Research Of Knowledge Retrieval In The Digital Document Management Systems

In recent years, the power of knowledge management (KM) is widely recognized. Most enterprises consider that their continued survival in industry mainly depends on the successful implementation of KM. In terms of computer science views, KM is the methodology which is based on Internet and information technology, help enterprises manage knowledge resources systematically, locate expertise in organizations, build expert nets for teamwork and access special knowledge conveniently for better decision-making, etc. KM is meant to promote ongoing business success through a formal, structured initiative to improve the creation, collaboration in an organization.Nowadays, the research on knowledge management is in the exploring period. It has many problems in KM systems. The same situations are in knowledge retrieval of digital document management systems (DDMS), which is a part of KM system. In knowledge retrieval process, the main purpose of users is not to look for special data, but to learn or think through studying a set of relevant artifacts across several different application domains. Furthermore, the things that can stimulate or inspire them to generate new ideas in problem solving or let them think in number of parallel ways. At present, many established DDMSs often suffer from non-use. The main reason is the deficiency of research on knowledge representation and organization. Once above issues resolved, the efficiencies of DDMSs are greatly improved on.In this paper, the index system and human-computer interaction (HCI) of DDMS, which provide cognitive supports for users to improve the quality andefficiency of knowledge retrieval are discussed. We propose semantic-based algorithms in order to construct the index system of DDMS. The methods are verified by the design of a prototype of DDMS. We also study the paper ranking based on the similarity of the profiles or the importance of papers. The effect of the rankings is evaluated by informal user study and the comparison experiments with the traditional ones. We integrate the techniques used in natural language processing and taxonomies to understand users' searching requirement and locate the artifacts that users may need in DDMS. Moreover, an iterative and interactive knowledge retrieval algorithm is designed, which can learn about customers' true characteristic intentions through iterative interactions.The proposed theory and models in the paper are verified by algorithm analysis and prototype experiments. It is proved that all kinds of methods proposed in the paper can provide better cognitive supports for users to improve the efficiency during the process of knowledge retrieval.