Personal Knowledge Markup Language
Personal Knowledge Markup Language (PKML) is an experimental markup language supporting learning, journaling and knowledge engineering.
To get some idea of what it is used for let’s look at some scenarios in which it is used.
Scenario: Vocabulary, Terms, Review and Cognitive Resources
You are reading a book or an article on-line and make some notes on a passage in your Personal Journal. There is a word or term that you don’t fully understand. You highlight the text and instruct the Personal Agent to look it up. It checks to see if you have seen this term before. As you suspected, this is the first time you have worked with the term. You tell the journal that you think this term is important and that you want to be quizzed on it later. Several days later when you put the Agent in Review Mode you may see the new term, review and interact with it. The Agent will keep track of how well you are learning it and will review it with you more or less frequently depending on how you are doing. If you use the term in the future the Agent will offer to show where it appears in the note you just built so that you can deepen your knowledge and continue to build associations.
Note-Taking, Snapshots and Capture: Building a Personal Knowledge Assistant
You are reading a book or taking some class notes, watching a movie or a lecture. There are some interesting ideas, important topics or useful facts that you would like save for later. You use a set of markup techniques to prepare what you are reading, or your own thoughts so that they later appear in something as simple as a set of review cards or as complex as a personal knowledge assistant. You use PKML to start the process without interfering with your thought process. The assistant uses the PKML to help you elaborate, deepen and build on your quick notes. The Assistant can then “read” the PKML to provide live interactive visualizations and review options. It creates trees of thought questions and perspective lenses to help deepen, elaborate, and integrate your note taking process. It uses the PKML to create thematic movies and narratives from your diary entries and images. One day it will even use PKML to animate little “knowledge objects” to populate virtual worlds.
Scenario: Building Knowledge
You ask the Journal Agent to read a note you just dropped into your system so that it can find themes and topics that you have taken notes about in the past. It highlights one and offers to add a reference to the entry so that it will show up next time. You have a little extra time so you ask it to show you the other places where these topics in the past so that you can review the context.
Then you go through the note and identify topics, terms and themes that you want to add “officially” your journal so that you can work with them over time.
Scenario: Connections and Context Matrix
You tell the Agent that you want to review a book or topic. The agent builds a matrix out of the related materials – an invisible multi-dimensional object constructed from the connections, context and cognitive resources in your Journal. The first thing you see is a phrase or topic selected from the matrix. You start your navigation here. To help explore your memory of the material you think about how the topic was used in your notes. To spark your memory the Agent will quiz you on the topic, its connections and contexts. To recall the context to memory you zoom out step by step to slowly reveal more of knowledge landscape. Then you zoom in again to bore down into related terms or ideas. You move forwards and backwards through context. Then you tell the agent to spin the matrix to some other random topic. For fun you decide to add more books and topics and the agent will regrow a new matrix based on your instructions.
These and other scenarios are partially implemented in the current version of the DOK system. And other scenarios are in the works. It is currently used in generating and maintaining parts of the Big Sky and is implemented in some of the knowledge aware software environments supporting it.
Other Resources:
PKML for Note-taking: Language Specification, Transforms, Editors, Clients and Visualizations -> Go!