The voting model for people search

The thesis investigates how persons in an enterprise organisation can be ranked in response to a query, so that those persons with relevant expertise to the query topic are ranked first. The expertise areas of the persons are represented by documentary evidence of expertise, known as candidate profiles. The statement of this research work is that the expert search task in an enterprise setting can be successfully and effectively modelled using a voting paradigm. In the so-called Voting Model, when a document is retrieved for a query, this document represents a vote for every expert associated with the document to have relevant expertise to the query topic. This voting paradigm is manifested by the proposition of various voting techniques that aggregate the votes from documents to candidate experts. Moreover, the research work demonstrates that these voting techniques can be modelled in terms of a Bayesian belief network, providing probabilistic semantics for the proposed voting paradigm. The proposed voting techniques are thoroughly evaluated on three standard expert search test collections, deriving conclusions concerning each component of the Voting Model, namely the method used to identify the documents that represent each candidate's expertise areas, the weighting models that are used to rank the documents, and the voting techniques which are used to convert the ranking of documents into the ranking of experts. Effective settings are identified and insights about the behaviour of each voting technique are derived. Moreover, the practical aspects of deploying an expert search engine such as its efficiency and how it should be trained are also discussed. This thesis includes an investigation of the relationship between the quality of the underlying ranking of documents and the resulting effectiveness of the voting techniques. The thesis shows that various effective document retrieval approaches have a positive impact on the performance of the voting techniques. Interestingly, it also shows that a `perfect' ranking of documents does not necessarily translate into an equally perfect ranking of candidates. Insights are provided into the reasons for this, which relate to the complexity of evaluating tasks based on ranking aggregates of documents. Furthermore, it is shown how query expansion can be adapted and integrated into the expert search process, such that the query expansion successfully acts on a pseudo-relevant set containing only a list of names of persons. Five ways of performing query expansion in the expert search task are proposed, which vary in the extent to which they tackle expert search-specific problems, in particular, the occurrence of topic drift within the expertise evidence for each candidate. Not all documentary evidence of expertise for a given person are equally useful, nor may there be sufficient expertise evidence for a relevant person within an enterprise. This thesis investigates various approaches to identify the high quality evidence for each person, and shows how the World Wide Web can be mined as a resource to find additional expertise evidence. This thesis also demonstrates how the proposed model can be applied to other people search tasks such as ranking blog(ger)s in the blogosphere setting, and suggesting reviewers for the submitted papers to an academic conference. The central contributions of this thesis are the introduction of the Voting Model, and the definition of a number of voting techniques within the model. The thesis draws insights from an extremely large and exhaustive set of experiments, involving many experimental parameters, and using different test collections for several people search tasks. This illustrates the effectiveness and the generality of the Voting Model at tackling various people search tasks and, indeed, the retrieval of aggregates of documents in general

Being Omnipresent To Be Almighty: The Importance of The Global Web Evidence for Organizational Expert Finding

Modern expert nding algorithms are developed under the assumption that all possible expertise evidence for a person is concentrated in a company that currently employs the person. The evidence that can be acquired outside of an enterprise is traditionally unnoticed. At the same time, the Web is full of personal information which is sufficiently detailed to judge about a person's skills and knowledge. In this work, we review various sources of expertise evidence out-side of an organization and experiment with rankings built on the data acquired from six dierent sources, accessible through APIs of two major web search engines. We show that these rankings and their combinations are often more realistic and of higher quality than rankings built on organizational data only

University of Twente at the TREC 2008 Enterprise Track: using the Global Web as an expertise evidence source

This paper describes the details of our participation in expert search task of the TREC 2007 Enterprise track.\ud This is the fourth (and the last) year of TREC 2007 Enterprise Track and the second year the University of Twente (Database group) submitted runs for the expert nding task. In the methods that were used to produce these runs, we mostly rely on the predicting potential of those expertise evidence sources that are publicly available on the Global Web, but not hosted at the website of the organization under study (CSIRO). This paper describes the follow-up studies\ud complimentary to our recent research [8] that demonstrated how taking the web factor seriously signicantly improves the performance of expert nding in the enterprise

Voting for candidates: adapting data fusion techniques for an expert search task

In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models

Using the Global Web as an Expertise Evidence Source

This paper describes the details of our participation in expert search task of the TREC 2007 Enterprise track. The presented study demonstrates the predicting potential of the expertise evidence that can be found outside of the organization. We discovered that combining the ranking built solely on the Enterprise data with the Global Web based ranking may produce significant increases in performance. However, our main goal was to explore whether this result can be further improved by using various quality measures to distinguish among web result items. While, indeed, it was beneficial to use some of these measures, especially those measuring relevance of URL strings and titles, it stayed unclear whether they are decisively important

Using relevance feedback in expert search

In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their "expertise need" by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks

Towards an alternative to Benner’s theory of expert intuition in nursing: A discussion paper

Several authors have highlighted the role of intuition in expertise. In particular, a large amount of data has been collected about intuition in expert nursing, and intuition plays an important role in the influential theory of nursing expertise developed by Benner (1984). We discuss this theory, and highlight both data that support it and data that challenge it. Based on this assessment, we propose a new theory of nursing expertise and intuition, which emphasizes how perception and conscious problem solving are intimately related. In the discussion, we propose that this theory opens new avenues of enquiry for research into nursing expertise

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