New trends in scientific knowledge graphs and research impact assessment

Manghi, Paolo; Mannocci, Andrea; Osborne, Francesco; Sacharidis, Dimitris; Salatino, Angelo A.; Vergoulis, Thanasis

doi:10.1162/qss_e_00160

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…The improved Efficiency of DataChat, which replaces multiple search dropdowns with a single natural language input, makes the dataset search process accessible for researchers, educators, and students, regardless of their technical expertise and time constraints. DataChat increases data Visibility through graph visualization, which also highlights different attributes of nodes and the schema of ICPSR‐SKG, enabling stakeholders to evaluate research impacts, identify gaps in knowledge, uncover potential collaborators, and gain insights into emerging research trends (Verma et al 2023; Manghi et al 2021). Lastly, DataChat visualization's Interactivity promotes user engagement by allowing users to emphasize specific nodes according to their needs and goals, creating a personalized experience as stakeholders explore research datasets.…”

Section: Discussionmentioning

confidence: 99%

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

Fan,

Lafia,

et al. 2023

Proceedings of the Association for Information Science and Tech

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Data users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter‐university Consortium for Political and Social Research (ICPSR), offer standardized metadata and search tools to support data search. Metadata standards emphasize the machine‐readability of data and its documentation. There are opportunities to enhance dataset search by improving users' ability to learn about, and make sense of, information about data. Prior research has shown that context and expertise are two main barriers users face in effectively searching for, evaluating, and deciding whether to reuse data. In this paper, we propose a novel chatbot‐based search system, DataChat, that leverages a graph database and a large language model to provide novel ways for users to interact with and search for research data. DataChat complements data archives' and institutional repositories' ongoing efforts to curate, preserve, and share research data for reuse by making it easier for users to explore and learn about available research data.

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Section: Discussionmentioning

confidence: 99%

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

Fan,

Lafia,

et al. 2023

Proceedings of the Association for Information Science and Tech

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“…Recent studies have demonstrated the application of knowledge graphs in identifying emerging trends, uncovering hidden connections, and predicting future research directions within scientific domains [4]- [11]. The integration of semantic analysis with graph-based structures allows for a more nuanced understanding of the thematic evolution in fields such as deep learning and cybersecurity [12], [13].…”

Section: A Knowledge Graphsmentioning

confidence: 99%

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Barron,

Eren,

Bhattarai

et al. 2024

2024 12th International Symposium on Digital Forensics and Security (ISDFS)

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Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG.

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“…On the other side, a recent comprehensive analysis of trends in SKG outcomes [1] observes that SKG efficiency is primarily significant in global encyclopedic databases of scientific results, with the ability to enrich "classic metadata" about publications. However, knowledge graph "incompleteness" causes serious obstacles to the expressive power of graphs as a whole [13] (we refer to the definition of graphs given in the article quoted above, where graphs «accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between these entities».…”

Section: Research Questionmentioning

confidence: 99%

“…The variety and diversity of published content are currently expanding in all areas of science, with the simultaneous growth of interdisciplinarity. Powerful new tools and new technical infrastructures such as scientific knowledge graphs (SKG) have been developed [1], to help users navigate the flood of scientific information. However, the search experience requires more precision because retrieval systems do not benefit from a rich panel of content descriptors, in the context of model-based information retrieval allowing personalized answers to queries.…”

Section: Introduction: Representing the Manifold Of Cognitive Communi...mentioning

confidence: 99%

“…Recently deployed tools, in principle, constitute solutions for the knowledge representation problem that we are exploring in this study; these are scientific knowledge graphs (SKGs) which already offer many innovative services in support of science, and which also address the challenges of evaluating the impact of research [1].…”

Section: Introduction: Representing the Manifold Of Cognitive Communi...mentioning

confidence: 99%

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Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

et al. 2022

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The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes.

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New trends in scientific knowledge graphs and research impact assessment

Cited by 10 publications

References 26 publications

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

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