Knowledge management for law firms and law departments has been pursued for decades, but the overall success given the investment seems debatable. It has been proven difficult to collect the unstructured text of lawyers in a system that others find useful enough to justify the cost.
Perhaps machine learning and natural language processing will replace the older paradigm of contributions by lawyers of their work product, often with key words extracted or sometimes with full-text searching, by a paradigm of software sifting through everything that is saved on a firm or law department’s servers, enriched by semantic networks or taxonomies created by software. Natural language processing (NLP) can create the infrastructure of knowledge without lawyers taking any of their time. Stated differently in the words of the lead articles of a recent publication, data analytics is potentially a “powerful force for increasing knowledge management by amplifying existing data.” If you can parse and organize and enrich material collected in the ordinary course of legal business, you can boost KM efforts enormously.
These dots connected for me as I read KMWorld, Oct. 2016, at S18 of its white paper on cognitive computing best practices.