GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes

• URL: http://arxiv.org/abs/2205.01331v1
• Date: Tue, 3 May 2022 06:35:47 GMT
• Title: GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes
• Authors: Renaud Fabre (LED), Otmane Azeroual (DZHW), Patrice Bellot (LIS), Joachim Sch\"opfel (GERIICO), Daniel Egret (PSL)
• Abstract summary: We tackle the understanding of the design of the information space of a cognitive representation of research activities. We propose a novel graph designed geometric architecture which optimize both the detection of the knowledge manifold of "cognitive communities" With a methodology for designing "Manifold Subnetworks of Cognitive Communities", GRAPHYP provides a classification of distinct search paths in a research field.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The cognitive manifold of published content is currently expanding in all areas of science. However, Scientific Knowledge Graphs (SKGs) only provide poor pictures of the adversarial directions and scientific controversies that feed the production of knowledge. In this Article, we tackle the understanding of the design of the information space of a cognitive representation of research activities, and of related bottlenecks that affect search interfaces, in the mapping of structured objects into graphs. We propose, with SKG GRAPHYP, a novel graph designed geometric architecture which optimizes both the detection of the knowledge manifold of "cognitive communities", and the representation of alternative paths to adversarial answers to a research question, for instance in the context of academic disputes. With a methodology for designing "Manifold Subnetworks of Cognitive Communities", GRAPHYP provides a classification of distinct search paths in a research field. Users are detected from the variety of their search practices and classified in "Cognitive communities" from the analysis of the search history of their logs of scientific documentation. The manifold of practices is expressed from metrics of differentiated uses by triplets of nodes shaped into symmetrical graph subnetworks, with the following three parameters: Mass, Intensity, and Variety.

Related papers

• Detecting text level intellectual influence with knowledge graph embeddings [0.0]
  We collect a corpus of open source journal articles and generate Knowledge Graph representations using the Gemini LLM. We attempt to predict the existence of citations between sampled pairs of articles using previously published methods and a novel Graph Neural Network based embedding model.
  arXiv  Detail & Related papers  (2024-10-31T15:21:27Z)
• Conversational Exploratory Search of Scholarly Publications Using Knowledge Graphs [3.3916160303055567]
  We develop a conversational search system for exploring scholarly publications using a knowledge graph. To assess the system's effectiveness, we employed various performance metrics and conducted a human evaluation with 40 participants.
  arXiv  Detail & Related papers  (2024-10-01T06:16:07Z)
• Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning [0.0]
  We have transformed a dataset comprising 1,000 scientific papers into an ontological knowledge graph. We have calculated node degrees, identified communities and connectivities, and evaluated clustering coefficients and betweenness centrality of pivotal nodes. The graph has an inherently scale-free nature, is highly connected, and can be used for graph reasoning.
  arXiv  Detail & Related papers  (2024-03-18T17:30:27Z)
• AceMap: Knowledge Discovery through Academic Graph [90.12694363549483]
  AceMap is an academic system designed for knowledge discovery through academic graph. We present advanced database construction techniques to build the comprehensive AceMap database. AceMap provides advanced analysis capabilities, including tracing the evolution of academic ideas.
  arXiv  Detail & Related papers  (2024-03-05T01:17:56Z)
• Local Feature Matching Using Deep Learning: A Survey [19.322545965903608]
  Local feature matching enjoys wide-ranging applications in the realm of computer vision, encompassing domains such as image retrieval, 3D reconstruction, and object recognition. In recent years, the introduction of deep learning models has sparked widespread exploration into local feature matching techniques. The paper also explores the practical application of local feature matching in diverse domains such as Structure from Motion, Remote Sensing Image Registration, and Medical Image Registration.
  arXiv  Detail & Related papers  (2024-01-31T04:32:41Z)
• Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning [74.6485604326913]
  We provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation. To propagate information on the knowledge graph, we propose a novel Residual Graph Convolutional Network (ResGCN) Experiments conducted on the widely used large-scale ImageNet-21K dataset and AWA2 dataset show the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-12-26T13:18:36Z)
• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources [79.48829365560788]
  Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios. HG embedding aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks.
  arXiv  Detail & Related papers  (2020-11-30T15:03:47Z)
• A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs [77.34726150561087]
  We survey the current research landscape on word, sentence and knowledge graph embedding algorithms. We provide a classification of the relevant alignment techniques and discuss benchmark datasets used in this field of research.
  arXiv  Detail & Related papers  (2020-10-26T16:08:13Z)
• Deep Learning for Community Detection: Progress, Challenges and Opportunities [79.26787486888549]
  Article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks. This article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks.
  arXiv  Detail & Related papers  (2020-05-17T11:22:11Z)
• A Survey on Knowledge Graphs: Representation, Acquisition and Applications [89.78089494738002]
  We review research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We explore several emerging topics, including meta learning, commonsense reasoning, and temporal knowledge graphs.
  arXiv  Detail & Related papers  (2020-02-02T13:17:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.