Learning to Rank Academic Experts in the DBLP Dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts, and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.Comment: Expert Systems, 2013. arXiv admin note: text overlap with arXiv:1302.041

TopicViz: Semantic Navigation of Document Collections

When people explore and manage information, they think in terms of topics and themes. However, the software that supports information exploration sees text at only the surface level. In this paper we show how topic modeling -- a technique for identifying latent themes across large collections of documents -- can support semantic exploration. We present TopicViz, an interactive environment for information exploration. TopicViz combines traditional search and citation-graph functionality with a range of novel interactive visualizations, centered around a force-directed layout that links documents to the latent themes discovered by the topic model. We describe several use scenarios in which TopicViz supports rapid sensemaking on large document collections

Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster-Shafer theory of evidence and Shannon's entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework

Exploring scholarly data with Rexplore.

Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves

Sharpening the Search Saw: Lessons from Expert Searchers

Many students consider themselves to be proficient searchers and yet are disappointed or frustrated when faced with the task of locating relevant scholarly articles for a literature review. This bleak experience is common among higher education students, even for those in library and information science programs who have heightened appreciation for information resources and yet may settle for “good enough Googling” (Plosker, 2004, p. 34). This is in large part due to reliance on web search engines that have evolved relevance ranking into a vastly intelligent business, one in which we are both its customers and product (Vaidhyanathan, 2011). Google’s Hummingbird nest of search algorithms (Sullivan, 2013) provides quick and targeted hits, yet it can trigger blinders-on trust in first-page results. Concern for student search practices ranges from this permissive trust all the way to lost ability to recall facts and formulate questions (Abilock, 2015), lack of confidence in one’s own knowledge (Carr, 2010), and increased dependence on single search boxes that encourage stream-of-consciousness user input (Tucker, 2013); indeed, students may be high in tech savvy but lacking the critical thinking skills needed for information research tasks (Katz, 2007). Students have come to rely on web search engine intelligence—and it is inarguably colossal—to such an extent that they may fail to formulate a question before charging forward to search for its answer. “Google is known as a search engine, yet there is barely any searching involved anymore. The gap between a question crystallizing in your mind and an answer appearing at the top of your screen is shrinking all the time. As a consequence, our ability to ask questions is atrophying” (Leslie, 2015, para. 4). Highly accomplished students often lament their lack of skills for higher-level searching that calls for formulating pointed questions when struggling to develop a solid literature review. In addition, many are unaware that search results are filtered based on previous searches, location, and other factors extracted from personal search patterns by the search engine. Two students working side by side and entering the same search terms may receive quite different results on Google, yet the extent to which this ‘filter bubble’ (Pariser, 2011) is personalizing their search results is difficult to assess and to overcome. Just as important, it can be impossible to know what a search might be missing: how to know what’s not there? This portrayal of the information landscape may appear gloomy but, in fact, it could not be a more inspiring environment in which to do research, to find connections in ideas, and to benefit from and generate new ideas. A few lessons from expert searchers, focused on critical concepts and search practices, can sharpen a student’s search saw and move the proficient student-researcher, desiring more relevant and comprehensive search results, into a trajectory toward search expertise. For the lessons involved in this journey, the focus is on two areas: first, the critical concepts— called threshold concepts (Meyer & Land, 2003)— found to be necessary for developing search expertise (Tucker et al., 2014); and, second, four strategic areas within search that can have significant and immediate impact on improving search results for research literature. The latter are grounded in the threshold concepts and positioned for application to literature reviews for graduate student studies

Learning Reputation in an Authorship Network

The problem of searching for experts in a given academic field is hugely important in both industry and academia. We study exactly this issue with respect to a database of authors and their publications. The idea is to use Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform topic modelling in order to find authors who have worked in a query field. We then construct a coauthorship graph and motivate the use of influence maximisation and a variety of graph centrality measures to obtain a ranked list of experts. The ranked lists are further improved using a Markov Chain-based rank aggregation approach. The complete method is readily scalable to large datasets. To demonstrate the efficacy of the approach we report on an extensive set of computational simulations using the Arnetminer dataset. An improvement in mean average precision is demonstrated over the baseline case of simply using the order of authors found by the topic models