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Abstract

A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developing methods to automatically manage Knowledge Bases which can serve as a knowledge source for computer systems trying to replicate the decision-making ability of human experts. Despite of most of the tasks involved in the building, exploitation and maintenance of KBs are far from being trivial, and significant progress has been made during the last years. However, there are still a number of challenges that remain open. In fact, there are some issues to be addressed in order to empirically prove the technology for systems of this kind to be mature and reliable.

5. Conclusions

In this work, we have presented the current state-of-the-art, problems that are still open and future research challenges for automated knowledge-base management. Our aim is to overview the past, present and future of this discipline so that complex expert systems exploiting knowledge from knowledge bases can be automatically developed and practically used. Concerning the state-of-the art, we have surveyed the current methods and techniques covering the complete life cycle for automated knowledge management, including automatic building, exploitation and maintenance of KBs, and all their associated tasks. That it is to say, knowledge acquisition, representation, storage and manipulation for automatic building of KBs. Knowledge reasoning, retrieval and sharing for exploitation of KBs, and knowledge meta-modeling, integration and validation for the automatic maintenance. From the current state-of-the-art, we have identified some problems that remain open and represent a bottleneck that is avoiding the rapid proliferation of systems of this kind. In fact, we have identified flaws in some areas including: (a) automatic generation of large KB, (b) lack of efficiency in methods for exploiting KBs, (c) lack of automatic methods for smartly configuring maintenance tasks, and (d) need of improving explanation delivery mechanisms.