We present CiteSeer: an autonomous citation indexing system which indexes academic literature in electronic format (e.g. Postscript files on the Web). CiteSeer understands how to parse citations, identify citations to the same paper in different formats, and identify the context of citations in the body of articles. CiteSeer provides most of the advantages of traditional (manually constructed) citation indexes (e.g. the ISI citation indexes), including: literature retrieval by following citation links (e.g. by providing a list of papers that cite a given paper), the evaluation and ranking of papers, authors, journals, etc. based on the number of citations, and the identification of research trends. CiteSeer has many advantages over traditional citation indexes, including the ability to create more up-to-date databases which are not limited to a preselected set of journals or restricted by journal publication delays, completely autonomous operation with a corresponding reduction in cost, and powerful interactive browsing of the literature using the context of citations. Given a particular paper of interest, CiteSeer can display the context of how the paper is cited in subsequent publications. This context may contain a brief summary of the paper, another author's response to the paper, or subsequent work which builds upon the original article. CiteSeer allows the location of papers by keyword search or by citation links. Papers related to a given paper can be located using common citation information or word vector similarity. CiteSeer will soon be available for public use.

Published research papers available on the World Wide Web (WWW or Web) are often poorly organized, often exist in non-text form (e.g. Postscript) documents, and increase in quantity daily. Significant amounts of time and effort are commonly needed to find interesting and relevant publications on the Web. We have developed a Web based information agent that assists the user in the process of performing a scientific literature search. Given a set of keywords, the agent uses Web search engines and heuristics to locate and download papers. The papers are parsed in order to extract information features such as the abstract and individually identified citations which are placed into an SQL database. The agent's Web interface can be used to find relevant papers in the database using keyword searches, or by navigating the links between papers formed by the citations. Links to both "citing " and "cited " publications can be followed. In addition to simple browsing and keyword searches, the agent can find papers which are similar to a given paper using word information and by analyzing common citations made by the papers.

Abstract. An increasing number of online information services calculate and report the citedness score of the source documents and provide a link to the group of records of the citing documents. The citedness score depends on the breadth of source coverage, and the ability of the software to identify the cited documents correctly. The citedness score may be a good indicator of the influence of the documents retrieved. Google Scholar gives the most prominence to the citedness score by using it in ranking the search results. Tests have been conducted to compare the individual and aggregate citedness scores of items in the results list of various known-item and subject searches in Web of Science (WoS) and Google Scholar (GS). This paper presents the findings of the comparison and analysis of the individual and aggregate citation scores calculated by WoS and GS for the papers published in 22 volumes of the Asian Pacific Journal of Allergy and Immunology (APJAI). The aggregate citedness score was 1,355 for the 675 papers retrieved by WoS, and 595 for 680 papers found in GS. The findings of the analysis and comparison of tests, and the reasons for the significant limitations of Google Scholar in calculating and reporting the citedness scores are presented. 1

The supra-classifier framework is an effective approach to flexible and scalable classifier knowledge reuse. This work introduces the concept of classifier knowledge reuse as a means of exploiting domain knowledge from previously created, relevant classifiers to build a more accurate classifier than can be constructed using a training set and a priori knowledge alone. ...

Introduction This proposal is submitted to the Library and Information Commission as a response to its Digital Libraries Research Call. The proposal is available online at http://openlib.org/home/krichel/CitEc.html. We are grateful to Nir Dagan for comments on an earlier draft. We welcome more feedback. The broad purpose of the proposal is to build an autonomous citation index for a large collection of scientific documents. This collection is the RePEc dataset of Economics research papers coordinated by the first applicant. In Section 2, we introduce that collection. The autonomous citation index will be prepared by the CiteSeer software, written by the second applicant, as introduced in Section 3. Section 4 details the aims of the applicants. In Section 5 deals with the management of the project. Section 6 describes the deliverables and sets out the timetable to achieve them. Section 7 concludes the application. 2RePEc RePEc is a decentralized collection of metad

Published research papers available on the World Wide Web (WWW or Web) are often poorly organized, often exist in non-text form (e.g. Postscript) documents, and increase in quantity daily. Significant amounts of time and effort are commonly needed to find interesting and relevant publications on the Web. We have developed a Web based information agent that assists the user in the process of performing a scientific literature search. Given a set of keywords, the agent uses Web search engines and heuristics to locate and download papers. The papers are parsed in order to extract information features such as the abstract and individually identified citations which are placed into an SQL database. The agent's Web interface can be used to find relevant papers in the database using keyword searches, or by navigating the links between papers formed by the citations. Links to both "citing" and "cited" publications can be followed. In addition to simple browsing and keyword searches, the agent...

Abstract. The process of publishing a journal article is often frustrating— misplacement, delay, overburdened editors and dense reviewers inhabit our worst nightmares. Since articles are the primary tool in academic discourse, improving this process is important. Here I present a market solution which highlights and rewards valuable articles, improves article placement, increases editorial efficiency, more accurately measures academic productivity, reduces citation games and reduces (while rewarding) the market power of entrenched parties. This solution relies on competitive auctions for articles and a system of residual payments to creators of prior art. While this paper concerned with economics, application to other fields is straightforward and will create positive network effects. 1. Publications Matter! Publish Us! Authors don’t want pennies for their papers, they want impact. That means readers, citations and researchers building on their work. That’s what gets them the real rewards.