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Knowledge Engineering: AI and KM- the perfect marriage

1 July 2021
Dr. Moria Levy
Knowledge Engineering chart

KMGN: KM-AI course Session 17, July 27th. Today Art opens a window to the fabulous world of knowledge engineering. We are past the first stage of our AI project, and we already know what business challenge we are dealing with. Knowledge engineering is the process we carry to turn the business issue into data and algorithms that will operate on them. Note: Refer to the SECI model components of it.


Step 1: Understand the baseline business process; understand the problems and challenges of this issue- syntax, semantic and perspective. Understand their root causes (Externalization).


Step 2: Describe the model. Describe it in free format (Externalization) and then extract a structured description (Combination). Of course, automatic knowledge graph construction can serve well for this step. Be sure your taxonomy is in place.


Step 3: Let the machine analyze the data and try learning from it (Combination).


Step 4: Share the initial ideas and add Subject Matter Experts' input (Socialization).


Step 5: Perform some sensemaking. Understand the findings (Externalization).


Step 6: Return to both / either expert and machine, improving their understandings (Internalization).


Perform the last steps again and again. Upon completion- celebrate success!



 This post was initially published in LinkedIn


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