Information Retrieval and Machine Learning Methods for Academic Expert Finding

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.Spanish “Agencia Estatal de Investigación” under grants PID2019-106758GB-C31 and PID2020-113230RB-C22Spanish “FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades” under grant A-TIC-146-UGR20European Regional Development Fund (ERDF-FEDER

Information retrieval and machine learning methods for academic expert finding

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.Agencia Estatal de Investigación | Ref. PID2019-106758GB-C31Agencia Estatal de Investigación | Ref. PID2020-113230RB-C22FEDER/Junta de Andalucía | Ref. A-TIC-146-UGR2

Entity finding in a document collection using adaptive window sizes

Traditional search engines work by returning a list of documents in response to queries. However, such engines are often inadequate when the information need of the user involves entities. This issue has led to the development of entity-search, which unlike normal web search does not aim at returning documents but names of people, products, organisations, etc. Some of the most successful methods for identifying relevant entities were built around the idea of a proximity search. In this thesis, we present an adaptive, well-founded, general-purpose entity finding model. In contrast to the work of other researchers, where the size of the targeted part of the document (i.e., the window size) is fixed across the collection, our method uses a number of document features to calculate an adaptive window size for each document in the collection. We construct a new entity finding test collection called the ESSEX test collection for use in evaluating our method. This collection represents a university setting as the data was collected from the publicly accessible webpages of the University of Essex. We test our method on five different datasets including the W3C Dataset, CERC Dataset, UvT/TU Datasets, ESSEX dataset and the ClueWeb09 entity finding collection. Our method provides a considerable improvement over various baseline models on all of these datasets. We also find that the document features considered for the calculation of the window size have differing impacts on the performance of the search. These impacts depend on the structure of the documents and the document language. As users may have a variety of search requirements, we show that our method is adaptable to different applications, environments, types of named entities and document collections

A user-oriented model for expert finding

Expert finding addresses the problem of retrieving a ranked list of people who are knowledgeable on a given topic. Several models have been proposed to solve this task, but so far these have focused solely on returning the most knowledgeable people as experts on a particular topic. In this paper we argue that in a real-world organizational setting the notion of the “best expert” also depends on the individual user and her needs. We propose a user-oriented approach that balances two factors that influence the user’s choice: time to contact an expert, and the knowledge value gained after. We use the distance between the user and an expert in a social network to estimate contact time, and consider various social graphs, based on organizational hierarchy, geographical location, and collaboration, as well as the combination of these. Using a realistic test set, created from interactions of employees with a university-wide expert search engine, we demonstrate substantial improvements over a state-of-the-art baseline on all retrieval measures