Lu An

Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes

Novelty and salience of the research topics are vital for the competitiveness of research institutions and the development of science and technology. In this study, two novel weighting methods were proposed to differentiate the emergence and salience of research topics. A methodology was constructed to measure and visualize the contributions of research institutions to emerging themes and salient ones. The methods were illustrated with the data of ninety Chinese and American Library and Information Science research institutions collected from the Engineering Compendex and China National Knowledge Infrastructure databases between 2001 and 2012. The contributions of the investigated research institutions to the emerging themes and salient ones were calculated and visualized with the Treemap technique. The institutions were further ranked by their contributions and categorized into four types. The findings can help research institutions evaluate novelty and salience of their research topics, discover research fronts and hotspots and promote their research development.

Visual topical analysis of Chinese and American Library and Information Science research institutions

Research institutions play an important role in scientific research and technical innovation. The topical analysis of research institutions in different countries can facilitate mutual learning and promote potential collaboration. In this study, we illustrate how an unsupervised artificial neural network technique Self-Organizing Map (SOM) can be used to visually analyze the research fields of research institutions. A novel SOM display named Compound Component Plane (CCP) was presented and applied to determine the institutions which made significant contributions to the salient research fields. Eighty-seven Chinese and American LIS institutions and the technical LIS fields were taken as examples. Potential international and domestic collaborators were identified based upon their research similarities. An approach of dividing research institutions into clusters was proposed based on their geometric distances in the SOM display, the U-matrix values and the most salient research topics they involved. The concepts of swarm institutions, pivots and landmarks were also defined and their instances were identified.

Using SOM to Mine Product Features from Free-Text Customer Reviews

This study examines how the Self-Organizing Map (SOM) technique can be used to identify product features from free-text customer reviews. A novel SOM display named “Attribute Accumulative Matrix” is presented. To verify the validity of the proposed approach, 22157 restaurant reviews are collected and product features of catering industry are identified. The experiment results show that this approach can effectively identify the product features.

Similarity-based link prediction in social networks: A path and node combined approach:

With the rapid development of the Internet, the computational analysis of social networks has grown to be a salient issue. Various research analyses social network topics, and a considerable amount of attention has been devoted to the issue of link prediction. Link prediction aims to predict the interactions that might occur between two entities in the network. To this aim, this study proposed a novel path and node combined approach and constructed a methodology for measuring node similarities. The method was illustrated with five real datasets obtained from different types of social networks. An extensive comparison of the proposed method against existing link prediction algorithms was performed to demonstrate that the path and node combined approach achieved much higher mean average precision (MAP) and area under the curve (AUC) values than those that only consider common nodes (e.g. Common Neighbours and Adamic/Adar) or paths (e.g. Random Walk with Restart and FriendLink). The results imply that two nodes are more likely to establish a link if they have more common neighbours of lower degrees. The weight of the path connecting two nodes is inversely proportional to the product of degrees of nodes on the pathway. The combination of node and topological features can substantially improve the performance of similarity-based link prediction, compared with node-dependent and path-dependent approaches. The experiments also demonstrate that the path-dependent approaches outperform the node-dependent appraoches. This indicates that topological features of networks may contribute more to improving performance than node features.

Visual Topical Analysis of Museum Collections

Museums are highly specialized cultural institutions. Obstacles exist between the knowledge and terminology of the museum professionals and that of the general public. Topical analysis of museum collections can reveal topical similarities and differences among museums and facilitate museum tours with recommended professional guides. In this study, 7177 French artworks collected by 90 art museums worldwide were investigated. The Self-Organizing Map SOM technique, an unsupervised artificial neural network method, was applied to visually analyze similarities and differences among the museums. The Treemap technique was also employed on a large dataset to reveal the distribution of the specific themes among the investigated museums. Finally, a comprehensive museum tour recommendation mechanism is established for tourists.

Mining competitive technical intelligence of high-tech products with self-organizing map

This study examines how the Self-organizing Map (SOM) technique can be used to identify key competitors and determine important technical attributes of high-tech products. An enhanced U-matrix was presented and applied to the SOM display. The cell phone industry was selected as the example and the advantages and disadvantages of competitors were explored. The leading competitors were defined in terms of some important technical attributes.