Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Knowledge Architecture: Graphing Your Knowledge

Ask any project manager and they will tell you the importance of reviewing lessons learned prior to starting a new project. The lesson learned databases are filled with nuggets of valuable information to help project teams increase the likelihood of project success. Why then do most lesson learned databases go unused by project teams? In my experience, they are difficult to search through and require hours of time to review the result set.

Recently I had a project engineer ask me if we could search our lessons learned using a list of 22 key terms the team was interested in. Our current keyword search engine would require him to enter each term individually, select the link, and save the document for review. Also, there was no way to search only the database, the query would search our entire corpus, close to 20 million URLs. This would not do. I asked our search team if they would run a special query against the lesson database only, using the terms provided. They returned a spreadsheet with a link to each document containing the terms. The engineer had his work cut out for him: over 1100 documents were on the list;.

I started thinking there had to be a better way. I had been experimenting with topic modeling, in particular to assist our users in connecting seemingly disparate documents through an easier visualization mechanism. Something better than a list of links on multiple pages. I gathered my toolbox: R/RStudio, for the topic modeling and exploring the data; Neo4j, for modeling and visualizing the topics; and Linkurious, a web front end for our users to search and visualize the graph database.

  • Login to see the comments

Knowledge Architecture: Graphing Your Knowledge

  1. 1. Knowledge Architecture: Graphing your knowledge David Meza Chief Knowledge Architect, NASA
  2. 2. David Meza Chief Knowledge Architect, NASA
  3. 3. © 2015 IHS. ALL RIGHTS RESERVED. KNOWLEDGE ARCHITECTURE: GRAPHING YOUR KNOWLEDGE Combining Strategy, Data Science and Informatics to Transform Data to Knowledge
  4. 4. AGENDA • Challenges • Knowledge Architecture • Graphing LLDB • Questions? 4
  5. 5. “The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker.” PETER F. DRUCKER, 1999
  6. 6. NASA Challenges • Hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geo spatial data, IT logs, etc., are stored nation wide • The data is growing in terms of variety, velocity, volume, value and veracity • Accessibility to Engineering data sources • Visibility is limited
  7. 7. To convert data to knowledge a convergence of Knowledge Management, Informatics and Data Science is necessary. 7 Knowledge Management Data ScienceInformatics
  8. 8. Knowledge Architecture • The people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations. • What is an intellectual infrastructure? • The set of activities to create, capture, organize, analyze, visualize, present, and utilize the information part of the information age.. • Information + Contexts = Knowledge • Knowledge Management + Informatics + Data Science = Knowledge Architecture • KM without Informatics is empty (Strategy Only) • Informatics without KM is blind (IT based KM) • Data Science transforms your data to knowledge 8
  9. 9. “We have an opportunity for everyone in the world to have access to all the world’s information. This has never before been possible. Why is ubiquitous information so profound? It is a tremendous equalizer. Information is power.” ERIC SCHMIDT (FORMER CEO OF GOOGLE)
  10. 10. There was a inquisitive engineer…
  11. 11. LESSON LEARNED DATABASE 12 2031 lessons submitted across NASA. Filter by date and Center only. Useful information stored in database.
  12. 12. Document to Graph 13
  13. 13. PATTERNS EMERGE
  14. 14. TOPIC MODELING 15 Topic models are based upon the idea that documents are mixtures of topics, where a topic is a probability distribution over words. LDA Model from Blei (2011) David Blei homepage - http://www.cs.columbia.edu/~blei/topicmodeling.htmlBlei, David M. 2011. “Introduction to Probabilistic Topic Models.” Communications of the ACM.
  15. 15. CORRELATION BY CATEGORY 16 To find the per-document probabilities we extract theta from the fitted model’s topic posteriors
  16. 16. TOPIC TRENDS 17 Using mean of theta by years to trend topics
  17. 17. TOPIC VISUALIZATION 18
  18. 18. GRAPH MODEL OF LESSON LEARNED DATABASE 19 http://davidmeza1.github.io/2015/07/16/Graphing-a-lesson-learned-database.html
  19. 19. GRAPH MODEL OF LESSON LEARNED DATABASE 20
  20. 20. GRAPH MODEL OF LESSON LEARNED DATABASE 21
  21. 21. DATA DRIVEN VISUALIZATION 22
  22. 22. 28 WHAT COULD YOU ACCOMPLISH IF YOU COULD: • Empower faster and more informed decision-making • Leverage lessons of the past to minimize waste, rework, re-invention and redundancy • Reduce the learning curve for new employees • Enhance and extend existing content and document management systems
  23. 23. Contact Information David Meza – david.meza-1@nasa.gov Twitter - @davidmeza1 Linkedin - https://www.linkedin.com/pub/david-meza/16/543/50b Github – davidmeza1 Blog davidmeza1.github.io 29
  24. 24. Contents © 2015 IHS. ALL RIGHTS RESERVED. 30 Report Name / Month 2015 QUESTIONS?

×