Search (12 results, page 1 of 1)

Zhang, J.: ¬A representational analysis of relational information displays (1996) 0.03
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Abstract

Analyses graphic and tabular displays under a common, unified form - relational information displays (RIDs) which are displays that represent relations between dimensions. A representational taxonomy is developed that classifies all RIDs and serves as a framework for systematic studies of RIDs. Develops a taxonomy of RIDs which can classifiy the majority of dimension based display tasks and analyzes the relation between representations of displays and structures of tasks in terms of a mapping principle
Li, D.; Luo, Z.; Ding, Y.; Tang, J.; Sun, G.G.-Z.; Dai, X.; Du, J.; Zhang, J.; Kong, S.: User-level microblogging recommendation incorporating social influence (2017) 0.01
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Abstract

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.
Wolfram, D.; Zhang, J.: ¬The influence of indexing practices and weighting algorithms on document spaces (2008) 0.01
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Abstract

Index modeling and computer simulation techniques are used to examine the influence of indexing frequency distributions, indexing exhaustivity distributions, and three weighting methods on hypothetical document spaces in a vector-based information retrieval (IR) system. The way documents are indexed plays an important role in retrieval. The authors demonstrate the influence of different indexing characteristics on document space density (DSD) changes and document space discriminative capacity for IR. Document environments that contain a relatively higher percentage of infrequently occurring terms provide lower density outcomes than do environments where a higher percentage of frequently occurring terms exists. Different indexing exhaustivity levels, however, have little influence on the document space densities. A weighting algorithm that favors higher weights for infrequently occurring terms results in the lowest overall document space densities, which allows documents to be more readily differentiated from one another. This in turn can positively influence IR. The authors also discuss the influence on outcomes using two methods of normalization of term weights (i.e., means and ranges) for the different weighting methods.
Geng, Q.; Townley, C.; Huang, K.; Zhang, J.: Comparative knowledge management : a pilot study of Chinese and American universities (2005) 0.00
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Abstract

Comparative study of knowledge management (KM) promises to lead to more effective knowledge use in all cultural environments. This pilot study compares KM priorities, needs, tools, and administrative structure components in large Chinese and American universities. General KM theory and literature related to KM in higher education are analyzed to develop the four components of the study. Comparative differences in KM practice at large Chinese and American universities are analyzed for each component. A correlation matrix reveals statistically significant co-variation among all but one of the study components. Four conclusions related to comparative KM and suggestions for future research are presented.
Zhang, J.; An, L.; Tang, T.; Hong, Y.: Visual health subject directory analysis based on users' traversal activities (2009) 0.00
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Abstract

Concerns about health issues cover a wide spectrum. Consumer health information, which has become more available on the Internet, plays an extremely important role in addressing these concerns. A subject directory as an information organization and browsing mechanism is widely used in consumer health-related Websites. In this study we employed the information visualization technique Self-Organizing Map (SOM) in combination with a new U-matrix algorithm to analyze health subject clusters through a Web transaction log. An experimental study was conducted to test the proposed methods. The findings show that the clusters identified from the same cells based on path-length-1 outperformed both the clusters from the adjacent cells based on path-length-1 and the clusters from the same cells based on path-length-2 in the visual SOM display. The U-matrix method successfully distinguished the irrelevant subjects situated in the adjacent cells with different colors in the SOM display. The findings of this study lead to a better understanding of the health-related subject relationship from the users' traversal perspective.
Wolfram, D.; Zhang, J.: ¬An investigation of the influence of indexing exhaustivity and term distributions on a document space (2002) 0.00
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Abstract

Wolfram and Zhang are interested in the effect of different indexing exhaustivity, by which they mean the number of terms chosen, and of different index term distributions and different term weighting methods on the resulting document cluster organization. The Distance Angle Retrieval Environment, DARE, which provides a two dimensional display of retrieved documents was used to represent the document clusters based upon a document's distance from the searcher's main interest, and on the angle formed by the document, a point representing a minor interest, and the point representing the main interest. If the centroid and the origin of the document space are assigned as major and minor points the average distance between documents and the centroid can be measured providing an indication of cluster organization. in the form of a size normalized similarity measure. Using 500 records from NTIS and nine models created by intersecting low, observed, and high exhaustivity levels (based upon a negative binomial distribution) with shallow, observed, and steep term distributions (based upon a Zipf distribution) simulation runs were preformed using inverse document frequency, inter-document term frequency, and inverse document frequency based upon both inter and intra-document frequencies. Low exhaustivity and shallow distributions result in a more dense document space and less effective retrieval. High exhaustivity and steeper distributions result in a more diffuse space.
Zhang, J.; Wolfram, D.; Wang, P.: Analysis of query keywords of sports-related queries using visualization and clustering (2009) 0.00
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Abstract

The authors investigated 11 sports-related query keywords extracted from a public search engine query log to better understand sports-related information seeking on the Internet. After the query log contents were cleaned and query data were parsed, popular sports-related keywords were identified, along with frequently co-occurring query terms associated with the identified keywords. Relationships among each sports-related focus keyword and its related keywords were characterized and grouped using multidimensional scaling (MDS) in combination with traditional hierarchical clustering methods. The two approaches were synthesized in a visual context by highlighting the results of the hierarchical clustering analysis in the visual MDS configuration. Important events, people, subjects, merchandise, and so on related to a sport were illustrated, and relationships among the sports were analyzed. A small-scale comparative study of sports searches with and without term assistance was conducted. Searches that used search term assistance by relying on previous query term relationships outperformed the searches without the search term assistance. The findings of this study provide insights into sports information seeking behavior on the Internet. The developed method also may be applied to other query log subject areas.
Li, D.; Tang, J.; Ding, Y.; Shuai, X.; Chambers, T.; Sun, G.; Luo, Z.; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging (2015) 0.00
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Abstract

Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.
Zhang, J.; Zhai, S.; Liu, H.; Stevenson, J.A.: Social network analysis on a topic-based navigation guidance system in a public health portal (2016) 0.00
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Abstract

We investigated a topic-based navigation guidance system in the World Health Organization portal, compared the link connection network and the semantic connection network derived from the guidance system, analyzed the characteristics of the 2 networks from the perspective of the node centrality (in_closeness, out_closeness, betweenness, in_degree, and out_degree), and provided the suggestions to optimize and enhance the topic-based navigation guidance system. A mixed research method that combines the social network analysis method, clustering analysis method, and inferential analysis methods was used. The clustering analysis results of the link connection network were quite different from those of the semantic connection network. There were significant differences between the link connection network and the semantic network in terms of density and centrality. Inferential analysis results show that there were no strong correlations between the centrality of a node and its topic information characteristics. Suggestions for enhancing the navigation guidance system are discussed in detail. Future research directions, such as application of the same research method presented in this study to other similar public health portals, are also included.
Zhang, J.; Zhai, S.; Stevenson, J.A.; Xia, L.: Optimization of the subject directory in a government agriculture department web portal (2016) 0.00
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Abstract

We investigated a subject directory in the US Agriculture Department-Economic Research Service portal. Parent-child relationships, related connections among the categories, and related connections among the subcategories in the subject directory were optimized using social network analysis. The optimization results were assessed by both density analysis and edge strength analysis methods. In addition, the results were evaluated by domain experts. From this study, it is recommended that four subcategories be switched from their original four categories into two different categories as a result of the parent-child relationship optimization.?It is also recommended that 132 subcategories be moved to 40 subcategories and that eight categories be moved to two categories as a result of the related connection optimization. The findings show that optimization boosted the densities of the optimized categories, and the recommended connections of both the related categories and subcategories were stronger than the existing connections of the related categories and subcategories. This paper provides visual displays of the optimization analysis as well as suggestions to enhance the subject directory of this portal.

Zhang, J.; Zeng, M.L.: ¬A new similarity measure for subject hierarchical structures (2014) 0.00

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Date: 8. 4.2015 16:22:13

Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.00
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Abstract

From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.

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