Relevant bibliographies by topics / Knowledge graph refinement

Academic literature on the topic 'Knowledge graph refinement'

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Published: 25 May 2024

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Journal articles on the topic "Knowledge graph refinement"

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Zhang, Dehai, Menglong Cui, Yun Yang, Po Yang, Cheng Xie, Di Liu, Beibei Yu, and Zhibo Chen. "Knowledge Graph-Based Image Classification Refinement." IEEE Access 7 (2019): 57678–90. http://dx.doi.org/10.1109/access.2019.2912627.

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Hsueh, Huei-Chia, Shuo-Chen Chien, Chih-Wei Huang, Hsuan-Chia Yang, Usman Iqbal, Li-Fong Lin, and Wen-Shan Jian. "A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications." PLOS ONE 19, no. 1 (January 31, 2024): e0296939. http://dx.doi.org/10.1371/journal.pone.0296939.

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Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.
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Kayali, Moe, and Dan Suciu. "Quasi-Stable Coloring for Graph Compression." Proceedings of the VLDB Endowment 16, no. 4 (December 2022): 803–15. http://dx.doi.org/10.14778/3574245.3574264.

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We propose quasi-stable coloring , an approximate version of stable coloring. Stable coloring, also called color refinement, is a well-studied technique in graph theory for classifying vertices, which can be used to build compact, lossless representations of graphs. However, its usefulness is limited due to its reliance on strict symmetries. Real data compresses very poorly using color refinement. We propose the first, to our knowledge, approximate color refinement scheme, which we call quasi-stable coloring. By using approximation, we alleviate the need for strict symmetry, and allow for a tradeoff between the degree of compression and the accuracy of the representation. We study three applications: Linear Programming, Max-Flow, and Betweenness Centrality, and provide theoretical evidence in each case that a quasi-stable coloring can lead to good approximations on the reduced graph. Next, we consider how to compute a maximal quasi-stable coloring: we prove that, in general, this problem is NP-hard, and propose a simple, yet effective algorithm based on heuristics. Finally, we evaluate experimentally the quasi-stable coloring technique on several real graphs and applications, comparing with prior approximation techniques.
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Paulheim, Heiko. "Knowledge graph refinement: A survey of approaches and evaluation methods." Semantic Web 8, no. 3 (December 6, 2016): 489–508. http://dx.doi.org/10.3233/sw-160218.

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Zhang, Yichong, and Yongtao Hao. "Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models." Electronics 13, no. 7 (April 7, 2024): 1395. http://dx.doi.org/10.3390/electronics13071395.

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This study explores the use of large language models in constructing a knowledge graph for Traditional Chinese Medicine (TCM) to improve the representation, storage, and application of TCM knowledge. The knowledge graph, based on a graph structure, effectively organizes entities, attributes, and relationships within the TCM domain. By leveraging large language models, we collected and embedded substantial TCM–related data, generating precise representations transformed into a knowledge graph format. Experimental evaluations confirmed the accuracy and effectiveness of the constructed graph, extracting various entities and their relationships, providing a solid foundation for TCM learning, research, and application. The knowledge graph has significant potential in TCM, aiding in teaching, disease diagnosis, treatment decisions, and contributing to TCM modernization. In conclusion, this paper utilizes large language models to construct a knowledge graph for TCM, offering a vital foundation for knowledge representation and application in the field, with potential for future expansion and refinement.
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Aldughayfiq, Bader, Farzeen Ashfaq, N. Z. Jhanjhi, and Mamoona Humayun. "Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB." Healthcare 11, no. 12 (June 15, 2023): 1762. http://dx.doi.org/10.3390/healthcare11121762.

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Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.
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Dong, Qian, Shuzi Niu, Tao Yuan, and Yucheng Li. "Disentangled Graph Recurrent Network for Document Ranking." Data Science and Engineering 7, no. 1 (February 15, 2022): 30–43. http://dx.doi.org/10.1007/s41019-022-00179-3.

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AbstractBERT-based ranking models are emerging for its superior natural language understanding ability. All word relations and representations in the concatenation of query and document are modeled in the self-attention matrix as latent knowledge. However, some latent knowledge has none or negative effect on the relevance prediction between query and document. We model the observable and unobservable confounding factors in a causal graph and perform do-query to predict the relevance label given an intervention over this graph. For the observed factors, we block the back door path by an adaptive masking method through the transformer layer and refine word representations over this disentangled word graph through the refinement layer. For the unobserved factors, we resolve the do-operation query from the front door path by decomposing word representations into query related and unrelated parts through the decomposition layer. Pairwise ranking loss is mainly used for the ad hoc document ranking task, triangle distance loss is introduced to both the transformer and refinement layers for more discriminative representations, and mutual information constraints are put on the decomposition layer. Experimental results on public benchmark datasets TREC Robust04 and WebTrack2009-12 show that DGRe outperforms state-of-the-art baselines more than 2% especially for short queries.
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Fauceglia, Nicolas, Mustafa Canim, Alfio Gliozzo, Jennifer J. Liang, Nancy Xin Ru Wang, Douglas Burdick, Nandana Mihindukulasooriya, et al. "KAAPA: Knowledge Aware Answers from PDF Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16029–31. http://dx.doi.org/10.1609/aaai.v35i18.18002.

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We present KaaPa (Knowledge Aware Answers from Pdf Analysis), an integrated solution for machine reading comprehension over both text and tables extracted from PDFs. KaaPa enables interactive question refinement using facets generated from an automatically induced Knowledge Graph. In addition it provides a concise summary of the supporting evidence for the provided answers by aggregating information across multiple sources. KaaPa can be applied consistently to any collection of documents in English with zero domain adaptation effort. We showcase the use of KaaPa for QA on scientific literature using the COVID-19 Open Research Dataset.
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Koutra, Danai. "The power of summarization in graph mining and learning." Proceedings of the VLDB Endowment 14, no. 13 (September 2021): 3416. http://dx.doi.org/10.14778/3484224.3484238.

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Our ability to generate, collect, and archive data related to everyday activities, such as interacting on social media, browsing the web, and monitoring well-being, is rapidly increasing. Getting the most benefit from this large-scale data requires analysis of patterns it contains, which is computationally intensive or even intractable. Summarization techniques produce compact data representations (summaries) that enable faster processing by complex algorithms and queries. This talk will cover summarization of interconnected data (graphs) [3], which can represent a variety of natural processes (e.g., friendships, communication). I will present an overview of my group's work on bridging the gap between research on summarized network representations and real-world problems. Examples include summarization of massive knowledge graphs for refinement [2] and on-device querying [4], summarization of graph streams for persistent activity detection [1], and summarization within graph neural networks for fast, interpretable classification [5]. I will conclude with open challenges and opportunities for future research.
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Huang, Yu-Xuan, Wang-Zhou Dai, Yuan Jiang, and Zhi-Hua Zhou. "Enabling Knowledge Refinement upon New Concepts in Abductive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 7928–35. http://dx.doi.org/10.1609/aaai.v37i7.25959.

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Recently there are great efforts on leveraging machine learning and logical reasoning. Many approaches start from a given knowledge base, and then try to utilize the knowledge to help machine learning. In real practice, however, the given knowledge base can often be incomplete or even noisy, and thus, it is crucial to develop the ability of knowledge refinement or enhancement. This paper proposes to enable the Abductive learning (ABL) paradigm to have the ability of knowledge refinement/enhancement. In particular, we focus on the problem that, in contrast to closed-environment tasks where a fixed set of symbols are enough to represent the concepts in the domain, in open-environment tasks new concepts may emerge. Ignoring those new concepts can lead to significant performance decay, whereas it is challenging to identify new concepts and add them to the existing knowledge base with potential conflicts resolved. We propose the ABL_nc approach which exploits machine learning in ABL to identify new concepts from data, exploits knowledge graph to match them with entities, and refines existing knowledge base to resolve conflicts. The refined/enhanced knowledge base can then be used in the next loop of ABL and help improve the performance of machine learning. Experiments on three neuro-symbolic learning tasks verified the effectiveness of the proposed approach.
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Dissertations / Theses on the topic "Knowledge graph refinement"

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Khajeh, Nassiri Armita. "Expressive Rule Discovery for Knowledge Graph Refinement." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG045.

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Les graphes de connaissances (KG) sont des structures de graphes hétérogènes représentant des faits dans un format lisible par une machine. Ils trouvent des applications dans des tâches telles que la réponse automatique aux questions, la désambiguïsation et liaison d'entités. Cependant, les graphes de connaissances sont intrinsèquement incomplets et il est essentiel de les raffiner pour améliorer leur qualité. Pour compléter le graphe de connaissances, il est possible de prédire les liens manquants dans un graphe de connaissances ou d'intégrer des sources externes. En extrayant des règles du graphe de connaissances, nous pouvons les exploiter pour compléter le graphe tout en fournissant des explications. Plusieurs approches ont été proposées pour extraire efficacement des règles. Or, la littérature manque de méthodes efficaces pour incorporer des prédicats numériques dans les règles. Pour répondre à cette lacune, nous proposons REGNUM, qui permet d'extraire des règles numériques avec des contraintes d'intervalle. REGNUM s'appuie sur les règles générées par un système d'extraction de règles existant et les enrichit en incorporant des prédicats numériques guidés par des mesures de qualité. En outre, la nature interconnectée des données web offre un potentiel significatif pour compléter et raffiner les KG, par exemple, par le liage des données, qui consiste à trouver des liens d'identité entre des entités de KG différents. Nous présentons RE-miner, une approche qui extrait des expressions référentielles (RE) pour une classe dans un graphe de connaissances.Les REs sont des règles qui ne s'appliquent qu'à une seule entité. Elles facilitent la découverte de connaissances et permettent de lier les données de manière explicable. De plus, nous visons à explorer les avantages et les opportunités de l'affinage des modèles linguistiques pour combler le fossé entre les KG et les données textuelles. Nous présentons GilBERT, qui exploite le fine-tuning sur des modèles linguistiques tels que BERT en optimisant une fonction de coût par triplet pour les tâches de prédiction de relation et de classification de triple. En prenant en compte ces défis et en proposant des approches novatrices, cette thèse contribue au raffinement des KG, en mettant particulièrement l'accent sur l'explicabilité et la découverte de connaissances. Les résultats de cette recherche ouvrent la voie à de nouvelles questions de recherche qui font progresser vers des KG de meilleure qualité
Knowledge graphs (KGs) are heterogeneous graph structures representing facts in a machine-readable format. They find applications in tasks such as question answering, disambiguation, and entity linking. However, KGs are inherently incomplete, and refining them is crucial to improve their effectiveness in downstream tasks. It's possible to complete the KGs by predicting missing links within a knowledge graph or integrating external sources and KGs. By extracting rules from the KG, we can leverage them to complete the graph while providing explainability. Various approaches have been proposed to mine rules efficiently. Yet, the literature lacks effective methods for effectively incorporating numerical predicates in rules. To address this gap, we propose REGNUM, which mines numerical rules with interval constraints. REGNUM builds upon the rules generated by an existing rule mining system and enriches them by incorporating numerical predicates guided by quality measures. Additionally, the interconnected nature of web data offers significant potential for completing and refining KGs, for instance, by data linking, which is the task of finding sameAs links between entities of different KGs. We introduce RE-miner, an approach that mines referring expressions (REs) for a class in a knowledge graph and uses them for data linking. REs are rules that are only applied to one entity. They support knowledge discovery and serve as an explainable way to link data. We employ pruning strategies to explore the search space efficiently, and we define characteristics to generate REs that are more relevant for data linking. Furthermore, we aim to explore the advantages and opportunities of fine-tuning language models to bridge the gap between KGs and textual data. We propose GilBERT, which leverages fine-tuning techniques on language models like BERT using a triplet loss. GilBERT demonstrates promising results for refinement tasks of relation prediction and triple classification tasks. By considering these challenges and proposing novel approaches, this thesis contributes to KG refinement, particularly emphasizing explainability and knowledge discovery. The outcomes of this research open doors to more research questions and pave the way for advancing towards more accurate and comprehensive KGs
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Gad-Elrab, Mohamed Hassan Mohamed [Verfasser]. "Explainable methods for knowledge graph refinement and exploration via symbolic reasoning / Mohamed Hassan Mohamed Gad-Elrab." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1239645341/34.

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Maus, Aaron. "Formulation of Hybrid Knowledge-Based/Molecular Mechanics Potentials for Protein Structure Refinement and a Novel Graph Theoretical Protein Structure Comparison and Analysis Technique." ScholarWorks@UNO, 2019. https://scholarworks.uno.edu/td/2673.

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Proteins are the fundamental machinery that enables the functions of life. It is critical to understand them not just for basic biology, but also to enable medical advances. The field of protein structure prediction is concerned with developing computational techniques to predict protein structure and function from a protein’s amino acid sequence, encoded for directly in DNA, alone. Despite much progress since the first computational models in the late 1960’s, techniques for the prediction of protein structure still cannot reliably produce structures of high enough accuracy to enable desired applications such as rational drug design. Protein structure refinement is the process of modifying a predicted model of a protein to bring it closer to its native state. In this dissertation a protein structure refinement technique, that of potential energy minimization using hybrid molecular mechanics/knowledge based potential energy functions is examined in detail. The generation of the knowledge-based component is critically analyzed, and in the end, a potential that is a modest improvement over the original is presented. This dissertation also examines the task of protein structure comparison. In evaluating various protein structure prediction techniques, it is crucial to be able to compare produced models against known structures to understand how well the technique performs. A novel technique is proposed that allows an in-depth yet intuitive evaluation of the local similarities between protein structures. Based on a graph analysis of pairwise atomic distance similarities, multiple regions of structural similarity can be identified between structures independently of relative orientation. Multidomain structures can be evaluated and this technique can be combined with global measures of similarity such as the global distance test. This method of comparison is expected to have broad applications in rational drug design, the evolutionary study of protein structures, and in the analysis of the protein structure prediction effort.
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Melo, André [Verfasser], and Heiko [Akademischer Betreuer] Paulheim. "Automatic refinement of large-scale cross-domain knowledge graphs / André Melo ; Betreuer: Heiko Paulheim." Mannheim : Universitätsbibliothek Mannheim, 2018. http://d-nb.info/1167160584/34.

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Melo, André Verfasser], and Heiko [Akademischer Betreuer] [Paulheim. "Automatic refinement of large-scale cross-domain knowledge graphs / André Melo ; Betreuer: Heiko Paulheim." Mannheim : Universitätsbibliothek Mannheim, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:180-madoc-459801.

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Book chapters on the topic "Knowledge graph refinement"

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Cui, Jie, Fei Pu, and Bailin Yang. "Dual-Dimensional Refinement of Knowledge Graph Embedding Representation." In Knowledge Science, Engineering and Management, 124–37. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40283-8_12.

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Liu, Yifan, Bin Shang, Chenxin Wang, and Yinliang Zhao. "Knowledge Graph Completion with Information Adaptation and Refinement." In Advanced Data Mining and Applications, 16–31. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_2.

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Ebeid, Islam Akef, Majdi Hassan, Tingyi Wanyan, Jack Roper, Abhik Seal, and Ying Ding. "Biomedical Knowledge Graph Refinement and Completion Using Graph Representation Learning and Top-K Similarity Measure." In Diversity, Divergence, Dialogue, 112–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71292-1_10.

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Chen, Chen, Yufei Wang, Yang Zhang, Quan Z. Sheng, and Kwok-Yan Lam. "Separate-and-Aggregate: A Transformer-Based Patch Refinement Model for Knowledge Graph Completion." In Advanced Data Mining and Applications, 62–77. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_5.

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Hogan, Aidan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs, Roberto Navigli, et al. "Refinement." In Knowledge Graphs, 127–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01918-0_8.

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Yoo, Illhoi, and Xiaohua Hu. "Clustering Large Collection of Biomedical Literature Based on Ontology-Enriched Bipartite Graph Representation and Mutual Refinement Strategy." In Advances in Knowledge Discovery and Data Mining, 303–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_36.

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Kosa, Victoria, Oles Dobosevych, and Vadim Ermolayev. "Terminology Saturation Analysis: Refinements and Applications." In AI, Data, and Digitalization, 25–41. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53770-7_3.

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AbstractIn this paper, we outline the results of our recent research on terminology saturation analysis (TSA) in subject domain-bounded textual corpora. We present the developed TSA method. We further report about the two use cases that proved the validity, efficiency, and effectiveness of TSA. Based on our experience of TSA use, we analyse the shortcomings of the method and figure out the ways to refinement and improvement. Further, we share our prognoses on how TSA could be used for: (i) generating quality datasets of minimal size for training large language models for performing better in scientific domains; (ii) iteratively constructing domain ontologies and knowledge graphs that representatively describe a subject domain, or topic; or (iii) detecting and predicting events based on the TSA of textual streams data.
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Buhl, Dominik, Daniel Szafarski, Laslo Welz, and Carsten Lanquillon. "Conversation-Driven Refinement of Knowledge Graphs: True Active Learning with Humans in the Chatbot Application Loop." In Artificial Intelligence in HCI, 41–54. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35894-4_3.

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Schürmann, Felix, Jean-Denis Courcol, and Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue." In Advances in Experimental Medicine and Biology, 237–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.

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AbstractIt has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.
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Schürmann, Felix, Jean-Denis Courcol, and Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue." In Advances in Experimental Medicine and Biology, 237–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.

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AbstractIt has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.
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Conference papers on the topic "Knowledge graph refinement"

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Zheng, Liu. "A Novel Graph-Based Image Annotation Refinement Algorithm." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.369.

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Wu, Jiaying, and Bryan Hooi. "DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599298.

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Saeedizade, Mohammad Javad, Najmeh Torabian, and Behrouz Minaei-Bidgoli. "KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods." In Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.sustainlp-1.3.

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Zhang, Qingheng, Zequn Sun, Wei Hu, Muhao Chen, Lingbing Guo, and Yuzhong Qu. "Multi-view Knowledge Graph Embedding for Entity Alignment." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/754.

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We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.
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Li, Zhongyang, Xiao Ding, Ting Liu, J. Edward Hu, and Benjamin Van Durme. "Guided Generation of Cause and Effect." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/502.

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We present a conditional text generation framework that posits sentential expressions of possible causes and effects. This framework depends on two novel resources we develop in the course of this work: a very large-scale collection of English sentences expressing causal patterns (CausalBank); and a refinement over previous work on constructing large lexical causal knowledge graphs (Cause Effect Graph). Further, we extend prior work in lexically-constrained decoding to support disjunctive positive constraints. Human assessment confirms that our approach gives high-quality and diverse outputs. Finally, we use CausalBank to perform continued training of an encoder supporting a recent state-of-the-art model for causal reasoning, leading to a 3-point improvement on the COPA challenge set, with no change in model architecture.
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Heyrani Nobari, Amin, Justin Rey, Suhas Kodali, Matthew Jones, and Faez Ahmed. "AutoSurf: Automated Expert-Guided Meshing With Graph Neural Networks and Conformal Predictions." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-115065.

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Abstract Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly refined meshes may ensure precision, they come with high computational costs. Similarly, adaptive remeshing techniques require multiple simulations and come at a great computational cost. This means that the meshing process is reliant upon expert knowledge and years of experience. Automating mesh generation can save significant time and effort and lead to a faster and more efficient design process. This paper presents a machine learning-based scheme that utilizes Graph Neural Networks (GNN) and expert guidance to automatically generate CFD meshes for aircraft models. In this work, we introduce a new 3D segmentation algorithm that outperforms two state-of-the-art models, Point-Net++ and PointMLP, for surface classification. We also present a novel approach to project predictions from 3D mesh segmentation models to CAD surfaces using the conformal predictions method, which provides marginal statistical guarantees and robust uncertainty quantification and handling. We demonstrate that the addition of conformal predictions effectively enables the model to avoid under-refinement, hence failure, in CFD meshing even for weak and less accurate models. Finally, we demonstrate the efficacy of our approach through a real-world case study that demonstrates that our automatically generated mesh is comparable in quality to expert-generated meshes, and enables the solver to converge and produce accurate results. The code and data for this project is made publicly available at https://github.com/ahnobari/AutoSurf.
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Minh Le, Thao, Vuong Le, Svetha Venkatesh, and Truyen Tran. "Dynamic Language Binding in Relational Visual Reasoning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/114.

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We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering. Relaxing the common assumption made by current models that the object predicates pre-exist and stay static, passive to the reasoning process, we propose that these dynamic predicates expand across the domain borders to include pair-wise visual-linguistic object binding. In our method, these contextualized object links are actively found within each recurrent reasoning step without relying on external predicative priors. These dynamic structures reflect the conditional dual-domain object dependency given the evolving context of the reasoning through co-attention. Such discovered dynamic graphs facilitate multi-step knowledge combination and refinements that iteratively deduce the compact representation of the final answer. The effectiveness of this model is demonstrated on image question answering demonstrating favorable performance on major VQA datasets. Our method outperforms other methods in sophisticated question-answering tasks wherein multiple object relations are involved. The graph structure effectively assists the progress of training, and therefore the network learns efficiently compared to other reasoning models.
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Amgoud, Leila, and Vivien Beuselinck. "Equivalence of Semantics in Argumentation." In 18th International Conference on Principles of Knowledge Representation and Reasoning {KR-2021}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/kr.2021/4.

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A large number of evaluation methods, called semantics, have been proposed in the literature for assessing strength of arguments. This paper investigates their equivalence. It argues that for being equivalent, two semantics should have compatible evaluations of both individual arguments and pairs of arguments. The first requirement ensures that the two semantics judge an argument in the same way, while the second states that they provide the same ranking of arguments. We show that the two requirements are completely independent. The paper introduces three novel relations between semantics based on their rankings of arguments: weak equivalence, strong equivalence and refinement. They state respectively that two semantics do not disagree on their strict rankings; the rankings of the semantics coincide; one semantics agrees with the strict comparisons of the second and it may break some of its ties. We investigate the properties of the three relations and their links with existing principles of semantics, and study the nature of relations between most of the existing semantics. The results show that the main extensions semantics are pairwise weakly equivalent. The gradual semantics we considered are pairwise incompatible, however some pairs are strongly equivalent in case of flat graphs including Max-based (Mbs) and Euler-based (Ebs), for which we provide full characterizations in terms respectively of Fibonacci numbers and the numbers of an exponential series. Furthermore, we show that both semantics (Mbs, EMbs) refine the grounded semantics, and are weakly equivalent with the other extension semantics. We show also that in case of flat graphs, the two gradual semantics Trust-based and Iterative Schema characterize the grounded semantics, making thus bridges between gradual semantics and extension semantics. Finally, the other gradual semantics are incompatible with extension semantics.
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