Continuing with this series on tools to help financial professionals get graphical, my last blog entry focused on a software product I have found very helpful over the last ten years. I use it to quickly capture, and look for organization in, my own thoughts - as well as using it to catalyze and organize group discussions. That technique is known as Mind Mapping. Now onto Mind Mapping “on steroids”.

I want to expand on that concept by looking at a related set of tools and techniques called Topic Maps. Topics Maps is an ISO standard (ISO/IEC 13250). According to the ISO, “Topic Maps is a technology for encoding knowledge and connecting this encoded knowledge to relevant information resources. Topic maps are organized around topics, which represent subjects of discourse; associations, representing relationships between the subjects; and occurrences, which connect the subjects to pertinent information resources.”

I first heard of Topic Maps twenty years ago. At the beginning of the development of XBRL, the technical folks (Specification Working Group) evaluated whether Topic Maps would be an appropriate tool for expressing the details and interrelationships of financial statements and other representations within the business reporting supply chain, but chose not to. Topic Maps isn’t really strong with numbers, which is a bit of a big thing in business reporting. I recently had the opportunity to dig into Topic Maps some twenty years later, working with one of its original co-authors/inventors, Michel Biezunski.

Michel is a highly sought-after speaker in light of Google’s (and other knowledge organizations) Knowledge Graphs (models of knowledge domains developed by SMEs with the help of machine learning algorithms, largely used to assist and inform machines). Knowledge Graphs have their basis in Topic Maps.

Topic Maps is best known as a tool for creating indexes for publishing – not the kind of thing that just pops out at you as having specific value to the financial professional. However, Michel let me know that his continued goal for Topic Maps is helping people capture knowledge, to facilitate free association and capture, and organize, the results. As such, both mind mapping and topic mapping tools can be used for free-form, spontaneous thinking

As I compare my longer-term experience (Mind Manager from Mindjet) with Michel’s own offering (Networker by Infoloom), I see one very important difference: formalization of not only the ideas (the topics, or subjects, but also of the connections between the ideas (the associations). Mind mappers let you capture multiple relationships between your thoughts. topic mappers not only capture relationships, but formalize the relationships, so you can capture the relationships between the relationships. 

In Mind Mapping, capturing the thought “XBRL is based on XML, which is a markup language” would have objects for “XBRL”, “XML”, and “markup language”, with connections between them.

With Topic Mapping, “is based on” and “which is a” (the associations) would also be objects, as well as “XBRL | is based on | XML” and “XML | which is a | “markup language” (the occurrences). That makes these associations and compound occurrences referenceable and connectable as well. For my XBRL savvy colleagues, or others accustomed to a separation between “metadata” (the “information about the information”, kept in schemas or taxonomies) and “data” (the “content described by the metadata”), this “Inception” like cascade of “everything is data” takes a little getting used to and, as with any model, has advantages and disadvantages compared with other ways to model knowledge. It’s not just turtles all the way down; it’s associations all the way down.

With Michel’s tool, you can provide alias and foreign counterparts to the names of each item, add notes, and provide scope limitations, amongst other features.

As such, Topic Maps is a broader-based tool for people to work with knowledge/information with more rigor than mind mapping. Some people have said the Topic Maps is a foundational tool for collecting knowledge from people in preparation for the Artificial Intelligence era. As such, Topic Maps may be a great way to capture your knowledge to share with people today … and machines tomorrow.