Knowledge map-based method for domain knowledge browsing

2014 | Decision Support Systems | Citations: 0

Authors: Hao, Jia; Yan, Yan; Gong, Lin; Wang, Guoxin; Lin, Jianjun

Abstract: The exponential growth of available information and the deployment of knowledge ... Expand

Abstract: The exponential growth of available information and the deployment of knowledge management systems delivers excessive information to the end users that they cannot manage at once. This problem has led to an increased emphasis on solutions to information overload. Searching and browsing are two methods to locate information. Many studies have focused on solving the information overload problem in the searching process, but the methods to alleviate information overload in browsing process have not been adequately studied. Hence, a method that addresses information overload in the browsing process is presented in this paper. The aim is to reduce the information overload during browsing domain knowledge for new knowledge users who have little understanding of the information. In this method, a knowledge map and social network analysis are utilized to navigate the knowledge users. Technologies first construct a knowledge map from text mining and the important knowledge that includes more information about the domain is then identified via social network analysis. Based on this process, the knowledge user can browse the domain knowledge starting from the important knowledge and navigate via the knowledge map. We applied the method to assist new knowledge users in browsing the Computer Numerical Control (CNC) domain knowledge base to validate the method. The results indicate that the method can identify the important knowledge at a highly acceptable level, the constructed knowledge map can efficiently navigate the knowledge users, and the information overload can be significantly decreased. Collapse

Topics: information overload knowledge base database system Java world wide web

Methods: experiment cluster analysis social network analysis natural language processing term frequency–inverse document frequency

Theories: graph theory

On linear mixture of expert approaches to information retrieval

2006 | Decision Support Systems | Citations: 6

Authors: Fan, Weiguo; Gordon, Michael; Pathak, Praveen

Abstract: Knowledge intensive organizations have vast array of information contained in la ... Expand

Abstract: Knowledge intensive organizations have vast array of information contained in large document repositories. With the advent of E-commerce and corporate intranets/extranets, these repositories are expected to grow at a fast pace. This explosive growth has led to huge, fragmented, and unstructured document collections. Although it has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. Information Retrieval systems help users identify relevant documents for their information needs. Matching functions match the information in documents with that required by users in terms of queries to produce a set of documents to be presented to the users. It is well known that a single matching function does not produce the best retrieval results for all contexts (documents and queries). In this paper we combine the results obtained from well known matching functions in the literature. We employ Genetic Algorithms to do such combinations and test our method using a large well known document dataset. It is observed that our method produces better retrieval results for both the consensus search and the routing tasks in information retrieval. Collapse

Topics: information retrieval expert system document database

Methods: experiment computational algorithm paired t-test machine learning term frequency–inverse document frequency

Improving Relevance Feedback with Unbiased Estimate of User's Information Need

2001 | International Conference on Information Systems | Citations: 0

Authors: Xu, Yunjie

Abstract: Relevance feedback is an effective and widely accepted method in information ret ... Expand

Abstract: Relevance feedback is an effective and widely accepted method in information retrieval to improve performance. Relevance feedback generally uses an adaptive learning method to estimate the user’s information need. In this research, we propose an alternative two-stage sampling method to obtain an unbiased estimate of the user’s information need. Our estimate shows not only improved retrieval performance, but also better prevention of query drift, which troubles traditional relevance feedback. We also give theoretical justification and empirical support for this method. Collapse

Topics: information retrieval document database database system

Methods: experiment descriptive statistic parametric test paired t-test term frequency–inverse document frequency