Search (5 results, page 1 of 1)

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Abstract

The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. We want to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. For this purpose, we examine the effectiveness of a variety of features for determining the academic influence of a citation. By asking authors to identify the key references in their own work, we created a data set in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we found a model for predicting academic influence that achieves good performance on this data set using only four features. The best features, among those we evaluated, were those based on the number of times a reference is mentioned in the body of a citing paper. The performance of these features inspired us to design an influence-primed h-index (the hip-index). Unlike the conventional h-index, it weights citations by how many times a reference is mentioned. According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.

Type

a

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Abstract

Main path analysis is a famous network-based method for understanding the evolution of a scientific domain. Most existing methods have two steps, weighting citation arcs based on search path counting and exploring main paths in a greedy fashion, with the assumption that citation networks are acyclic. The only available proposal that avoids manual cycle removal is to preprint transform a cyclic network to an acyclic counterpart. Through a detailed discussion about the issues concerning this approach, especially deriving the "de-preprinted" main paths for the original network, this article proposes an alternative solution with two-fold contributions. Based on the argument that a publication cannot influence itself through a citation cycle, the SimSPC algorithm is proposed to weight citation arcs by counting simple search paths. A set of algorithms are further proposed for main path exploration and extraction directly from cyclic networks based on a novel data structure main path tree. The experiments on two cyclic citation networks demonstrate the usefulness of the alternative solution. In the meanwhile, experiments show that publications in strongly connected components may sit on the turning points of main path networks, which signifies the necessity of a systematic way of dealing with citation cycles.

Type

a

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Abstract

Faceted classification is believed to be suitable for organizing digital information resources. Based on a faceted classification model suggested for Web resources (Zins, 2002), the current study analyzed popular Web directories from different Asian countries/areas and examined cultural differences reflected in their classification systems. Three popular Web directories from four countries/regions (China, Hong Kong, Korea, and Thailand) were selected and their classifications were analyzed and compared: a local Yahoo and two home-grown Web directories from each country/region. Based on the findings, the study suggests a model that might be more suitable to Asian culture.

Language

a

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Abstract

This paper focuses on exploring the usage patterns and regularities of co-employment of various popular tags and their relationships with the activeness of users and the interest level of resources in social tagging. A hypernetwork for social tagging is constructed in which a tagging action is expressed as a hyperedge and the user, resource, and tag are expressed as nodes. Quantitative measures for the constructed hypernetwork are defined, including the hyperdegree and its distribution, the excess average hyperdegree, and the hyperdegree conditional probability distribution. Using the data set from Delicious, an empirical study was conducted. The empirical results show that multiple individual tags and one or very few popular tags are generally employed together in one tagging action, and the usage patterns and regularities of tags with varying popularity are correlated to both user activity and resource interest. The empirical results are further discussed and explained from the perspectives of tag functions and motivations. Finally, suggestions regarding the usage of various popular tags for both tagging users and service providers of social tagging are given.

Type

a

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Abstract

As an increasing number of open government data (OGD) portals are created, an evaluation method is needed to assess these portals. In this study, we drew from the existing principles and evaluation methods to develop a User Interaction Framework, with concrete criteria in five dimensions: Access, Trust, Understand, Engage-integrate, and Participate. The framework was then used to evaluate the current OGD sites created and maintained by 34 U.S. municipal government agencies. The results show that, overall, portals perform well in terms of providing access, but not so well in helping users understand and engage with data. These findings indicate room for improvement in multiple areas and suggest potential roles for information professionals as data mediators. The study also reveals that portals using the Socrata platform performed better, regarding user access, trust, engagement, and participation. However, the variability among portals indicates that some portals should improve their platforms to achieve greater user engagement and participation. In addition, city governments need to develop clear plans about what data should be available and how to make them available to their public.

Type

a