Applying Science Models for Search

The paper proposes three different kinds of science models as value-added services that are integrated in the retrieval process to enhance retrieval quality. The paper discusses the approaches Search Term Recommendation, Bradfordizing and Author Centrality on a general level and addresses implementation issues of the models within a real-life retrieval environment.Comment: 14 pages, 3 figures, ISI 201

Science Models as Value-Added Services for Scholarly Information Systems

The paper introduces scholarly Information Retrieval (IR) as a further dimension that should be considered in the science modeling debate. The IR use case is seen as a validation model of the adequacy of science models in representing and predicting structure and dynamics in science. Particular conceptualizations of scholarly activity and structures in science are used as value-added search services to improve retrieval quality: a co-word model depicting the cognitive structure of a field (used for query expansion), the Bradford law of information concentration, and a model of co-authorship networks (both used for re-ranking search results). An evaluation of the retrieval quality when science model driven services are used turned out that the models proposed actually provide beneficial effects to retrieval quality. From an IR perspective, the models studied are therefore verified as expressive conceptualizations of central phenomena in science. Thus, it could be shown that the IR perspective can significantly contribute to a better understanding of scholarly structures and activities.Comment: 26 pages, to appear in Scientometric

Fairness-Aware Graph Neural Networks: A Survey

Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. Previous work on fair GNN models and techniques are discussed in terms of whether they focus on improving fairness during a preprocessing step, during training, or in a post-processing phase. Furthermore, we discuss how such techniques can be used together whenever appropriate, and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed

Unraveling the Relationship between Co-Authorship and Research Interest

Co-authorship in scientific research is increasing in the past decades. There are lots of researches focusing on the pattern of co-authorship by using social network analysis. However, most of them merely concentrated on the properties of graphs or networks rather than take the contribution of authors to publications and the semantic information of publications into consideration. In this paper, we employ a contribution index to weight word vectors generated from publications so as to represent authors’ research interest, and try to explore the relationship between research interest and co-authorship. Result of curve estimation indicates that research interest couldn’t be employed to predict co-authorship. Therefore, graph-based researcher recommendation needs further examination

A knowledge graph embeddings based approach for author name disambiguation using literals

Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available in the form of Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: (1) multimodal KGEs, (2) a blocking procedure, and finally, (3) hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8–14% in terms of F1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github (https://github.com/sntcristian/and-kge) and Zenodo (https://doi.org/10.5281/zenodo.6309855) respectively

A knowledge graph embeddings based approach for author name disambiguation using literals

Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available in the form of Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: (1) multimodal KGEs, (2) a blocking procedure, and finally, (3) hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8–14% in terms of F1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github (https://github.com/sntcristian/and-kge) and Zenodo (https://doi.org/10.5281/zenodo.6309855) respectively

Predicting the dynamics of scientific activities: A diffusion-based network analytic methodology

Copyright © 2018 by Association for Information Science and Technology With the rapid explosion of information and the dramatic development of bibliometric techniques in the past decades, it becomes a challenge to comprehensively, extensively, and efficiently understand science maps. Aim-ing to explore in-depth insights from science maps and predict the dynamics of scientific activities, this paper, based on the co-occurrence statistics of terms derived from scientific documents, proposes a diffusion-based network analytic methodology to conduct the prediction study from two aspects: the research interest of scien-tific researchers and the evolutionary directions of scientific topics. A case study on academic articles down-loaded from three leading journals in the field of bibliometrics demonstrates the feasibility of the methodology. The future directions of bibliometrics are identified, such as the application of information technologies to tradi-tional bibliometric data, the interactions between bibliometrics and science, technology, and innovation policy issues, and individual-level bibliometrics. The results also provide recommendations as potential research inter-ests for a set of experts. The proposed method could be a toolkit to conduct forecasting studies for a given technological area or a given discipline, and a recommender system to assist academic researchers in identify-ing potential research interests and extended areas

Interdisciplinary Collaborative Research for Professional Academic Development in Higher Education

Although faculties are more diverse, decentralized, and increasingly isolated in technology-supported modern universities, effective technology use can also foster faculty professional academic development and collegiality. This scoping literature review applied Cooper’s systemic review model and a categorical content analysis technique targeting decentralized collaborative research teams in higher education. Findings indicate technology supports formal and informal university and nonuniversity networks, as well as various collaborative research structures; all contributing to professional academic development. Shared attributes of successful collaborative online teams include a sense of social presence, accountability, institutional and team leadership. Collaborative teams are integral to research and allow more faculty members to contribute and benefit from professional academic development through scholarship. Collaborative team research should be investigated further to understand and promote cross-discipline and cultural collaboration potential for research and professional academic development possibilities with special attention given to opportunities for women, online, and adjunct facult