Towards a Framework for Discovering Project-Based Knowledge Maps

Managing enterprise knowledge for decision support is crucial for enterprises to gain competitive advantages in knowledge-based economy. The valuable knowledge patterns hidden in numerous projects are important assets of enterprises. The management of such project knowledge is becoming increasingly important and challenging for organizational adaptation and survival in the face of continuous environmental change. This work proposes a project-based knowledge map framework to capture project knowledge and discover valuable knowledge patterns from previous projects. A collaborative two-phase data mining approach is applied to extract valuable project attributes, and discover their associations. Moreover, the discovered knowledge patterns are organized in a well-structured knowledge map, which facilitates effective navigation of project knowledge

Discovery and Support of Problem-Solving Knowledge in e-Business

Employees generally need to solve various situations of problems occurred during task-executions in e-business. Such problem-solving behaviors contain rich information helpful to provide effective knowledge support. This work proposes a mining framework to discover problem-solving knowledge by analyzing problem-solving behaviors recorded in an intranet portal log file. The discovered knowledge can provide guidance and support to assist employees handle various situations of problems

Patent Classification Using Ontology-Based Patent Network Analysis

Patent management is increasingly important for organizations to sustain their competitive advantage. The classification of patents is essential for patent management and industrial analysis. In this study, we propose a novel patent network-based classification method to analyze query patents and predict their classes. The proposed patent network, which contains various types of nodes that represent different features extracted from patent documents, is constructed based on the relationship metrics derived from patent metadata. The novel approach analyzes reachable nodes in the patent ontology network to calculate their relevance to query patents, after which it uses the modified k-nearest neighbor classifier to classify query patents. We evaluate the performance of the proposed approach on a test dataset of patent documents obtained from the United States Patent and Trademark Office (USPTO), and compare it with the performance of the three conventional methods. The results demonstrate that the proposed patent network-based approach outperforms the conventional approaches

Combining Clustering and MCDM Approach for Evaluating Customer Lifetime Value Ratings

Creating successful transaction actions to retain customers for future re-purchasing is extremely important in fiercely competitive environments. Moreover, different market strategies should be practiced for customers with different lifetime values and loyalty ratings. This work proposes a method, which combines clustering analysis and multiple criteria decision-making approach to evaluate customer lifetime value ratings, and construct the classification rules for individual clusters in market segmentation. An empirical case involving a hardware retailer is illustrated to show the usefulness for evaluating customer lifetime value ratings

Document Recommendation in Organizations with Personal Folders

In organizations, knowledge workers usually have their own personal folders that store and organize needed codified knowledge (textual documents) in taxonomy. In such personal folder environments, providing knowledge workers needed knowledge from other workers’ folders is important to facilitate knowledge sharing. This work adopts recommendation techniques to provide knowledge workers needed textual documents from other workers folders. Experiments are conducted to verify the performance of various methods using data collected from a research institute laboratory. The result shows that the CBF approach outperforms other methods

A Collaborative Relevance Feedback Approach to Task-driven Recommendation

Abstract Managing knowledge is an important issue for organizations to gai

A Mining-Based System Framework for Deploying Knowledge Maps of Composite E-Services

Providing e-services and composite e-services on the Internet is an important trend of e-business. Composite e-services are complex processes which consist of various e-services provided by different e-service providers. In such complex environments, the flexibility and success of e-business depend on effective knowledge supports to access related information and resources of composite e-services. This work proposes a knowledge map platform to provide an effective knowledge support for utilizing composite e-services. A mining-based system framework is proposed to construct the knowledge map. Moreover, the proposed knowledge map is integrated with recommendation capability to provide users customized decision support in utilizing composite e-services

Virtual Goods Recommendations in Virtual Worlds

Virtual worlds (VWs) are computer-simulated environments which allow users to create their own virtual character as an avatar. With the rapidly growing user volume in VWs, platform providers launch virtual goods in haste and stampede users to increase sales revenue. However, the rapidity of development incurs virtual unrelated items which will be difficult to remarket. It not only wastes virtual global companies’ intelligence resources, but also makes it difficult for users to find suitable virtual goods fit for their virtual home in daily virtual life. In the VWs, users decorate their houses, visit others’ homes, create families, host parties, and so forth. Users establish their social life circles through these activities. This research proposes a novel virtual goods recommendation method based on these social interactions. The contact strength and contact influence result from interactions with social neighbors and influence users’ buying intention. Our research highlights the importance of social interactions in virtual goods recommendation. The experiment’s data were retrieved from an online VW platform, and the results show that the proposed method, considering social interactions and social life circle, has better performance than existing recommendation methods

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words