The web is an important medium for scholarly communication of various types, perhaps eventually to entirely replace some traditional mechanisms such as print journals. Yet the web analogy of citations, hyperlinks, are much more varied in use and existing citation techniques are difficult to generalise to the new medium. In this context, one new challenging object of study is the modern multi-faceted, multi-genre, partly unregulated university web site. This paper develops a methodology to analyse the patterns of interlinking between university web sites and uses it to indicate that the degree of interlinking decreases with distance, at least in the UK. This is perhaps not in itself a surprising result, despite claims of a paradigm shift from the traditional virtual college towards collaboratories, but the methodology developed can also be used to refine existing web link metrics to produce more powerful tools for comparing groups of sites. The web and scholarly communication

Measurements of the impact and history of research literature provide a useful complement to scientific digital library collections. Bibliometric indicators have been extensively studied, mostly in the context of journals. However, journal-based metrics poorly capture topical distinctions in fast-moving fields, and are increasingly problematic with the rise of open-access publishing. Recent developments in latent topic models have produced promising results for automatic sub-field discovery. The fine-grained, faceted topics produced by such models provide a clearer view of the topical divisions of a body of research literature and the interactions between those divisions. We demonstrate the usefulness of topic models in measuring impact by applying a new phrase-based topic discovery model to a collection of 300,000 Computer Science publications, collected by the Rexa automatic citation indexing system.

In this paper we introduce a new data gathering method “Web/URL Citation ” and use it and Google Scholar as a basis to compare traditional and Web-based citation patterns across multiple disciplines. For this, we built a sample of 1,650 articles from 108 Open Access (OA) journals published in 2001 in four science and four social science disciplines. We recorded the number of citations to the sample articles using several methods based upon the ISI Web of Science, Google Scholar and the Google search engine (Web/URL citations). For each discipline, we found significant correlations between ISI citations and both Google Scholar and Google Web/URL citations; with similar results when using total or average citations, and when comparing within and across (most) journals. We also investigated disciplinary differences. Google Scholar citations were more numerous than ISI citations in our four social science disciplines as well as in computer science, suggesting that Google Scholar is a more comprehensive tool for citation tracking in the social sciences and perhaps also in fast-moving fields where conference papers are highly valued and published online. The results for Web/URL citations suggested that counting a maximum of one hit per site produces a better measure for assessing the impact of OA journals or articles, because replicated web citations are very common within individual sites. The results can be considered as additional evidence that there is some commonality between traditional and Web-extracted citations. 1.

Whilst hyperlinks within Web sites may be primarily created for navigation purposes, those between sites are a rich source of information about the content and use of the Web. As a result there is a need to derive descriptive statistics about them, both to help understand the underlying communication processes and so that policy makers can gain insights into the use of online information by those located within their constituency. It is known, however, that using the individual web link source page as the basic unit of counting is problematical because of the number and size of link anomalies. The challenge addressed in this paper is that of developing methods to assess techniques for counting links from groups of large university web sites (site outlinks). Two methods to assess the reliability of link counts are developed and applied to judge which of seven advanced document models are most appropriate in each case. The most generally applicable method used is an internal consistency test based upon a highly simplified model of web linking behaviour. The data used comes from crawls of UK, Australian and New Zealand universities. The standard domain advanced web document model emerges as the logical choice for comparison purposes within this set. Some descriptive statistics concerning Top Level Domain link targets are given and it is demonstrated that the choice of model can effect the final results.

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Abstract. Product recommendation is an important part of current electronic commerce. Useful, direct and indirect relationships between pages, especially product home pages in an e-commerce site, can be extracted from web usage i.e. from historical user sessions. The proposed method introduces indirect association rules complementing typical, direct rules, which, in the web environment, usually only confirm existing hyperlinks. The direct confidence, the basic measure of direct association rules, reflects pages ’ co-occurrence in common user sessions, while the indirect confidence exploits an additional, transitive page and relationships existing between, not within sessions. The complex confidence, combining both direct and indirect relationships, is engaged in the personalized process of product recommendation in ecommerce. Carried out experiments have confirmed that indirect association rules can deliver the useful knowledge for recommender systems. 1

Purpose – The purpose of this paper is to develop a ranking of knowledge management and intellectual capital academic journals. Design/methodology/approach – A revealed preference, also referred to as citation impact, method was utilized. Citation data were obtained from Google Scholar by using Harzing’s Publish or Perish tool. The h-index and the g-index were employed to develop a ranking list. The revealed preference method was compared to the stated preference approach, also referred to as an expert survey. A comprehensive journal ranking based on the combination of both approaches is presented. Findings – Manual re-calculation of the indices reported by Publish or Perish had no impact on the ranking list. The revealed preference and stated preference methods correlated very strongly (0.8 on average). According to the final aggregate journal list that combined stated and revealed preference

Google’s PageRank is an influential algorithm that uses a model of Web use that is dominated by its link structure in order to rank pages by their estimated value to the Web community. This paper reports on the outcome of applying the algorithm to the Web sites of three national university systems in order to test whether it is capable of identifying the most important Web pages. The results are also compared to simple inlink counts. It was discovered that the highest inlinked pages do not always have the highest PageRank, indicating that the two metrics are genuinely different, even for the top pages. More significantly, however, internal links dominated external links for the high ranks in either method and superficial reasons accounted for high scores in both cases. It is concluded that PageRank is not useful for identifying the top pages in a site and that it must be combined with a powerful text matching techniques in order to get the quality of information retrieval results provided by Google.

Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBA 1)thattackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers.

When faculty members are evaluated, they are judged in part by the impact and quality of their scholarly publications. While all academic institutions look to publication counts and venues as well as the subjective opinions of peers, many hiring, tenure, and promotion committees also rely on citation analysis to obtain a more objective assessment of an author’s work. Consequently, faculty members try to identify as many citations to their published works as possible to provide a comprehensive assessment of their publication impact on the scholarly and professional communities. The Institute for Scientific Information’s (ISI) citation databases, which are widely used as a starting point if not the only source for locating citations, have several limitations that may leave gaps in the coverage of citations to an author’s work. This paper presents a case study comparing citations found in Scopus and Google Scholar with those found in Web of Science (the portal used to search the three ISI citation databases) for items published by two Library and Information Science full-time faculty members. In addition, the paper presents a brief overview of a prototype system called CiteSearch, which analyzes combined data from multiple citation databases to produce citation-based quality evaluation measures.