Concept Based Author Recommender System for CiteSeer

The information explosion in today's electronic world has created the need for information filtering techniques that help users filter out extraneous content to identify the right information they need to make important decisions. Recommender systems are one approach to this problem, based on presenting potential items of interest to a user rather than requiring the user to go looking for them. In this paper we propose a recommender system that recommends research papers of potential interest to the author from the CiteSeer database. For each author participating in the study, we create a user profile based on their previously published papers. Based on similarities between the user profile and profiles for documents in the collection, additional papers are recommended to the author. We introduce a novel way of representing the user profiles as tree of concepts and an algorithm for computing the similarity between the user profiles and document profiles using a tree-edit distance measure. Experiments with a group of volunteers show that our tree based algorithm provides better recommendations than a traditional vector-space model based technique

Write While You Search: Ambient Searching of a Digital Library in the Context of Writing

We consider ideas for a tighter integration of searching a digital library while writing a paper. A prototype system based on web services is described which allows us to explore the design space of ambient search tools to support and inspire the writing process.published or submitted for publicationis peer reviewe

DC Proposal: Evaluating trustworthiness of web content using semantic web technologies

Trust plays an important part in people's decision processes for using information. This is especially true on the Web, which has less quality control for publishing information. Untrustworthy data may lead users to make wrong decisions or result in the misunderstanding of concepts. Therefore, it is important for users to have a mechanism for assessing the trustworthiness of the information they consume. Prior research focuses on policy-based and reputation-based trust. It does not take the information itself into account. In this PhD research, we focus on evaluating the trustworthiness of Web content based on available and inferred metadata that can be obtained using Semantic Web technologies. This paper discusses the vision of our PhD work and presents an approach to solve that problem

Citation recommendation: approaches and datasets

Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction to automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles