Integrating Siloed Personal Knowledge Graphs

Critical steps in improving the productivity of enterprise note-taking

Figure 1: A dataflow diagram for integrating personal knowledge graph applications such as Roam and Obsidian. Image by the author.

The IPKG Hypothesis

The basis for much of the logic around these Integrated Personal Knowledge Graph (IPKG) efforts is the following hypothesis:

Align Your IPKG Benefits with Organization Strategy

There will undoubtedly be many individuals that will be ultra-enthusiastic about finally having an excellent note-taking tool. Integrating a siloed PKG with companies’ complete entity-type systems has many benefits, and our guess is your users will love the experience. However, companies rarely spend millions of dollars only to make their employees happy. Employee happiness is a pleasant side effect but should never be considered a primary driver for corporate IPKG initiatives.

  1. sharing knowledge
  2. avoiding duplication of knowledge
  3. making knowledge consistent
  4. improving collaboration between teams within a department
  5. improving collaboration between departments or business units
  6. streamlining communication
  7. increasing knowledge worker productivity
  8. improving search results and search ranking

Find and Educate an Executive Sponsor

My experience is that most successful knowledge management and Integrated PKG projects are driven by empowered staff with formal training in enterprise knowledge management strategies. Not all organizations have these teams, or if they do have them, they don’t have the enterprise mandate or sponsorship of an influential executive. To be successful, you will need long-term sponsorship and funding. Integration takes time and money. You will need to find staff with the right skills and the ability to get access to essential data services. Your business alignment document will help you convince executives that your Integrated PKG project will help them achieve their strategic objectives.

Define Clear Metrics for Success

One of the critical challenges of integrating organization knowledge with any text editor is to agree on standard measures of productivity improvement. Let’s take a simple example: adding a list of industry-specific terms or company product names to an individual spell checker.

Example: Metrics for Customise Spell Checking

The key is to capture the small productivity gains if your word processor is smart enough to gather terms from the IPKGs of your peers. It takes only a few seconds to add a word to the dictionary of a local word processor. But if you have thousands of terms and each of your 10,000 employees needs to add these words, then lost productivity is measured. You will need to estimate the cost/hour of your knowledge workers and use the best estimates of the increased productivity without the distractions of your text editor constantly telling you there are terms that it needs to understand. These are the types of metrics that can help you build a business case for IPKGs.

Example: Automatic HyperLinking

Let’s take this one step further. What if the first time we typed in a new technical term in a document, a wiki, or PKG editor, the text editor would automatically add a link to your company or department business glossary? Then your new employees could quickly jump to the right place to find that term and understand the definition of that term and related terms.

Create an Inventory of Current Knowlege Repositories

One of the critical challenges of building good IPKGs is understanding where related information and knowledge exist within your organization today. Once we have an inventory of these knowledge stores, we can ask what autosuggest-related services could be designed using existing APIs. You will also need to estimate how frequently a note-taker will want to reference these concepts in their documents and prioritize the most commonly used terms.

  1. Approved business terms from a centralized glossary of terms
  2. List of industry-specific words or phrases from medical or legal dictionaries
  3. Names of wiki pages that are the most frequently referenced in other wiki pages or tagged with a specific label
  4. List of products and product attributes in a product catalog.
  5. List of the approved desktop applications or tools that employees are using
  6. List of computer systems or databases
  7. List of employee names (both first and last names), including familiar person names from other countries and languages used in your organization

Building a Reference Architecture Diagram

After your IPKG team starts to inventory knowledge sources, you should create a single-page “map” showing how knowledge flows into your IPKG system and its new services. These data flow diagrams start with a list of input sources on the left, your IPKG in the center, and consumers of this data on the right side. The top of this blog has an example of this diagram.

Monitoring You Progress

The IPKG reference architecture diagram includes a monitoring dashboard that accesses the list of terms and the user event log files. This dashboard pulls in data from multiple sources and allows different users to build a customizable single-page view of your progress.

Monitoring Broken and Obsolete Links

In addition to monitoring autocompletion events, the quality dashboards might also attempt to track when documents’ links point to concepts that are no longer relevant. For example, you might have documents describing an old computer system decommissioned last year. In many cases, links to decommissioned systems are of interest to historians and auditors and should remain the same. In other cases, links to decommissioned resources need to be updated with links to the systems that have replaced them.

Conclusion

Many word processing and editing tools today allow you to easily add custom dictionaries for basic tasks such as spell checking that leverage your organization’s shared knowledge. However, a new generation of personal note-taking tools, such as Roam Research, and Obsidian, allows personal note-taking autocomplete functions to reach far beyond simple term lists.

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Dan McCreary

Distinguished Engineer that loves knowledge graphs, AI, and Systems Thinking. Fan of STEM, microcontrollers, robotics, PKGs, and the AI Racing League.