Expert Finding Systems Research Articles

Bibliometric Analysis and Systematic Review of Research on Expert Finding: A PRISMA-guided Approach

This study conducts a comprehensive exploration of expert retrieval using a dual approach of bibliometric analysis and systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) methodology. From 2000 to 2023, our investigation reveals a notable upward trajectory in expert locating study, focusing on 494 articles identified from Scopus using specific keywords related to Expert Finding (EF) and Expert Finding Systems (EFSs). Through bibliometric analysis, utilizing VOSviewer, we identify prominent co-author groups, highly-cited documents, and global participation, shedding light on the collaborative and internationally expansive nature of EF investigations. Keyword co-occurrence and text analysis reveal thematic clusters, signaling the evolving emphases in the field from foundational expert search tasks to considerations of platform interactions. Simultaneously, our systematic review, conducted on a subset of 51 articles using NVivo, explores domains seeking expert solutions, prevalent datasets, and common evaluation methods. This research not only synthesizes the current state of EF and EFS literature but also charts a course for future exploration, contributing to a deeper understanding of the field and guiding the trajectory of forthcoming research endeavors

A Model to Identify Redundancy and Relevancy in Question-Answer Systems of Digital Scholarly Platforms

Many digital scholarly platforms allow researchers to conduct scientific discussions using their question-answer systems. Such discussions are helpful for emerging as well as established researchers in numerous prospects. In order to rely on such discussions, it is essential to effectively validate these questions and answers based on content as well as context. This will help in finding ‘quality’ content, improving user experience for researchers and acquiring efficient results in realistic applications such as influence analysis, topic modeling, expert finding and recommendation systems. In this research, one of the well-known scholarly platforms, ResearchGate (RG) is focused. Questions and answers of 14,000 researchers from RG are rendered. A qualitative analysis is performed to perceive various anomalies in the rendered data. To resolve these anomalies, we propose a Word2Vec based model that identifies redundancy and relevancy in questions and answers using content as well as context analysis. The proposed model is evaluated on the rendered data and the outcome suggests that the model accurately identifies redundant questions and irrelevant answers from scholarly platforms.

Modeling Academic Research Collaborator Selection Using an Integrated Model

Expert finding systems try to alleviate the information overload problem and recommend experts who can satisfy users' needs. They support researchers to find research collaborators automatically. The main challenge of current expert finding systems is that they retrieve experts based on the content of their documents but ignore the human interaction perspective. The human interaction perspective comprises the factors that influence collaborator selection decisions in real life. This study aimed to develop a collaborator selection model for expert finding systems in research universities. This model includes human capital, social capital, and cultural capital factors that influence collaborator selection. The researchers integrated the Scientific and Technical Human Capital (STHC) model and Social Capital Theory to determine these factors. The authors conducted a survey comprising 349 researchers from Malaysian research universities to validate the research hypotheses. A partial least squares structural equation model (PLS-SEM) was employed to analyze all the survey data. The empirical results revealed that the significant factors that influence collaborator selection in the research universities context were cognitive accessibility, reliability, relevance, commitment, physical accessibility, cultural experiences, complementary skills, and research experience. Surprisingly, the results revealed that network ties, relational accessibility, and reputation were insignificant factors for collaborator selection. This study proposed a research model for collaborator selection in the research universities context and provided several recommendations for the policymakers and practitioners. The model will help to provide sufficient criteria to select academic research collaborator in universities and can be used by expert finding systems designers, researchers, collaborators, and universities.

Mining shape of expertise: A novel approach based on convolutional neural network

Expert finding addresses the task of retrieving and ranking talented people on the subject of user query. It is a practical issue in the Community Question Answering networks. Recruiters looking for knowledgeable people for their job positions are the most important clients of expert finding systems. In addition to employee expertise, the cost of hiring new staff is another significant concern for organizations. An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective. In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. The proposed model tries to match queries and users by extracting local and position-invariant features from their corresponding documents. In other words, it detects users’ shape of expertise by learning patterns from documents of users and queries simultaneously. The proposed model contains two parallel CNN’s that extract latent vectors of users and queries based on their corresponding documents and join them together in the last layer to match queries with users. Experiments on a large subset of Stack Overflow documents indicate the effectiveness of the proposed method against baselines in terms of NDCG, MRR, and ERR evaluation metrics.

Expert Finding Systems: A Systematic Review

The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.

Understanding expert finding systems: domains and techniques

Expert finding can be required for a variety of purposes: finding referees for a conference paper, recommending consultants for a software project, and identifying qualified answerers for a question in online knowledge-sharing communities, to name a few. This paper presents taxonomy of the task of expert finding that highlights the differences between finding experts, from the type of expertise indicator’s point of view. The taxonomy supports deep understanding of different sources of expertise information in the enterprise or online communities; for example, authored documents, emails, online posts, and social networks. In addition, different content and non-content features that characterize the evidence of expertise are discussed. The goal is to guide researchers who seek to conduct studies regarding the different types of expertise indicators and state-of-the-art techniques for expert finding in organizations or online communities. The paper concludes that although researchers have utilized a large number of graph and machine-learning techniques for locating expertise, there are still technical issues associated with the implementation of some of these methods. It also corroborates that combining content-based expertise indicators and social relationships has the benefit of alleviating some of the issues related to identifying and ranking answer experts. The above findings give implications for developing new techniques for expert finding that can overcome the technical issues associated with the performance of current methods.

A survey on scholarly data: From big data perspective

Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform, with specific focus on directions for future research and maps them to the different phases of the big data lifecycle.

A survey on expert finding techniques

Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into different categories based on their underlying algorithms and models. We also introduce the most widely used data collection for evaluating expert finding systems, and discuss future research directions.

Declarative specifications for the development of multi-agent systems

The designing and implementation of a multi-agent system (MAS), where autonomous agents collaborate with other agents for solving problems, constitute complex tasks that may become even harder when agents work in new interactive environments such as the Semantic Web. In order to deal with the complexities of designing and implementing a MAS, a domain-specific language (DSL) can be employed inside the MAS's development cycle. In such a manner, a MAS can be completely specified by programs written in a DSL. Such programs are declarative, expressive, and at the right abstraction level. In this way the complexity of MAS development is then partially shifted to DSL development and the task herein can be much more feasible by using a proper DSL development methodology and related tools. This paper presents and discusses our methodology for DSL development based on declarative formal specifications that are easy to compose, and its usage during MAS development. A practical case-study is also provided covering an example of a MAS's development for expert finding systems. By using denotational semantics for precisely defining the language, we show that it is possible to generate the language automatically. In addition, using attribute grammars makes it possible to have modular methodology within which evolutionary language development becomes easier.

Topic modeling for expert finding using latent Dirichlet allocation

The task of expert finding is to rank the experts in the search space given a field of expertise as an input query. In this paper, we propose a topic modeling approach for this task. The proposed model uses latent Dirichlet allocation (LDA) to induce probabilistic topics. In the first step of our algorithm, the main topics of a document collection are extracted using LDA. The extracted topics present the connection between expert candidates and user queries. In the second step, the topics are used as a bridge to find the probability of selecting each candidate for a given query. The candidates are then ranked based on these probabilities. The experimental results on the Text REtrieval Conference (TREC) Enterprise track for 2005 and 2006 show that the proposed topic‐based approach outperforms the state‐of‐the‐art profile‐ and document‐based models, which use information retrieval methods to rank experts. Moreover, we present the superiority of the proposed topic‐based approach to the improved document‐based expert finding systems, which consider additional information such as local context, candidate prior, and query expansion.This article is categorized under: Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Data Concepts Technologies > Classification Technologies > Machine Learning

ExpertRank: A topic-aware expert finding algorithm for online knowledge communities

With increasing knowledge demands and limited availability of expertise and resources within organizations, professionals often rely on external sources when seeking knowledge. Online knowledge communities are Internet based virtual communities that specialize in knowledge seeking and sharing. They provide a virtual media environment where individuals with common interests seek and share knowledge across time and space. A large online community may have millions of participants who have accrued a large knowledge repository with millions of text documents. However, due to the low information quality of user-generated content, it is very challenging to develop an effective knowledge management system for facilitating knowledge seeking and sharing in online communities. Knowledge management literature suggests that effective knowledge management should make accessible not only written knowledge but also experts who are a source of information and can perform a given organizational or social function. Existing expert finding systems evaluate one's expertise based on either the contents of authored documents or one's social status within his or her knowledge community. However, very few studies consider both indicators collectively. In addition, very few studies focus on virtual communities where information quality is often poorer than that in organizational knowledge repositories. In this study we propose a novel expert finding algorithm, ExpertRank, that evaluates expertise based on both document-based relevance and one's authority in his or her knowledge community. We modify the PageRank algorithm to evaluate one's authority so that it reduces the effect of certain biasing communication behavior in online communities. We explore three different expert ranking strategies that combine document-based relevance and authority: linear combination, cascade ranking, and multiplication scaling. We evaluate ExpertRank using a popular online knowledge community. Experiments show that the proposed algorithm achieves the best performance when both document-based relevance and authority are considered.

Developing a Biomedical Expert Finding System Using Medical Subject Headings

ObjectivesEfficient identification of subject experts or expert communities is vital for the growth of any organization. Most of the available expert finding systems are based on self-nomination, which can be biased, and are unable to rank experts. Thus, the objective of this work was to develop a robust and unbiased expert finding system which can quantitatively measure expertise.MethodsMedical Subject Headings (MeSH) is a controlled vocabulary developed by the National Library of Medicine (NLM) for indexing research publications, articles and books. Using the MeSH terms associated with peer-reviewed articles published from India and indexed in PubMed, we developed a Web-based program which can be used to identify subject experts and subjects associated with an expert.ResultsWe have extensively tested our system to identify experts from India in various subjects. The system provides a ranked list of experts where known experts rank at the top of the list. The system is general; since it uses information available with the PubMed, it can be implemented for any country.ConclusionsThe expert finding system is able to successfully identify subject experts in India. Our system is unique because it allows the quantification of subject expertise, thus enabling the ranking of experts. Our system is based on peer-reviewed information. Use of MeSH terms as subjects has standardized the subject terminology. The system matches requirements of an ideal expert finding system.

A Framework for the Construction of Ontology for ICT Experts

Systems that help to find answers to suitable experts have received the attention of many researchers. Pioneer researchers on the development of search system for experts emphasized the importance of a search system that required returning a list prioritizing the names of individuals. Among the issues raised related to locating experts are, the critical problem of maintaining up-to-date information in the expert database and the inadequacy in expert finding systems in returning search results that are expected to account for not only the list of the names of the experts, but any information related to the experts and those involved with them. In the last decade, researchers have examined the search for experts from various research perspectives such as expert tracking system and construction of expert profiles involving ontology. This paper aims to describe a new approach in designing a framework for the construction of ontology that will be used in the directory of ICT experts. The researchers propose to incorporate thesaurus in their construction of ontology on ICT experts by providing a profile of the experts including their social profile and whatever concerns that may be associated with the expert. The researchers constructed the ontology on ICT experts by extracting information from sources such as their resumes and personal web sites to obtain the index glossary of words that characterized experts to be used in the directory of experts system. In this study, the standards contained in the Performance Evaluation System of the National University of Malaysia are used to extract information on the academicians in the Faculty of Information Science and Technology. The researchers employed the index glossary, metadata of experts and integrate appropriate taxonomy, thesaurus and classification schemes; such as Association for Computing Machinery (ACM) taxonomy, web classification schemes such as Standard Industrial Classification (SIC), North American Industry Classification System (NAICS) to enrich the ontology of ICT expertise. The proposed framework aims at helping users find information on the expert they require and at the same time obtain other information related to the experts from various perspectives encompassing research, consultancy, links with research partners, other interest related to a particular field and other resources. With a semantically driven directory of ICT experts, matters related to ICT can be referred to the right experts. The framework will be validated by developing the directory of ICT experts prototype and by involving domain experts to evaluate the content of the ontology constructed. In addition, the researchers will evaluate the search results of users who will use the prototype to search for ICT experts.

SocLaKE: Social Latent Knowledge Explorator

In the recent world, we are surrounded by various social networks. In every company, every institution, and every place worldwide, people call each other, exchange emails, text messages, post in forums, co-author documents, meet at diverse events, etc. In other words, they communicate and collaborate with each other creating and maintaining mutual relationships in a complex social network. The traditional expert finding systems try to locate a relevant expert to whom the query should be sent. However, most of the experts are not willing to solve the problems for people they do not know. In our proposed novel system, a social paradigm is used to encourage experts to send their solutions. By means of recommendations, the system propagates the query not directly to the expert, but to friends and colleagues of the expert through the acquaintance chain existing in the social network. The experts are more likely to answer if the requests come from their acquaintances. The general idea, model, and simulations on the recommender system for query propagation in the social network are presented in the paper.

Recommending knowledgeable people in a work-integrated learning system

According to studies into learning at work, interpersonal help seeking is the most important strategy of how people acquire knowledge at their workplaces. Finding knowledgeable persons, however, can often be difficult for several reasons. Expert finding systems can support the process of identifying knowledgeable colleagues thus facilitating communication and collaboration within an organization. In order to provide the expert finding functionality, an underlying user model is needed that represents the characteristics of each individual user. In our article we discuss requirements for user models for the workintegrated learning (WIL) situation. Then, we present the APOSDLE People Recommender Service which is based on an underlying domain model, and on the APOSDLE User Model. We describe the APOSDLE People Recommender Service on the basis of the Intuitive Domain Model of expert finding systems, and explain how this service can support interpersonal help seeking at workplaces.

Integrating multiple windows and document features for expert finding

AbstractExpert finding is a key task in enterprise search and has recently attracted lots of attention from both research and industry communities. Given a search topic, a prominent existing approach is to apply some information retrieval (IR) system to retrieve top ranking documents, which will then be used to derive associations between experts and the search topic based on cooccurrences. However, we argue that expert finding is more sensitive to multiple levels of associations and document features that current expert finding systems insufficiently address, including (a) multiple levels of associations between experts and search topics, (b) document internal structure, and (c) document authority. We propose a novel approach that integrates the above‐mentioned three aspects as well as a query expansion technique in a two‐stage model for expert finding. A systematic evaluation is conducted on TREC collections to test the performance of our approach as well as the effects of multiple windows, document features, and query expansion. These experimental results show that query expansion can dramatically improve expert finding performance with statistical significance. For three well‐known IR models with or without query expansion, document internal structures help improve a single window‐based approach but without statistical significance, while our novel multiple window‐based approach can significantly improve the performance of a single window‐based approach both with and without document internal structures.

Expert-Finding Systems for Organizations: Problem and Domain Analysis and the DEMOIR Approach

Computer systems that augment the process of finding, in an organization or worldwide, the appropriate expert for a given problem are becoming more feasible than ever as a result of the prevalence of corporate intranets and the Internet. In this article, we investigate such systems in 2 parts. First, we explore the expert-finding problem in depth, review and analyze existing systems in this domain, and suggest a domain model that can serve as a framework for design and development decisions. Second, on the basis of our analyses of the problem and solution spaces, we bring to light the gaps that remain to be addressed. Finally, after this two-part investigation, we present our approach, called DEMOIR (dynamic expertise modeling from organizational information resources), which is a modular architecture for expert-finding systems that is based on a centralized expertise-modeling server but also incorporates decentralized components for expertise information gathering and exploitation.