto-tagging Querying Query Expansion (Stemming & Synonyms) Discovery, Standardization & Quality Control Search within Results Spell Checker Type Ahead Browsing and Navigation Sort Results Facet/Filter Selection Hierarchy Display Taxonomy Knowledge Graph Taxonomy Ontology Modeling Solution Functionality Use Case Business Value Semantic Formalization & Expressivity Informs Development & Maintenance @EKCONSULTING
  10. 10. Knowledge Graph Applications Recommender Systems Data Management & Quality Auto-tagging Taxonomy & Ontology Development Standardization and Dereferencing Natural Language and Semantic Search Data Visualization and Reporting Dashboard Data Governance @EKCONSULTING
  11. 11. ENTERPRISE KNOWLEDGE Key Roles for Knowledge Graph Projects
  12. 12. Key Roles for Knowledge Graph Projects Core Technical Team Business Team Ontologist Designs the ontology, taking use cases and inferencing needs into account Information Analyst Maps the ontology to existing data sources, determining which fields in a source “match” to which properties, classes in the ontology Semantic Developer Transforms data in various source systems to generate a semantic knowledge graph System Admin/IT Professional Installs and maintains software resources (e.g. ontology management tool, graph database) Subject Matter Expert Understands the domain being modeled and can validate ontology design and knowledge graph data Business Stakeholder Defines the goals of a knowledge graph project, prioritizes knowledge graph use cases Product Manager Defines the knowledge graph as a product and ensures it is well-scoped and managed @EKCONSULTING
  13. 13. ● Ability to design simple and complex ontology solutions that may involve integration of taxonomies, ontologies, and knowledge graphs ● Good understanding of key semantic web standards like RDF, OWL, and SKOS ● Model and document ontologies for priority use cases using various types of semantic tools for ontology management Ontologists ● Good understanding of foundational principles and common applications of taxonomies, ontologies, and semantics ● Ability to analyze content and data sources to discover core components and relationships ● Make sense of large quantities of data and help uncover unexpected data connections ● Identify and document ontology and knowledge graphs use cases and requirements Information Analysts ● Lead and support the technical implementation of semantic solutions ● Leverage common taxonomy/ontology management tools and graph databases. ● Create and work with RDF graph data, including semantic inference, structured and unstructured data, auto- tagging, SPARQL, SHACL validation, and graph machine learning techniques Semantic Developers Skills Required from Core Technical Team Roles @EKCONSULTING
  14. 14. ENTERPRISE KNOWLEDGE Ontology Design Approach
  15. 15. ONTOLOGY DESIGN Not Agile Approach Wait until the ontology is almost complete to share it with the user. Agile Approach Involve the users from the initial use case definition and gather feedback throughout the design process. @EKCONSULTING
  16. 16. Involve the users from the beginning and gather feedback throughout the process. VISION and PLANNING ANALYSIS DESIGN VALIDATION IMPLEMENTATION Ontology Projects Approach @EKCONSULTING
  17. 17. Vision and Planning 1. Define Use Cases 2. Identify Business Value 3. Develop User Personas SALES CUSTOMER ACCOUNT MANAGER INTERNAL SUPPORT Semantic Search Chatbots Content Recommendation Entity Resolution @EKCONSULTING
  18. 18. Analysis TOP-DOWN Talk to subject matter experts BOTTOM-UP Analyze existing data @EKCONSULTING
  19. 19. Design Sketch it out Get a mental picture of how things are connected Potential Tools: ● A whiteboard ● LucidChart ● Microsoft Visio ● PowerPoint ● gra.fo Formalize in RDF Assign official labels, URIs, properties, cardinalities, etc. Potential Tools: ● gra.fo ● PoolParty ● Protégé ● Semaphore (Smartlogic) ● Synaptica ● TopBraid EDG @EKCONSULTING
  20. 20. Let’s walk through design, Imagine that… …we’re building an ontology for a large, multinational retailer. This retailer sells products, which are ordered by customers and delivered by shippers. How do we go about conceptualizing this ontology? @EKCONSULTING
  21. 21. What are we trying to answer? Who worked on project X? Who can help me with topic Y? Who worked on project X? What orders include Category X? Product recommendations based on Category Z? Is there a Shipper trend for any Product? Step 1: Determine the questions we want to be able to answer @EKCONSULTING
  22. 22. What are we trying to answer? Step 2: Determine which classes are necessary to answer each question Who worked on project X? Who can help me with topic Y? Product Category Shipper Order Who worked on project X? What orders include Category X? Product recommendations based on Category Z? Is there a Shipper trend for any Product? @EKCONSULTING
  23. 23. What are we trying to answer? Who worked on project X? Who can help me with topic Y? Product Category Shipper Order Who worked on project X? What orders include Category X? Product recommendations based on Category Z? Is there a Shipper trend for any Product? Supplier Shipper Product Category Customer belongsToCategory includedInOrder Territory managesTerritory shippedByShipper suppliesProduct Employee processedByEmployee submitsOrder Order Step 3: Determine which relationships between the classes are necessary to answer the questions @EKCONSULTING
  24. 24. Validation Perform a mix of techniques to validate your model ● Sanity Check ● Sensitivity Check ● Data Fit Check ● Technical Check ● Best Practices Check Potential Tools Ontology Pitfall Scanner (OOPS) or similar open- source tools can be used to check for: ● Missing type declarations ● Missing labels ● Missing domain/range ● Multiple domains/ranges ● Cyclical hierarchies ● Incorrect inverse properties @EKCONSULTING
  25. 25. Implementation Position the ontology so that it can fulfill the use case(s). Often, implementation of an ontology involves the creation of a knowledge graph. Tooling Considerations: ● Ontology Management/Editors ● Governance Workflows and Controls ● Documentation ● Integrations or Consuming Applications @EKCONSULTING
  26. 26. Ontology Best Practices Ontology Design Best Practices Ontology Implementation Best Practices Identify a clear use case Specify expected data-types for attributes Reuse standards and existing vocabularies Prioritize relationships Leverage consistent naming conventions Use singular nouns for classes Start small and grow iteratively Define & document your purpose Plan for the long- term Focus on the end user Leverage governance Use simplest language possible Look to usability best practices These best practices will help enhance the usability of the ontology. However, these rules are slightly flexible – use your best judgement and keep business need centered. @EKCONSULTING
  27. 27. Design and Implementation Challenges Complexity: Domains may be complex, and thus developing an ontology to describe them require intensive research and validation. Data & Technology: The data contained in the legacy technology may lack a clear organization scheme or require additional transformations.. Understanding: Internal experts often have conflicting ideas on the process and about data intent or usage. Scaling: Beyond a prototype. Challenges Linked Open Data Analysis: Analyze existing ontologies available as linked open data that may provide clarity and understanding to a complex process. Top-Down Analysis: To overcome the lack of a clear organization scheme, combine bottom-up analysis approach with focus groups and validation sessions. Federation and Virtualization: Present the ontology in numerous ways to help communicate the ontology design effectively, show it can be used on real data, and build consensus among subject matter experts. How we addressed them @EKCONSULTING
  28. 28. ENTERPRISE KNOWLEDGE Knowledge Graph Case Studies
  29. 29. . THE CHALLENGE THE SOLUTION THE RESULTS ● We developed a cloud-hosted semantic course recommendation service powered by a redesigned taxonomy that was applied to a healthcare-oriented knowledge graph. ● EK extracted key terms and topics from the content in order to rapidly build relationships between content components. ● The recommendation engine was integrated with the organization’s learning platform, successfully delivering courses relevant to each user’s exam performance. Personalized Course Recommendations A healthcare workforce solutions provider: ● Had failed to consistently deliver relevant tailored course content to healthcare professionals. ● Wanted to increase engagement and learning outcomes across their learning platform. ● Wanted to deliver personalized content offerings to connect users with the exact courses that would help them master key competencies. The recommendation service is beating accuracy benchmarks and replacing manual processes, supporting higher- quality, more advanced, and targeted recommendations that provide clear reasons why the course was recommended to the user. @EKCONSULTING
  30. 30. Solutioning Challenge Questions Courses What is the Question about? What is the Course about? How are Courses related to Questions? How are the Concepts relevant to each other? Healthcare Professional (Assessment) @EKCONSULTING
  31. 31. Course Recommendations Ontology @EKCONSULTING
  32. 32. Respiratory Specialist Pulmonary Rehabilitation Oxygen Therapy Asthma Emphysema Respiratory Conditions Asthma Attack Airway Management Assessment Respiratory Emergencies Checklist Dr. James Respiratory Specialist Hospital Profile Input: Assessment Question Subjects Output: Recommended Course A knowledge graph stores a semantic model of content topics including variation in topic naming conventions, and expert facts about the topics and their relevance to each other. Semantic Network Example @EKCONSULTING
  33. 33. Process of Generating Semantic Networks Data Integration Connecting existing data models & concepts Data Enrichment Organizing & enhancing data via extraction, tagging, & classification Data Creation Adding new data concepts via taxonomy development, data entry, etc. ● Taxonomy and Ontology ● Questions ● Courses ● Competency Concepts ● Evaluation Methods ● Proficiency Level ● Extracting Topics from Assessments for Taxonomy Enrichment ● Tagging Questions ● Classifying Competency Concepts @EKCONSULTING
  34. 34. ENTERPRISE KNOWLEDGE ● Start with a small scope ● Involve SMEs each knowledge domain ● Leverage ontology design best practices ● Identify “gold standards” to adjust the model along the way ● Explore how the knowledge graph can help with other solutions in the future Key Takeaways @EKCONSULTING
  35. 35. Q&A Thank you for listening. Questions?

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