Back to "School": Learning the Basics of Protege, Python, and Cypher

 A few years back, I inherited a substantial collection of vinyl records that accumulated over two generations and two sides of the family.  The result is a very eclectic collection that goes from field recordings of birds of Kenya to early career Johnny Cash and lots of Edith Piaf. I have a simple record player and once in a while over the past few years, I have played some of my favorite records.  About a month or so ago, having some free time, I decided to catalog the collection using my favorite tool, TiddlyWiki.  In the process, I learned quickly how to leverage some of the functionalities I had previously neglected in TiddlyWiki.  I had always used the tagging function but I had not used the metadata fields.

I now have a complete catalog with close to 700 records and this turns out to be an interesting data set for learning purposes because:  

a) I can save the TiddlyWiki as a CSV file with all the metadata.

b) I am at the point in my exploration of Python where I can understand basic instructions and use Python in Excel to manipulate the data, including (perhaps) the less structured data (all the album titles and song titles). 

I was getting bored with Python exercises that use meaningless data.  Hopefully using my own dataset will be a little more exciting.  Using the TiddlyWiki Records Catalog will also lead to some data cleaning issues.  For example, I'm not sure I can easily extract a list of songs even though I've "captured" all the data in the text section of each record. 

At times, when I am struggling to understand what I am doing with Python, I ask myself why I am trying to learn Python in the first place since I don't plan to become a programmer. I am reminded of my long-term goal: I need a better, broad understanding of data management and associated tools to be able to converse intelligently around the technical aspects of knowledge graphs.  As a result, I am learning the basics of Protege to understand ontology design, the basics of Python, the basics of Cypher to understand graph querying, etc.. 

Evolution of my Concept Mapping Practice

I started mapping using Inspiration, then relatively quickly turned to CmapTools. More recently I have explored the mapping functionalities associated with TiddlyMap.  The tools and their specific functionalities have played a key role in the development of my practice around mapping, and my evolving needs have played a key role in how I sometimes push the tools well beyond their original purpose.  I pushed CmapTools to become a gigantic map of maps, well beyond a sustainable system.  I am using TiddlyMap to mimic the capabilities of a knowledge graph.

In developing my mapping practice, I also acquired a few bad habits. Perhaps they were useful adaptation rather than just bad habits but now I am reconsidering my approach.

Examining even just the collection of maps that I have made public on my site, it is clear that I have used concept mapping mostly as a personal thinking tool. When I have used mapping within organizations, including at NASA and Chemonics, I had to either establish a clear framework (especially at NASA) so that the maps could be easily readable and usable. The NASA mapping approach was presented as early as 2016 at the APQC KM conference.

The connecting words are key to concept maps. They make the maps more readable and clarify the meaning of the connection.  These connecting words become the "predicate" in the world of ontologies and Knowledge Graphs. There is a subject (one node representing a concept), an object (another node representing a concept), and a predicate (connecting words) articulating the relationship between the two nodes.

In my early concept mapping practice, I either omitted the connecting words or used a few key words repetitively (and, such as, including). 

Most of the maps I created at NASA were not really concept maps because the nodes were not individual concepts but rather entire phrases. The unit of analysis for a node was at the higher level of "complete thought" and not individual concept. I called them knowledge maps or KMAPs, and eventually insight maps on my own website.  This allowed me to put much more content on a map than what is possible by identifying and mapping the connections between individual concepts. 

The connecting words and the way concepts are connected becomes more and more important as we move towards more "readable" maps or graphs. In a simple map, the connecting line and arrow may be relatively easy to interpret but that is not always the case and something more standardized is needed at scale. In particular, if we want these types of maps to be machine readable, then a formal ontology with very well defined "predicates" or connecting words becomes essential.  Ontology tools/software also become essential to manage larger ontologies.

I thought it would be interesting to take some of my old maps and see if they can be made more readable with greater attention to the connecting words and a more granular approach to concepts. It's also possible that what I was trying to express with those early maps isn't meant for a real concept map.

The first map I ever posted when I was still using Inspiration as a mapping tool, can easily be improved and turned into a proper concept map or even a knowledge graph. I have now recreated that early map using a more formal mapping technique inspired by ontologies, with specific classes of concepts (visually distinct on the map with different colors) and well-defined, consistent connecting words.  See Map #34 in the Insight Maps collection (and below). 

The original map included the movies' release year in parenthesis.  This type of data can be added as an annotation in the map, visible when hovering over each movie node.  Other properties could be added, including a link to the IMDb record for example.  In the related ontology, the "releasedYear" could simply be a property of the movie class.  

Not every map requires a formal ontology, but understanding ontologies and learning more about knowledge graph is helping me to refine my mapping practice. 

The Concept Mapping label provides access to some related posts over the years.  

Enhancing Project Management & Learning through Knowledge Graphs

 I would like to test a set of interconnected ideas. 

1. Transforming Theory of Change into a Knowledge Graph
A project's Theory of Change (ToC) can be transformed into a Knowledge Graph.  A Theory of Change has a number of key entities: Activities, Results, Assumptions.  Each can be characterized in some detail with indicators, properties, etc.. and there are clear relationships.  An activity "contributes to" a result.  An assumption can "influence" an activity.   The ToC is a hypothesis based on past evidence from similar or related projects. 

The challenge here may be to ensure that the ToC's complexity doesn't lead to an overly complicated Knowledge Graph.  It will also be important to consider how to represent uncertainties and evolving assumptions over time within the Knowledge Graph.

2. Accumulating Data to Confirm or Adjust the ToC
As the project is implemented, data accumulates that will either confirm the ToC or require some adjustments. That's what adaptive management should be about.  The data can combine structured, quantitative data based on predetermined indicators, AND unstructured data from learning activities such as After-Action-Reviews, interviews, stories, etc.  All this data can be added to the Knowledge Graph. 

The challenge here will be in consistently capturing and integrating unstructured data in a way that it's meaningfully represented within the Knowledge Graph.  NLP tools may be necessary. Would they be effective?  In addition, as the graph grows in complexity with new data, will it remain manageable?

3. Integrating KGs across Projects for Organizational Learning
Across an organization, tools like Propel that create a structure for knowledge capture could lend themselves well to integration with a Knowledge Graph to aggregate data across projects AND enable more sophisticated analysis. A set of project-based Knowledge Graphs developed based on their individual ToCs (as in steps 1 and 2 above) can be connected into a bigger Knowledge Graph.  At first, this expansion could be focused on a particular sector with a "Grand Sector Theory of Change" with regional as well as country-specific adaptations and tailored assumptions based on context. 

Challenges might include a) interoperability between different project Knowledge Graphs, particularly if they were developed independently or using different methodologies; b) the requirement for a unified ontology or framework for creating these graphs; c) associated governance and data-sharing protocols to enable integration without compromising data integrity or security.

4.  Portfolio-level Analysis
Expanding the scope further, a donor could look at an entire portfolio of projects within a comprehensive knowledge graph, combine insights from the graph with meta-analysis like evidence-mapping reviews to help generate better RFPs. That would still require extensive country/local knowledge.

Challenges might include scalability issues, particularly in ensuring that the Knowledge Graph remains up-to-date and relevant across a broad portfolio.  The need for documenting extensive local knowledge is also a potential bottleneck.

5. Local Knowledge Mapping and Knowledge Graphs
Therefore, local knowledge mapping and knowledge graphs would need to be further developed for and by local SMEs to provide leverage for local organizations to impact project designs and project implementations.  

The challenge might be in creating incentives and building capacity for local SMEs to develop and maintain these Knowledge Graphs.  There would be issues related to standardization to ensure that local KGs can interface with broader organizational or donor-level KGs without losing the richness of local insights.  There would also be significant issues related to data ownership.  

This is potentially unrealistic, overly ambitious, crazy, etc.. I wonder if anyone has already started working on step 1 because I often think I've come up with an idea and soon realize it's already been done (or tried).   Turning a Theory of Change into a Knowledge Graph is definitely feasible.  Then we would discover whether that's useful at all, before thinking too much about the more ambitious steps. 

Tacit Knowledge Mapping?

A question came up on a messaging channel of the KMGN network:  How would you map tacit knowledge?  The post also referenced CVs as a way to document experience, but I would agree with the person posting that initial question that a CV doesn't scratch the surface of tacit knowledge. 

At best, a CV reflects a series of experiences and since the purpose of a CV is typically to send to potential employers ahead of an interview, the main purpose of a CV Is not to convey tacit knowledge.  A CV reflects experiences but doesn't say much about what the person learned from those experiences, either explicitly or in the form of tacit knowledge.  

I once went to a job interview with a simple concept map instead of a CV.  The map didn't reflect specific jobs or positions but areas I had experience with through these jobs.  More importantly, because it was a form of concept map and not a linear document, I was able to show how the combination of experiences had resulting in a mental map, a framework to connect these different experiences.  I think that in order to map tacit knowledge, we must reflect on our experiences and as a result, make that knowledge explicit.  You end up with something that is no longer tacit since you've "explicited" it through reflection.  That is still probably scratching the surface or barely digging into the underwater part of the tacit/explicit iceberg, but very much worth doing in my opinion. 

This is essentially a form of Personal Knowledge Management and the mapping doesn't necessarily have to be in the form of a concept map.  I just find it very useful because it helps connect things previously not connected in my mind.  

Let's start at the top.  By definition, tacit knowledge is not conscious, so mapping tacit knowledge sounds like an oxymoron.  However, I believe we can strive to make some of our tacit knowledge more explicit in order to map it out and the process of mapping can help make tacit knowledge more explicit.  

Why map it out you might say?  Isn't it enough to make it explicit?  Well, are you making it explicit in a blog or a personal journal that just accumulates content in a sequential way?  Mapping out that "explicited"  knowledge is a way to enable further articulation, further exploration of the connections between insights generated through reflection and analysis.  

I know "explicit" isn't a verb. I should refer to the SECI model and say "externalize" instead.  Before we can externalize or articulate that tacit knowledge, we need to have an idea of what we are looking for. Externalize feels like "extracting" knowledge and I don't like these harsh-sounding words.  

Here is an example of an attempt at externalization:  I have a lot of tacit knowledge around the construction of concept maps.  I have a lot of experience constructing concept maps and when asked how I do it or what constitutes a good concept maps, I find it difficult to answer.  I know a good concept map when I see it. This is also related to the curse of knowledge. I tried, years ago, to articulate (or externalize) that tacit knowledge by creating a couple of courses about concept mapping.  Essentially, I was trying to create the conditions for others to experience the process of constructing concept maps as the way for them to acquire that tacit knowledge themselves.  In trying to articulate that process, I felt the need to specify a number of steps or method, but that was a shortcut and not truly how I personally experience constructing concept maps and I can't go back in time and remember how I initially learned how to construct concept maps. All I was able to do was set up an environment where someone could go on a similar path to learn for themselves and acquire their own tacit knowledge. 

Because tacit knowledge is based on first-hand experience, it is unique to the individual.  Granted, every person who knows how to ride a bike acquires tacit knowledge of bicycling, but even that is somewhat unique to the individual experience.  I was passed by a large flock of real cyclists this morning on my morning bike ride.  They were going three times my speed and moving in ways I have never experienced because I don't ride in groups. Their accumulated tacit knowledge around cycling is very different from mine.  As a side note, if they regularly cycle as a team, they will acquire a form of collective intelligence based on their collective tacit knowledge.   There is a lot more to explore around collective intelligence and the ability of people to combine their explicit and explore their respective tacit knowledge through effective collaboration.  In fact, there is probably a lot around effective collaboration that is based on tacit knowledge rather than explicit best practices because it's in the realm of communication and human behavior that we engage in daily without giving it much thought -- unless you're a huge fan of personal knowledge management and you are a strong reflective practitioner.  

Going back to the original question:  How could anyone map their tacit knowledge? Again, you wouldn't map your tacit knowledge but rather externalize knowledge acquired through experience and map it. 

Is it only knowledge acquired through experience?  If I'm combining ideas from other people in new ways, it's not based on my first hand experience, but it's my accumulated experience that allows me to come up with this new way of combining ideas and come up with new insights.  And the more I have reflected upon my experience over time, the more raw and refined materials I have to draw from to articulate / externalize insights based on other people's experiences because I can relate them better to my experiences and create the connections.

So, here are some of the techniques useful to articulate/externalize and then map tacit knowledge:

• Reflection and journaling, closely linked to learning logs or learning diaries; blogging is also great but has a public dimension that may hinder more personal reflections.
• Concept mapping, closely related to mind mapping. 
• Interviews and dialogue, storytelling, very useful to get people to more organically access their tacit knowledge via skilled prompting.
• Observation and feedback (essentially qualitative methods used in apprenticeships for example).

The first two methods are the ones I use most regularly because they build on each other.  The reflection can be documented with a map and the process of generating a map inevitably leads to new avenues for reflection, (potentially) deepening access to tacit knowledge.  The benefit of a concept map over a more linear narrative is the ability to explicitly and visually show connection across ideas and concepts.  In a real concept map, the connecting words that link distinct concepts are perhaps the most important elements. That's also why relationships are the most important element in knowledge graphs... but that's another fascinating topic. 

Let's get back again to mapping newly externalized knowledge.  I am currently working on a project that seems to address a lot of that.  It's a combination of personal narrative via concept mapping and knowledge graphs.  I don't know where this project is going yet so perhaps I should keep it at that.   I gave myself the title of Knowledge Explorer, so I'm exploring, without a specific destination in mind because I haven't discovered any new territory yet. The new territory seems to be Knowledge Graphs, but I need to revisit old territory (concept maps) to become more insightful about Knowledge Graphs. This conversation is helping in some way to externalize that old territory raw material of experience. 

I can say that a key to reflection and journaling is "know thyself".  That's a starting point for personal knowledge management aimed at accessing tacit knowledge.  There is nothing wrong with personal knowledge management focused on optimal curation of one's readings and resources, but that's scratching the surface in the same way that a repository of documents in an organization is not equivalent to managing knowledge.  

So, my modern version of "know thyself" includes working on self-awareness, some mindful practices (to slow down the brain), values clarification (which helps me to focus on what's most important to me), deepening my reflective practice, exploring personal narratives, acknowledging cognitive biases, known unknowns and unknown unknowns, and setting personal learning goals while exploring my interests.  Given that this is all too cerebral, I'm integrating elements to "know your body" and engaging in a lot of movement because brain and body work together and a moving brain is much more capable than a sitting brain. 

Years of intermittent note-taking -- I can't really call that journaling -- have led to habits and lots of notebooks (physical and virtual) that remind me of who I was and what I was thinking.  This is also an aspect of "knowing oneself" and the evolution of one's mental frameworks but also being reminded of the stable core of who we truly are.  I can safely assume that I've acquired some bad habits along the way.  It would be interesting to try to identify those.  That would fall into the category of acknowledging biases.

Again, how should anyone proceed if they want to try to map their tacit knowledge or more accurately, externalize their tacit knowledge so it can be mapped?

1. Start with simple reflections around a specific project or event.  It can be overwhelming to try to reflect on the entirety of our daily experiences.  Focus is essential.  Think in terms of critical knowledge.   If you're not sure about what to focus on, keep a random journal for a few days or weeks and then review your entries to try to identify a recurring theme.  In fact, you could use a concept map to expose key themes that would then allow you to focus your reflections.   I've posted maps that were based on events or just individual readings. It's nothing fancy.

2. Define a learning agenda to further focus your reflective practice.  At first, it's probably best to identify a relatively narrow set of topics to focus on.  As discussed in another post, I undertook a pretty ambitious learning agenda this year with a broad set of topics.  That was intentional and it's something I can do in my current personal/work life.  It might not be sustainable if I were still working full time and managing a family. So, define a learning agenda that is reasonable and high value for you.  

3. Find ways to share.  Once you're comfortable with your own progress becoming a reflective practitioner, or just comfortable enough sharing, ''find ways to share'' what you are thinking, your insights, your learning process.  This should result in interesting interactions and further learning.  That is not my strength and I know it, which is probably why I just barely push a few posts on my blog and I consider myself "done" with the sharing.   

I also teach so perhaps that's where I end up sharing a lot of tacit knowledge without thinking too much about it, in a more organic way, based on conversations with students and the questions that come up. I may not be mapping that but hopefully some of it is still transferred and some of it is mapped in the student's own notes and mental models. 

Resources to Explore:  This is not an exhaustive list. 

Tacit Knowledge & Personal Knowledge Management

• The Tacit Dimension, by Michael Polanyi (of course)
• The Knowledge Creating Company, by Ikujiro Nonaka and Hirotaka Takeuchi (for the SECI model). 
• The Reflective Practitioner: How Professionals Think in Action, by Donald A. Schon. 
• Personal Knowledge Management: How to do it, with 25 resources and 10 books on PKM, by Stan Garfield
• The Fifth Discipline: The Art & Practice of the Learning Organization, by Peter M. Senge.  There is a nice focus on personal mastery and mental models which are crucial for understanding tacit knowledge. 
• Harold Jarche - Seek, Sense, Share Framework.
• There are 173 entries tagged as PKM on my old Diigo account.  I have not updated anything recently.   First entry dates from 2009 and a lot of the links are most likely dead.  That's also partly why collecting, storing and tagging links (curating) cannot be the full picture.  
• There are also a number of posts on this blog that I've tagged as PKM although I clearly have not posted on this topic in a long time or didn't tag. 

Some focused on becoming a better "learner", some more focused on managing information overload.

• Mindshift:  Break Through Obstacles to Learning and Discover Your Hidden Potential, by Barbara Oakley.  All the books by Barbara Oakley are focused on learning based on neuroscience insights and while targeted at "students" most insights are valuable more broadly to learning at any age, including learning from experience.
• Building A Second Brain: A Proven Method to Organize Your Digital Life and Unlock Your Creative potential, by Tiago Forte.
• Thriving on Overload: The Five Powers for Success in a World of Exponential Information, by Ross Dawson. 
• Riding the Current:  How to Deal with the Daily Deluge of Data, by Madelyn Blair. 

Concept Mapping Resources:

• Learning, Creating and Using Knowledge:  Concept Maps as Facilitative Tools in Schools and Corporations, by Joseph D. Novak
• The Mind Map Book, Tony and Barry Buzan
• Visual Tools for Transforming Information Into Knowledge, by David Hyerle

Some concept mapping resources are more focused on business applications and collaborative mapping:  

• Applied Concept Mapping: Capturing, Analyzing, and Organizing Knowledge. by Brian M. Moon, Robert R. Hoffman, Joseph D. Novak and Alberto J. Cañas.
• Visible Thinking: Unlocking Causal Mapping for Practical Business Results, by John M. Bryson, Fran Ackermann, Colin Eden, and Charles B. Finn. 

Localization and Local Knowledge(s)

 I may have posted on this topic in the past but my thinking often evolves, so this is unlikely to be a full repeat.

World Localization Day is approaching.  Localization Day is celebrated on June 21.  It is an annual celebration convened by Local Futures, an international NGO led by [[Helena Norberg-Hodge]].  

Localization in the context of international development assistance (an in particular USAID jargon) refers to a set of internal reforms, actions, and behavior changes undertaken by organizations (donors and their implementing partners) to ensure that their work prioritizes local actors.

The two uses of the term "localization" are obviously related, but the meaning is much more restrictive in the international development assistance context and the underlying beliefs and strategies do not always align.

I've hit my head against walls many times in the past trying to argue that conversations/narratives, and associated jargon emerging from the international development industry continues to reflect the needs of the industry more than anything else and do not reflect local needs and realities.  That's why "localization" within the development industry is all about reforming how the development industry itself should behave.  Localization day -- and all it represents, on the other hand, is totally independent of the international development industry and should be celebrated EVERYWHERE. When I buy produce from my farmers' market instead of the grocery store, I am supporting localization in a small but tangible way.

Similar distinctions are probably necessary when talking about local knowledge(s).  Local knowledge in the international development industry is often associated with indigenous knowledge.  That's potentially quite limiting. I suspect there is a lot of local knowledge that is not (or no longer) associated with a specific indigenous population.  Local knowledge can simply be a deep knowledge of the local context, its history, current trends, most of which may have little to do with traditional or indigenous knowledge. 

I don't know yet exactly how to think about local knowledge(s), but I want to avoid using the term assuming that everyone else using it is talking about the same thing. I also think it's related to different ways of knowing, because Western "rational" thinking isn't the only way to KNOW something. 

Knowledge Mapping in International Development - Webinar follow up

In a previous post, I mentioned that I was preparing for two related yet distinct presentations.  I presented the first of the two on May 23rd and I can now share the recording recording.  I don't dare listening to myself talk, so I will just trust that it was good enough and worth sharing.

  

The feedback and questions were very useful. 

Some people wanted to know more about the specific tools I use or recommend.  Unfortunately, I don't make recommendations about tools.  I can talk about what I use but tool selection requires a thorough understanding of the context and unless I'm engaged in a consulting assignment where I can gather information about the context, I stay away from recommending tools.  Even in the context of a consulting assignment, I would most likely come up with a list of options rather than a recommendation of a single tool. 

Some people would have wanted a practical session where they could engage in the process of developing a map themselves.  I have done that in my teaching and at one point I had a course in Skillshare to learn how to map.  That was just not the objective of this webinar but it does indicate an interest in the topic and the practical application.

I don't think I adequately focused on specific use cases in international development even though that was what the audience was most likely to be interested in. That is something I will try to remedy with future work.

And that brings me to the second session, this time in June, through the Knowledge Management Global Network (KMGN) research group.  My plan there is to present my own learning journey so far around concept mapping and knowledge graphs, and open up a discussion around opportunities for further action learning/action research and deep dives.  

From Local Nodes to Global Networks: Mapping Knowledge Ecosystems

 

I often come up with unconventional ideas when the tasks on my "to do" list undergo a melding process. Here's an example:

• Task A: Exploring knowledge graphs and their benefits.
• Task B: Reading the Agenda Knowledge for Development Goals and individual statements, while considering writing my own.

Combining these tasks led to a new question, both for the knowledge graph topic I am presenting on later in June and for my potential individual statement supporting the Agenda Knowledge for Development goals: Can knowledge graphs be developed to represent local knowledge ecosystems? If so, what would be the benefits?

From there, I began to consider:

• What could we learn from hundreds of local knowledge ecosystem graphs?
• How could these local graphs be linked into a global knowledge ecosystem graph?

One potential advantage might be that it would preserve the integrity of the local knowledge ecosystem. This idea is very meta because it's not just about the knowledge itself, but about the structure and interactions within the knowledge ecosystem(s). Is this just a case of wilding a hammer (knowledge graphs) and looking for nails to pound on?

Pause to Appreciate Your Knowledge Ecosystem

 

Morning Dew on Grape Leaf: Nature's Simple Beauty
Photo Credit: Barbara Fillip

In the early morning, the garden's grape vine shows off tiny dew droplets on its leaves. Each drop clings to the leaf's edge, reflecting the greenery around it. This moment captures the beauty of nature and its role in supporting life. As the sun rises, these droplets will help nourish the plant, contributing to the garden's ecosystem. Let's remember to pause and appreciate the complexity and beauty of the world around us.

In our hurried lives, we should also strive to pause and appreciate the complexity of the knowledge ecosystem that allow us to work collaboratively and achieve so much more as teams and broader entities than we could as individuals. We often take the knowledge ecosystem for granted just as we take our ecosystems' magical functions for granted.

Knowledge Explorer

 Why I Call Myself a "Knowledge Explorer"

As I recently returned to solo consulting, I had to figure out what to call myself.  It took about 30 seconds to settle on Knowledge Explorer.  I will use the title "Knowledge Explorer" because it captures my deep commitment to learning and exploring new ideas. Here's why this title fits me:

1. Curiosity-Driven: I am naturally curious and always eager to learn more. I enjoy exploring new fields and discovering new ideas, which keeps my work and life exciting.

2. Interdisciplinary Approach: My work benefits from combining knowledge from different areas. This helps me solve complex problems, especially in knowledge management, where understanding different perspectives is key.

3. Innovative Mindset: I'm always looking for new ways to improve how knowledge is used and understood. Staying open to new technologies and ideas is a big part of who I am.

4. Educator and Thought Leader: Teaching and sharing knowledge with others is important to me. I strive to make complex information clear and useful for everyone.

5. Commitment to Growth: Being an explorer means I never stop learning. Even as I plan for semi-retirement, I look forward to continuing my education and exploring new areas.

Calling myself a Knowledge Explorer reflects my ongoing quest for new insights and my dedication to growing and helping others grow. It's a perfect summary of my approach to work and life.

I have also updated a couple of pages on the site:

• About Insight Mapping
• Services

Think Globally, Act Locally

 A significant portion of my professional life has revolved around knowledge work, delving into analytics, and navigating the realm of words, documents, and evidence. It's a landscape of abstraction, particularly in the context of international development where my focus on knowledge management often leads me far from the tangible impacts we aim to achieve. While you can envision how your efforts contribute to projects worldwide, the chain of impact often feels elusive.

A couple of years ago, I made a deliberate shift towards more hands-on involvement, seeking tangible actions beyond the confines of my analytical mind. Volunteering became my avenue for hands-on local engagement, with two organizations in Arlington:

• Arlington Neighborhood Village, where I assist older citizens with garden tasks and engage in regular conversations with those who may benefit from more social contact.
• EcoAction Arlington, where I engage in various activities, from park clean-ups to improving housing for low-income residents, promoting energy and water conservation.

In embracing this ethos of localization, I've come to realize that sustainable development isn't bound by geography. It's a universal principle, applicable everywhere. I take comfort in being able to act locally in alignment with global sustainable development goals.

Through volunteering, I've gained insights into the diverse fabric of my community, encountering individuals and systems that enrich my understanding of local dynamics and governance structures, imperfect as they may be.

Receiving the Impact Award from EcoAction Arlington last week was a humbling affirmation of these efforts.

Holding my Impact Award.  Photo by Alexandra Fillip. 

Curated Nuggets of Knowledge - Week ending 4/28/2024

Let's start with a quote I recently curated.

"I'm drawn to the idea that the key to creating in the age of information abundance is to become a skilled curator. With so much content available, the ability to sift through the noise and identify the most relevant, compelling, and thought-provoking ideas becomes invaluable." 

~Joan Westenberg, How to be a creator in the age of information overload, Medium, March 2024.

Knowledge Mapping: Preparing for Two Upcoming -- related but different -- Presentations

Knowledge Mapping has been an interest of mine for years, perhaps a couple of decades now.  While I have treated it as a niche area of interest and expertise rather than a specific service or method, I now have the freedom to explore and see if there is more to it.  More recently, knowledge mapping has led me to knowledge graphs and a whole new world of technology-enhanced mapping.  

And now I have an opportunity to talk about Knowledge Mapping in a more public arena, through my friends at Consult KM International on May 23rd and then with the KMGN Research Community on June 19th.  As I prepare for the presentations, I am faced with a couple of challenges.  I have not had to explain knowledge mapping to many people. I tried it in February with a small, safe audience and I learned a few things from that. Most importantly, I need to be as clear as possible about what I mean by "knowledge mapping", how it can be used -- without overwhelming the audience with too many examples -- and I need to stick to a relatively simple message.  However it's okay to say "I'm still exploring this and I don't have success stories or proof that it works in every context".

I have also not been very consistent with names.  I call it "Applied Insight Mapping" and "Insight Mapping" on this site.  I've called it "KMAPs" when I was doing mapping at NASA.  I've also referred to it as conversation mapping because I was using maps to document Pause and Learn sessions which are essentially facilitated conversations.

Knowledge Mapping in the context of Knowledge Management can also refer to a specific set of practices meant to document organizational knowledge.  I apply the term more broadly to refer to any visual representation of a knowledge domain that relies on words and concepts rather than images, with a focus on relationships between components of the map (nodes).  That's where it connects with knowledge graphs.

So, how do I coherently talk about something that has been so deeply engrained in my work for so many years. Serendipity to the rescue.  I opened LinkedIn and came across a post that points to this course:  Curse of Knowledge for Specialists.  I must have posted about the curse of knowledge in the past but here I need to watch that I don't fall prey to this cognitive bias as I prepare for this presentation. 

The two presentations are related but intended for different audiences.  The first one will focus on "mapping" and its possible applications, based on 20+ years of experience with concept mapping, and touching on knowledge graphs perhaps just at the end while the second presentation is more about my learning journey looking forward, exploring knowledge graphs as a (scaled and automated) extension of concept mapping. 

For more information about these upcoming presentations:

•  ConsultKM International, Knowledge Mapping in International Development: Applications, Purpose, and Benefits, "Live with an Expert" session #4, May 23, 2024. 
  
  
•  KMGN Research Community: From Concept Maps to Knowledge Graphs: A Learning Journey and Possible Research Agenda, June 19, 2024. 

Atomic Notes for Personal Knowledge Graphs

I came across the work of Ivo Velitchkov on Personal Knowledge Graphs yesterday.  Some of it goes well beyond my current level of comprehension because of the technical aspects but I enjoyed the more conceptual elements and the history of the evolution of Personal Knowledge Management (PKM). 

Digging deeper into technology-enhanced notetaking made me question the way I take notes and whether I need to make better use of some of the more advanced functionalities of TiddlyWiki, and by extension, TiddlyMap.  In particular, transclusion seems to offer ways to create more atomic notes.  

For example, taking notes on a book I am reading in a single page or Tiddler) could result in a very long page with many different ideas.  I can tag the page with keywords but I want to be able to quickly reference specific elements of my notes.  I want to be able to quickly pull a specific visual.  The way to do that is by creating "atomic notes" that are then transcluded in the page for that book.  Each atomic note becomes a node in the graph, with its own properties and relationships to other nodes. 

Created by DALL.E:  Atomic Notes in the Context
of a Personal Knowledge Graph 4/24/2024.

This exploration into note taking also made me wonder whether it would be worth revisiting all kinds of paper-based notebooks I have accumulated over the years into my growing TiddlyMap.  The first notebook I came across is a small notebook meant to capture quotes.  I know I also have collections of quotes in other TiddlyWikis.  Bringing everything together (with a focus on the key themes I have already identified) would be a very integrative exercise.

Another question worth exploring is whether it would be possible to integrate personal knowledge graphs with enterprise knowledge graphs.  That is, to some extent, what the Microsoft Graph does and how Microsoft Copilot for M365 pulls content from individual employees' email, Teams, files, and combines that with whatever enterprise content the individual employee also has access to.  

What if there were a way to structure OneNote notebooks for atomic notes and a personal knowledge graph?  What if that personal knowledge graph could be integrated into an enterprise knowledge graph?  I am not suggesting that a personal knowledge graph be share with the enterprise.  I am only suggesting that an individual employee should have easy access to their own personal graph, the enterprise knowledge graph, AND external graphs (like Google's Knowledge Graph) through a single interface. 

White Spaces and Unknown Unknowns

I dug up some coloring books from the basement's collection of arts and crafts supplies dating from when my kids were growing up.  I needed something to occupy my hands and empty my brain.  Ironically, that tends to be when ideas show up unexpectedly.  

I was attacking my third drawing when suddenly the decision I had to make wasn't which color marker to pick, but whether or not to color the "white space".  The pre-printed lines clearly show shapes and the brain immediately interprets the area between lines as something to be colored.  This happens even with geometric shapes or lines that don't represent a recognizable object.  Of course, one could decide to leave some of those shapes uncolored, but that means they are intentionally left white.  What I was wondering about was whether or not to color the white space that corresponds to "empty space" or negative space.  

In cognitive science, this relates to selective attention.  Our brain focuses on certain stimuli while ignoring others.  We notice some things and miss others.  Refocusing my attention to the empty spaces was a way to alter my focus and consequently, my perception of the image I was coloring.  I refocused on the negative space. This is somewhat similar to or related to the concept of unknown unknowns, the things we don't know we don't know.  We're oblivious to our own blindness and ignorance.  

What am I not seeing?  What am I missing?  How could I see differently?

Explorations and Discovery: Looking for Adjacent Content

Exploration is more than just discovering what we agree with; it's about pushing boundaries and challenging our perceptions.

The words exploration and discovery go well together.  If you start on an exploration journey, you are likely to make some discoveries.  It's even a little more exciting than going on a learning journey and collecting some lessons. Words matter.

Today I discovered someone I now want to follow because what I read from that person resonated strongly.  This is part of the problem.  We tend to read and follow what resonates, what we agree with, which leads to reading more of the same people who write things that resonate with us, which only reinforces our opinion of ourselves and allows us to dismiss everything else either as crazy or just noise.  

I would like my explorations to lead me to things that are adjacent to what resonates with me, to push the boundaries a little, to expand the zone of what resonates with me to what makes me think and rethink.  

I am particularly interested in this discovery because it will expand my thinking.  I discovered Joan Westenberg's blog and other writings.  I discovered her via Harold Jarche's blog.  Harold Jarche is already on the edge of my comfort zone and someone I have followed closely for a long time around Personal Knowledge Management.  Joan Westenberg is pushing things in the same zone of discovery.

What's the point I am trying to make?

In an attempt to curate interesting, relevant information, and to support my explorations for valuable discoveries, I need to strategically pay attention beyond what immediately resonates with me and notice the adjacent content that can take my own thinking one step further. 

2024 Explorations - Q1 Review

I started my 2024 Explorations in January. It was meant as a combination of two main objectives:

1) a learning framework or learning agenda, not so much to ensure that I would engage in continuous learning but more to ensure that my continuous learning was adequately focused on some key themes of interest;

2) using technology, and more specifically TiddlyMap, as a personal knowledge management tool that would allow me to experiment with (a form of) knowledge graph.

We've reached the end of the first quarter of 2024 and so far so good. I just completed a quarterly review of progress and generated a few insights.

• Is TiddlyMap allowing me to really learn about knowledge graphs? Yes, but as expected, it has its limitations. I will eventually crash the tool. I don't think it is meant as a graph database but it works well as an exploratory tool. Ultimately I need to move my data to a real knowledge graph tool like Neo4J. That should be a goal for the second quarterly. I started learning more about Neo4J, including learning the basics of Cypher.
  
  
• Is my learning framework working? The main themes and topics have proven very useful as guardrails and as an organization schema both for my thinking and for capturing notes. There are some issues with the taxonomy. Some topics are overlapping and I keep wanting to create more tags. So far, I have limited the number of additional tags and I have only made minor adjustments to the topic tags. Proliferation of tags would lead to inconsistencies. Until the tagging is automated, the number of tags is limited by my capacity to remember them all.
  
  The maps are telling me something pretty clear. I have focused perhaps 80% of my efforts on the AI and Knowledge Graph topics. The maps related to those topics are very large, which has enabled me to test filters. Like a search returning too many results, a map showing too many relationships is unreadable. For other topics, I have collected and curated resources, but I have not spent time connecting the dots. As a result, the maps are less interesting (so far).
  
  
  

• Is the basic ontology working? Yes, but the value of TiddlyMap's automated functionalities has made it much more powerful to create maps based on what TiddlyMap does with relationships based on tags and links associated with Tiddlers than to manually create a specific set of relationships based on my simple ontology. I have learned most by manipulating the filters to understand how relationships are displayed. Interpreting the resulting maps for potential insights is the next stage I want to dive into. Since I am the one creating all the links and the tags, the maps are not telling me anything I didn't already notice, but they are representing the connections visually and often reminding me of connections I made weeks ago that I don't hold in immediate memory.  I posted one of the maps in the Insight Maps section. 

The biggest ha-ha moment was related to tagging. I was using the tagging functionality to tag too many different types of things and failing to use a major functionality of the tool. I realized after a while that I should be using the fields to document the properties of a node. For example, "author" is a field rather than a tag. This allows me to have a consistent set of node properties and to rely on tags only for topics and some of the metadata used for navigation purposes. This also really helped make the maps more meaningful.

Using a GPT to get updates in topics of interest

About a month ago, I created a GPT based on ChatGPT 4.0.  It's easy to create but requires some fine-tuning.  I used Ross Dawson's approach detailed here:  Creating custom GPTs for news and Information Scanning; and I adjusted it to suit my own purpose.  The results have been mixed but I'm reasonably happy with what I got today. 

Critical Cognitive Capabilities in the Age of AI

In an era increasingly dominated by artificial intelligence (AI), our cognitive landscape is undergoing a transformation as significant as any in our history. This shift demands not just an adaptation but a deliberate enhancement of our cognitive capabilities. Amidst this technological evolution, the cultivation of critical thinking, emotional intelligence, creativity, and adaptive learning emerges as essential to thrive.

Created by DALL.E, with human input.  A vision of a harmonious collaboration between humans and AI, showcasing human collective intelligence, creativity and innovation, augmented by the capabilities of technology, including AI. 

The story of Theuth and his invention of writing, as recounted by Socrates (see the previous post), provides a profound starting point for this discussion. Just as the introduction of writing raised concerns about memory and wisdom, today's rapid advancements in AI and the Internet pose new challenges and opportunities for human cognition. In this digital age, the capacity for ''critical thinking'' has never been more important. As we navigate vast oceans of information, discerning fact from fiction, valuable data from noise, requires a keen analytical mind. This skill ensures we remain effective decision-makers in both personal and professional realms, notwithstanding the deluge of AI-generated content and analysis.

Emotional intelligence stands out as a uniquely human attribute that AI is far from replicating. Our ability to understand, empathize, and interact with others is paramount, especially as AI technologies handle more cognitive tasks. Developing emotional intelligence helps us navigate the complexities of human relationships and teamwork, fostering environments where collaboration between humans and AI tools is productive and innovative.

Creativity is another domain where humans can excel beyond AI's capabilities. While AI can generate new patterns and ideas based on existing data, the human capacity to think abstractly, imagine the unimaginable, and connect disparate concepts in novel ways remains unmatched. Encouraging creativity in education and the workplace ensures that as AI takes over more routine or analytical tasks, humans will continue to lead in innovation, design, and artistic expression.

Lastly, the concept of adaptive learning is crucial. Just as AI systems learn and evolve based on new data, so too must we. However, our learning is not just about absorbing information; it's about adapting to new ways of thinking, new technologies, and changing societal norms. This ability to learn and relearn throughout life is what will keep us relevant and resilient in the face of rapid technological changes.

As we consider the future in this age of AI,  our focus should not only be on developing technical skills to use and manage AI systems. Instead, we must emphasize the uniquely human capabilities that will complement AI's growth. By nurturing critical thinking, emotional intelligence, creativity, and adaptive learning, we prepare ourselves not just to coexist with AI but to lead a future where technology enhances our human experience, not diminishes it.

Of related interest:

• Gianni Giacomelli's "With AI, learning and reskilling ≠ training|", blog post of 3/25/2024. 

The Echoes of Theuth: From Writing to the Internet and AI

In Plato's "Phaedrus," Socrates recounts the tale of Theuth, the Egyptian god of writing, presenting his invention to King Thamus. This ancient narrative, exploring the invention's impact on memory and wisdom, mirrors the last couple of decades' discourse on the emergence of the Internet, and now, Artificial Intelligence.  This post's primary lens focuses on cognitive implications.  There are, of course, broader concerns the digital age and AI have introduced.

Revisiting Theuth in the Age of Information and AI

Theuth's claim that writing would enhance memory and wisdom was met with skepticism by Thamus, who argued it would instead weaken memory and give only an illusion of wisdom. This cautionary perspective finds its echo in the modern era, first with the Internet, and now more profoundly with AI. Both technologies, while distinct, share a common thread in their transformative impact on how we acquire, process, and value knowledge.

The Internet: A Precursor to AI's Cognitive Challenge

Before AI became a household term, the Internet had already begun reshaping our cognitive landscapes. Dubbed as an external "hard drive" for our collective memory, it introduced the "Google effect," where the ease of accessing information led to a potential decline in memory retention and effort in learning (See Nicholas Carr's book, The Shallows). The vast, accessible sea of data promised knowledge but often delivered a surface-level engagement with complex subjects, mirroring Socrates' concerns about the written word.

AI and Beyond: A Continuation of Digital Age Dilemmas

AI amplifies these concerns, offering unparalleled access to information and automating tasks with efficiency but at the potential cost of diminishing our cognitive faculties. The ''illusion of wisdom'', where individuals may overestimate their understanding due to the breadth of accessible information, becomes an even greater risk. As AI systems take on more roles that require analysis, decision-making, and even creativity, the question of what it means to truly know or understand something becomes increasingly pertinent.

Acknowledging the Spectrum of Concerns

While I focus here on cognitive effects, I also want to recognize that the challenges posed by AI and the Internet are multifaceted. Ethical dilemmas, privacy breaches, algorithmic biases, and the digital divide are significant issues that warrant attention. The societal impact of these technologies stretches beyond individual cognitive abilities, affecting our collective moral and social frameworks.

Charting a Thoughtful Path Forward

In navigating the future of AI and the digital landscape, a balanced, thoughtful approach is essential. By critically assessing the benefits and potential pitfalls, especially in how these technologies influence human cognition, we honor the Socratic tradition of deep questioning. This not only involves scrutinizing AI's capabilities and impacts but also reflecting on how the Internet has set the stage for today's digital challenges.

As we continue to integrate AI and digital technologies into our lives, let's maintain a critical eye towards their impact on our cognitive abilities and society. The story of Theuth, extending through the age of the Internet to the dawn of AI, serves as a valuable framework for understanding these challenges, encouraging us to ensure that technology enhances, rather than diminishes, our human experience.

----

I used AI to research and write this post.  To what extent did that contribute to a potential decline of my cognitive capabilities -- regardless of inevitable age-related decline?  To what extent did it enhance my knowledge, understanding, and cognitive capabilities?  How would I know?  What can I do to prevent cognitive decline related to technology use (or overuse) while leveraging technology to enhance my access to and use of knowledge?

The Evolution of Content Management: From Static Documents to Dynamic Collaboration

In the digital age, content management has become a cornerstone of knowledge work, enabling us to organize, access, and share information like never before. My journey through various tools and concepts in content management has illuminated a fundamental shift: from managing static documents to engaging in dynamic, collaborative content creation. This post explores this evolution through my experiences with TiddlyMap (starting almost 10 years ago), Learning Management Systems (LMS), Knowledge Graphs, and Microsoft Loop.

Discovering Transclusion in TiddlyMap

My exploration began with TiddlyMap, a tool that blurs the lines between notetaking and concept mapping. It's where I first encountered the concept of transclusion. This feature allows content from one Tiddler (note) to be included in another seamlessly, ensuring that updates are reflected universally. The result? A single source of truth within my personal knowledge base, facilitating a modular organization of content that is both efficient and consistent.  (See Transclusion in WikiText)

Key Takeaway: Transclusion in TiddlyMap showcased the power of interconnected content, highlighting the importance of maintaining consistency and efficiency in personal knowledge management.

Revisiting Reusable Objects in Learning Management Systems

My journey took me back to the concept of reusable objects in LMS, something I had encountered earlier. These digital resources can be utilized across various courses or modules, embodying the principle of modularity and reuse. This approach not only saves time and resources but also ensures consistency across the educational spectrum.

Key Takeaway: The practice of creating and using reusable objects in education underscores the need for content that is both flexible and adaptable, catering to diverse learning contexts and styles.

Connecting the Dots with Componentized Content

A recent webinar on Knowledge Graphs brought the term "componentized content" into sharper focus for me. This concept, akin to reusable objects, emphasizes breaking down content into manageable, standalone components that can be dynamically assembled. It resonated with my experiences, highlighting a broader trend toward agile and responsive content management systems that can evolve with our needs.(See Taking Content Personalization to the Next Level: Graphs and Componentized Content Management). 

Key Takeaway: Componentized content is at the heart of modern content management, reflecting a shift towards more agile, responsive, and interconnected systems that can support complex information ecosystems.

Experimenting with Microsoft Loop

My exploration culminated with Microsoft Loop, a tool that epitomizes the modern ethos of collaborative work. Loop's components are modular pieces of content that teams can collaboratively edit in real-time, streamlining the way we work together. This real-time collaboration, without duplicating content, signals a new era of efficiency and connectedness in teamwork.  (See Get to Know Loop Components)

Key Takeaway: Microsoft Loop represents the future of collaborative work, where dynamic, component-based content and real-time collaboration drive productivity and innovation.

Conclusion:

We're moving from static, siloed documents to a world where content is dynamic, interconnected, and collaborative. This evolution is not just technological but philosophical, changing how we think about knowledge, learning, and work.

These tools and concepts have reshaped my approach to content management, pushing me towards more flexible, efficient, and collaborative methods. They highlight a broader shift in our digital landscape, one that values modularity, reusability, and collaboration above all.  While my personal knowledge management tools re often a playground for learning, the biggest value may come from collaboration and ultimately,  the combination of people and tools to achieve augmented collective intelligence (ACI).

Final Thought:

Testing new tools is always fun (to me). They offer a glimpse into the future of content management—a future where knowledge is more accessible, collaboration is seamless, and learning is boundless. At the same time, it is worth reminding ourselves -- repeatedly -- that the tools are meant to enhance human capabilities.  Some will be more effective at enhancing individual capabilities, like TiddlyMap, while others are designed for collaboration and enhanced team or group capabilities. 

Next I have to think about the implications of this evolution for Knowledge Management and how we might need to rethink our knowledge management models and approach to knowledge assets.

Start with the problem(s) and prioritize

I recently read an interesting piece from Harvard Business Review titled "Find the AI Approach That Fits the Problem You're Trying to Solve " The essence of the article resonates deeply with my own beliefs, particularly around the notion that effective problem-solving begins with asking the right questions. Statements such as "without the right questions, you'll be starting your journey in the wrong place" and "Start with the problem, not the technology" echo a seemingly obvious yet profoundly complex reality.

This concept, while straightforward, is far from simplistic. In the realm of international development, organizations are confronted with a labyrinth of challenges, far beyond the scope of a singular issue. It's not just about identifying a problem and pairing it with a technological solution. There lies a critical, yet often overlooked step: prioritization.

Consider the diverse array of organizations striving to address global development issues. The challenge isn't merely in selecting a single problem but in discerning which lever to pull for maximal impact. Should technology then be primarily leveraged to navigate these strategic decisions, allocating resources more effectively?

While funding agencies may gravitate towards these macro questions, implementing organizations face more pragmatic concerns. Their focus often shifts towards securing necessary funding, leveraging technology to streamline grant seeking and proposal writing processes. This delineation underscores a fundamental principle: the application of technology, particularly AI, must be tailored not just to the problem at hand, but to the scale and scope of the organization's mission and resources.  

Funding agencies are not going to fund implementers to improve their proposal development mechanisms, but they could and will fund efforts to leverage technology (including AI) to address global challenges.  To what extent will that funding go to macro questions around levers for maximum impact?