Scholarly knowledge graphs through structuring scholarly communication: a review

Verma, Shilpa; Bhatia, Rajesh; Harit, Sandeep; Batish, Sanjay

doi:10.1007/s40747-022-00806-6

Cited by 13 publications

(6 citation statements)

References 104 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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“…We argue that knowledge graph structure is necessary for accurately determining trustfulness and identifying disinformation in documents. This is because the knowledge graph can model relationships between documents to bring information that cannot be obtained from the document itself (Verma et al, 2023). Several recent studies have strongly supported the integration of AI models with domain-specific knowledge graphs for better performance (Holzinger et al, 2023;Pan et al, 2023;Trajanoska et al, 2023).…”

Section: What Are Th E Salient Takeaways Of Th Is Study?mentioning

confidence: 99%

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

Ali Khan,

Kauttonen,

Aunimo

et al. 2024

JITE:Research

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Aim/Purpose: The purpose of this paper is to address the challenges posed by disinformation in an educational context. The paper aims to review existing information assessment techniques, highlight their limitations, and propose a conceptual design for a multimodal, explainable information assessment system for higher education. The ultimate goal is to provide a roadmap for researchers that meets current requirements of information assessment in education. Background: The background of this paper is rooted in the growing concern over disinformation, especially in higher education, where it can impact critical thinking and decision-making. The issue is exacerbated by the rise of AI-based analytics on social media and their use in educational settings. Existing information assessment techniques have limitations, requiring a more comprehensive AI-based approach that considers a wide range of data types and multiple dimensions of disinformation. Methodology: Our approach involves an extensive literature review of current methods for information assessment, along with their limitations. We then establish theoretical foundations and design concepts for EMIAS based on AI techniques and knowledge graph theory. Contribution: We introduce a comprehensive theoretical framework for an AI-based multimodal information assessment system specifically designed for the education sector. It not only provides a novel approach to assessing information credibility but also proposes the use of explainable AI and a three-pronged approach to information evaluation, addressing a critical gap in the current literature. This research also serves as a guide for educational institutions considering the deployment of advanced AI-based systems for information evaluation. Findings: We uncover a critical need for robust information assessment systems in higher education to tackle disinformation. We propose an AI-based EMIAS system designed to evaluate the trustworthiness and quality of content while providing explanatory justifications. We underscore the challenges of integrating this system into educational infrastructures and emphasize its potential benefits, such as improved teaching quality and fostering critical thinking. Recommendations for Practitioners: Implement the proposed EMIAS system to enhance the credibility of information in educational settings and foster critical thinking among students and teachers. Recommendation for Researchers: Explore domain-specific adaptations of EMIAS, research on user feedback mechanisms, and investigate seamless integration techniques within existing academic infrastructure. Impact on Society: This paper’s findings could strengthen academic integrity and foster a more informed society by improving the quality of information in education. Future Research: Further research should investigate the practical implementation, effectiveness, and adaptation of EMIAS across various educational contexts.

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“…This article adopts a top-down method to construct the knowledge graph. Constructing knowledge graphs using a top-down approach facilitates a comprehensive understanding of the situation to avoid over-construction and information redundancy [47][48][49][50]. First, the diversified objects of mountain highway scene and their associated relationships are analysed, based on the data of mountain highway [51][52][53], basic geographic scene, highway sensor dynamic data and domain-related textual data.…”

Section: Knowledge Graph Construction For Mountain Highway Twin Scenesmentioning

confidence: 99%

A Knowledge-Guided Fusion Visualisation Method of Digital Twin Scenes for Mountain Highways

Tang,

Zhu,

Ren

et al. 2023

IJGI

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Informatization is an important trend in the field of mountain highway management, and the digital twin is an effective way to promote mountain highway information management due to the complex and diverse terrain of mountainous areas, the high complexity of mountainous road scene modeling and low visualisation efficiency. It is challenging to construct the digital twin scenarios efficiently for mountain highways. To solve this problem, this article proposes a knowledge-guided fusion expression method for digital twin scenes of mountain highways. First, we explore the expression features and interrelationships of mountain highway scenes to establish the knowledge graph of mountain highway scenes. Second, by utilizing scene knowledge to construct spatial semantic constraint rules, we achieve efficient fusion modeling of basic geographic scenes and dynamic and static ancillary facilities, thereby reducing the complexity of scene modeling. Finally, a multi-level visualisation publishing scheme is established to improve the efficiency of scene visualisation. On this basis, a prototype system is developed, and case experimental analysis is conducted to validate the research. The results of the experiment indicate that the suggested method can accomplish the fusion modelling of mountain highway scenes through knowledge guidance and semantic constraints. Moreover, the construction time for the model fusion is less than 5.7 ms; meanwhile, the dynamic drawing efficiency of the scene is maintained above 60 FPS. Thus, the construction of twinned scenes can be achieved quickly and efficiently, the effect of replicating reality with virtuality is accomplished, and the informatisation management capacity of mountain highways is enhanced.

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Scholarly knowledge graphs through structuring scholarly communication: a review

Cited by 13 publications

References 104 publications

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

A System to Ensure Information Trustworthiness in Artificial Intelligence Enhanced Higher Education

A Knowledge-Guided Fusion Visualisation Method of Digital Twin Scenes for Mountain Highways

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