The role of manual & automatic tagging (classification and categorization) and information & knowledge management strategies in influencing information search and discovery in the enterprise.
A third paper has been accepted for presentation by the International Society for Knowledge Organization (ISKO) 2015 conference in London.
The best of both worlds: Highlighting the synergies of combining knowledge modelling and automated techniques to improve information search and discovery in oil and gas exploration.
Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have in order to generate value and reduce risk. In response, many organizations have deployed some form of enterprise search to improve the ‘findability’ of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a Knowledge Organization System (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. Drawing on prior empirical and theoretical research, an interdisciplinary theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help re-conceptualize the ‘manual’ versus ‘automatic’ debate in many enterprises, accommodating a broader range of industry needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what is often perceived as mutually exclusive competing approaches. Knowledge Organization (KO) itself may have evolved to a point where it has become difficult to distinguish it as a discrete discipline, but nevertheless plays a crucial role in a new interdisciplinary field.