Monday, 8:00am

 

Random Walk on Co-word Network: Ranking Terms Using Structural Features

Wanying Chiu¹, Kun Lu²
¹Wuhan University, China, People’s Republic of; ²The University of Oklahoma, U.S.A

Summary

This study proposes a weighted random walk method on co-word networks to identify important themes of a field using structural features of the networks. The goal is to test whether the weighted random walk method can be used to produce meaningful results on co-word networks. In addi-tion, we examined the relationships among the results from the random walk method and other two common metrics for identifying important themes in a field: frequency and point centrality. Using a dataset of 17K bibliographic rec-ords for the articles in the LIS field from the Web of Sci-ence, our results indicate that all three measures are signifi-cantly correlated, while the correlation between frequency and point centrality themselves is much stronger than their correlations with the random walk method. A detailed comparison of the top terms ranked by the three metrics from the years of 2002-2006 and 2007-2012 is provided. The results show that the three measures are generally simi-lar in revealing hotspots and development of the field. However, some noticeable differences are also found. The random walk method boosted the rankings of some lower ranked terms in the other two metrics (e.g. “retrieval”, “lit-eracy” and “seek”) due to their co-occurrences with top ranked terms (e.g. “information”). The findings of this study help to understand the use of random walk method on co-word networks.


Online Search in English as a Non-native Language

Peng Chu¹, Anita Komlodi¹, Gyöngyi Rózsa²
¹University of Maryland, Baltimore County, United States of America; ²Budapest University of Technology and Economics, Hungary

Summary

Non-native English speakers (NNESs) often search in English due to the limited availability of information in their native language on the Web. Information seeking in a non-native language can present special challenges for users. Current research literature on non-native language search behavior is insufficient and even less is known about how online systems and tools may accommodate NNESs’ needs and assist their behaviors. To gain a better understanding of user behavior and the search process of NNESs, this paper presents a study of online searching in English as a foreign language (EFL) or second-language (L2). Particular attention is paid to language selection, search challenges, query formulation and reformulation, as well as user interaction with online systems and tools. Results from eight focus groups (36 participants) and 36 questionnaires indicate NNESs face a unique set of challenges that may not be present for native speakers when searching for information in English. A user interaction model is abstracted to address the iterative and spiral search process of NNESs. Implications for design of systems and tools to assist this particular user group are discussed.


Learning User-Defined, Domain-Specific Relations: A Situated Case Study and Evaluation in Plant Science

Ana Lucic, Catherine Blake
University of Illinois, United States of America

Summary

Although methods exist to identify well-defined relations, such as is_a or part_of, existing tools rarely support a user who wants to define new, domain-specific relations. We conducted a situated case study in plant science and introduce four new domain-specific relations that are of interest to domain scientists but have not been explored in information science. Results show that precision varies between relations and ranges from 0.73 to 0.91 for the manufacturer location category, 0.89 and 0.93 for the seed donor-bank relation, 0.29 and 0.67 for the seed origin location, and 0.32 and 0.77 for the field experiment location. The manufacturer location category recall varies from 0.91 to 0.94, the seed bank-donor location recall ranges between 0.93 and 1, the seed origin relation from 0.33 to 0.82 while the field experiment location from 0.67 to 0.83 depending on the classifier and using a combination of lexical and syntactic features in the background.