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Littérature scientifique sur le sujet « Knowledge Graph (KG) »

Auteur : Grafiati

Publié le 7 juillet 2024

Mis à jour le 19 juillet 2025

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Articles de revues sur le sujet "Knowledge Graph (KG)"

Hao, Wu, Jiao Menglin, Tian Guohui, Ma Qing, and Liu Guoliang. "R-KG: A Novel Method for Implementing a Robot Intelligent Service." AI 1, no. 1 (2020): 117–40. http://dx.doi.org/10.3390/ai1010006.

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Aiming to solve the problem of environmental information being difficult to characterize when an intelligent service is used, knowledge graphs are used to express environmental information when performing intelligent services. Here, we specially design a kind of knowledge graph for environment expression referred to as a robot knowledge graph (R-KG). The main work of a R-KG is to integrate the diverse semantic information in the environment and pay attention to the relationship at the instance level. Also, through the efficient knowledge organization of a R-KG, robots can fully understand the

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Khan, Arijit. "Knowledge Graphs Querying." ACM SIGMOD Record 52, no. 2 (2023): 18–29. http://dx.doi.org/10.1145/3615952.3615956.

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Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples - that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities. Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. While significant progress has been made on KG construction and curation, thanks to deep learning rece

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Fang, Yin, Qiang Zhang, Haihong Yang, et al. "Molecular Contrastive Learning with Chemical Element Knowledge Graph." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 3968–76. http://dx.doi.org/10.1609/aaai.v36i4.20313.

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Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive learning is a promising paradigm as it utilizes self-supervision signals and has no requirements for human annotations. However, prior works fail to incorporate fundamental domain knowledge into graph semantics and thus ignore the correlations between atoms that have common attributes but are not directly connected by bonds. To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize m

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Bai, Liting, Lin Liu, Shengli Song, and Yueshen Xu. "NCR-KG: news community recommendation with knowledge graph." CCF Transactions on Pervasive Computing and Interaction 1, no. 4 (2019): 250–59. http://dx.doi.org/10.1007/s42486-019-00020-3.

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Tong, Peihao, Qifan Zhang, and Junjie Yao. "Leveraging Domain Context for Question Answering Over Knowledge Graph." Data Science and Engineering 4, no. 4 (2019): 323–35. http://dx.doi.org/10.1007/s41019-019-00109-w.

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Abstract With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questi

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Swapnil, S. Mahure. "Missing Link Prediction in Art Knowledge Graph using Representation Learning." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 5 (2024): 30–33. https://doi.org/10.35940/ijitee.J9264.13050424.

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<strong>Abstract:</strong> Knowledge graphs are an important evolving field in Artificial Intelligence domain which has multiple applications such as in question answering, important information retrieval, information recommendation, Natural language processing etc. Knowledge graph has one big limitation i.e. Incompleteness, it is due to because of real world data are dynamic and continues evolving. This incompleteness of Knowledge graph can be overcome or minimized by using representation learning models. There are several models which are classified on the base of translation distance, seman

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Tian, Xin, and Yuan Meng. "Relgraph: A Multi-Relational Graph Neural Network Framework for Knowledge Graph Reasoning Based on Relation Graph." Applied Sciences 14, no. 7 (2024): 3122. http://dx.doi.org/10.3390/app14073122.

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Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the exchange of information between predicates. To address these challenges, we introduce Relgraph, a novel KG reasoning framework. This framework introduces relation graphs to explicitly model the interactions between different relations, enabling more comprehensive and accurate handling of representation learning and reasoning tasks on KGs. Furthermor

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Kejriwal, Mayank. "Knowledge Graphs: A Practical Review of the Research Landscape." Information 13, no. 4 (2022): 161. http://dx.doi.org/10.3390/info13040161.

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Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be simply defined as a directed, labeled, multi-relational graph with some form of semantics. In part, this has been fueled by increased publication of structured datasets on the Web, and well-publicized successes of large-scale projects such as the Google Knowledge Graph and the Amazon Product Graph. However, another factor that is less discussed, but which has been equally instrumental in the success of KGs, is the cross-disci

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Yan, Yuchen, Lihui Liu, Yikun Ban, Baoyu Jing, and Hanghang Tong. "Dynamic Knowledge Graph Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4564–72. http://dx.doi.org/10.1609/aaai.v35i5.16585.

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Knowledge graph (KG for short) alignment aims at building a complete KG by linking the shared entities across complementary KGs. Existing approaches assume that KGs are static, despite the fact that almost every KG evolves over time. In this paper, we introduce the task of dynamic knowledge graph alignment, the main challenge of which is how to efficiently update entity embeddings for the evolving graph topology. Our key insight is to view the parameter matrix of GCN as a feature transformation operator and decouple the transformation process from the aggregation process. Based on that, we fir

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Zuo, H., Y. Yin, and P. Childs. "Patent-KG: Patent Knowledge Graph Extraction for Engineering Design." Proceedings of the Design Society 2 (May 2022): 821–30. http://dx.doi.org/10.1017/pds.2022.84.

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AbstractThis paper builds a patent-based knowledge graph, patent-KG, to represent the knowledge facts in patents for engineering design. The arising patent-KG approach proposes a new unsupervised mechanism to extract knowledge facts in a patent, by searching the attention graph in language models. The extracted entities are compared with other benchmarks in the criteria of recall rate. The result reaches the highest 0.8 recall rate in the standard list of mechanical engineering related technical terms, which means the highest coverage of engineering words.

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Thèses sur le sujet "Knowledge Graph (KG)"

Schaeffer, Marion. "Towards efficient Knowledge Graph-based Retrieval Augmented Generation for conversational agents." Electronic Thesis or Diss., Normandie, 2025. http://www.theses.fr/2025NORMIR06.

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Les agents conversationnels se sont largement répandus ces dernières années. Aujourd'hui, ils ont dépassé leur objectif initial de simuler une conversation avec un programme informatique et sont désormais des outils précieux pour accéder à l'information et effectuer diverses tâches, allant du service client à l'assistance personnelle. Avec l'essor des modèles génératifs et des grands modèles de langage (LLM), les capacités des agents conversationnels ont été décuplées. Cependant, ils sont désormais sujets à des hallucinations, générant ainsi des informations erronées. Une technique populaire p

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Salehpour, Masoud. "High-performance Query Processing over Knowledge Graphs." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28569.

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The label “Knowledge Graph” (KG) has been used in the literature for over four decades, typically to refer to a collection of information about real-world entities and their inter-relationships. The proliferation of KGs in recent times opens up exciting opportunities for a broad range of semantic applications such as recommendations. However, unlocking the full potential of KGs in response to the growing deployment requires data platforms to efficiently store and process the content to support various applications. What began with extensions of relational database systems to store the conte

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Sima, Xingyu. "La gestion des connaissances dans les petites et moyennes entreprises : un cadre adapté et complet." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP047.

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La connaissance est essentielle pour les organisations, particulièrement dans le contexte de l'Industrie 4.0. La Gestion des Connaissances (GC) joue un rôle critique dans le succès des organisations. Bien que la GC ait été relativement bien étudiée dans les grandes organisations, les Petites et Moyennes Entreprises (PMEs) reçoivent moins d'attention. Les PMEs font face à des défis uniques en termes de GC, nécessitant un cadre de GC dédié. Notre étude vise à définir un cadre répondant à leurs défis tout en tirant parti de leurs forces inhérentes. Cette thèse présente un cadre de GC dédié et com

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Saxena, Apoorv Umang. "Leveraging KG Embeddings for Knowledge Graph Question Answering." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6082.

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Knowledge graphs (KG) are multi-relational graphs consisting of entities as nodes and relations among them as typed edges. The goal of knowledge graph question answering (KGQA) is to answer natural language queries posed over the KG. These could be simple factoid questions such as “What is the currency of USA? ” or it could be a more complex query such as “Who was the president of USA after World War II? ”. Multiple systems have been proposed in the literature to perform KGQA, include question decomposition, semantic parsing and even graph neural network-based methods. In a separate lin

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Ojha, Prakhar. "Utilizing Worker Groups And Task Dependencies in Crowdsourcing." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4265.

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Crowdsourcing has emerged as a convenient mechanism to collect human judgments on a variety of tasks, ranging from document and image classification to scientific experimentation. However, in recent times crowdsourcing has evolved from solving simpler tasks, like recognizing objects in images, to more complex tasks such as collaborative journalism, language translation, product designing etc. Unlike simpler micro-tasks performed by a single worker, these complex tasks require a group of workers and greater resources. In such scenarios, where groups of participants are the atomic units, it is a

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Chapitres de livres sur le sujet "Knowledge Graph (KG)"

Krause, Franz, Kabul Kurniawan, Elmar Kiesling, et al. "Leveraging Semantic Representations via Knowledge Graph Embeddings." In Artificial Intelligence in Manufacturing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_5.

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AbstractThe representation and exploitation of semantics has been gaining popularity in recent research, as exemplified by the uptake of large language models in the field of Natural Language Processing (NLP) and knowledge graphs (KGs) in the Semantic Web. Although KGs are already employed in manufacturing to integrate and standardize domain knowledge, the generation and application of corresponding KG embeddings as lean feature representations of graph elements have yet to be extensively explored in this domain. Existing KGs in manufacturing often focus on top-level domain knowledge and thus ignore domain dynamics, or they lack interconnectedness, i.e., nodes primarily represent non-contextual data values with single adjacent edges, such as sensor measurements. Consequently, context-dependent KG embedding algorithms are either restricted to non-dynamic use cases or cannot be applied at all due to the given KG characteristics. Therefore, this work provides an overview of state-of-the-art KG embedding methods and their functionalities, identifying the lack of dynamic embedding formalisms and application scenarios as the key obstacles that hinder their implementation in manufacturing. Accordingly, we introduce an approach for dynamizing existing KG embeddings based on local embedding reconstructions. Furthermore, we address the utilization of KG embeddings in the Horizon2020 project Teaming.AI (www.teamingai-project.eu.) focusing on their respective benefits.

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Sanou, Gaoussou, Véronique Giudicelli, Nika Abdollahi, Sofia Kossida, Konstantin Todorov, and Patrice Duroux. "IMGT-KG: A Knowledge Graph for Immunogenetics." In The Semantic Web – ISWC 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19433-7_36.

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Wu, Tianxing, Cong Gao, Guilin Qi, et al. "KG-Buddhism: The Chinese Knowledge Graph on Buddhism." In Semantic Technology. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70682-5_17.

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Kwapong, Benjamin, Amartya Sen, and Kenneth K. Fletcher. "ELECTRA-KG: A Transformer-Knowledge Graph Recommender System." In Services Computing – SCC 2022. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23515-3_5.

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Möller, Cedric. "Knowledge Graph Population with Out-of-KG Entities." In The Semantic Web: ESWC 2022 Satellite Events. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11609-4_35.

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Meng, Jiawei, and Wen Zhang. "KG-Diffusion: An Improved Knowledge Graph Completion with Diffusion." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1809-5_1.

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Pflueger, Maximilian, David J. Tena Cucala, and Egor V. Kostylev. "GNNQ: A Neuro-Symbolic Approach to Query Answering over Incomplete Knowledge Graphs." In The Semantic Web – ISWC 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19433-7_28.

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AbstractReal-world knowledge graphs (KGs) are usually incomplete—that is, miss some facts representing valid information. So, when applied to such KGs, standard symbolic query engines fail to produce answers that are expected but not logically entailed by the KGs. To overcome this issue, state-of-the-art ML-based approaches first embed KGs and queries into a low-dimensional vector space, and then produce query answers based on the proximity of the candidate entity and the query embeddings in the embedding space. This allows embedding-based approaches to obtain expected answers that are not logically entailed. However, embedding-based approaches are not applicable in the inductive setting, where KG entities (i.e., constants) seen at runtime may differ from those seen during training. In this paper, we propose a novel neuro-symbolic approach to query answering over incomplete KGs applicable in the inductive setting. Our approach first symbolically augments the input KG with facts representing parts of the KG that match query fragments, and then applies a generalisation of the Relational Graph Convolutional Networks (RGCNs) to the augmented KG to produce the predicted query answers. We formally prove that, under reasonable assumptions, our approach can capture an approach based on vanilla RGCNs (and no KG augmentation) using a (often substantially) smaller number of layers. Finally, we empirically validate our theoretical findings by evaluating an implementation of our approach against the RGCN baseline on several dedicated benchmarks.

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Motger, Quim, Xavier Franch, and Jordi Marco. "MApp-KG: Mobile App Knowledge Graph for Document-Based Feature Knowledge Generation." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61000-4_15.

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Meyer, Lars-Peter, Claus Stadler, Johannes Frey, et al. "LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-43705-3_8.

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ZusammenfassungKnowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work.Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.Zusammenfassung. Wissensgraphen (englisch Knowledge Graphs, KGs), bieten uns eine strukturierte, flexible, transparente, systemübergreifende und kollaborative Möglichkeit, unser Wissen und unsere Daten über verschiedene Bereiche der Gesellschaft und der industriellen sowie wissenschaftlichen Disziplinen hinweg zu organisieren. KGs übertreffen jede andere Form der Repräsentation in Bezug auf die Effektivität. Die Entwicklung von Wissensgraphen (englisch Knowledge Graph Engineering, KGE) erfordert jedoch fundierte Erfahrungen mit Graphstrukturen, Webtechnologien, bestehenden Modellen und Vokabularen, Regelwerken, Logik sowie Best Practices. Es erfordert auch einen erheblichen Arbeitsaufwand.In Anbetracht der Fortschritte bei großen Sprachmodellen (englisch Large Language Modells, LLMs) und ihren Schnittstellen und Anwendungen in den letzten Jahren haben wir umfassende Experimente mit ChatGPT durchgeführt, um sein Potenzial zur Unterstützung von KGE zu untersuchen. In diesem Artikel stellen wir eine Auswahl dieser Experimente und ihre Ergebnisse vor, um zu zeigen, wie ChatGPT uns bei der Entwicklung und Verwaltung von KGs unterstützen kann.

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Dessì, Danilo, Francesco Osborne, Diego Reforgiato Recupero, Davide Buscaldi, Enrico Motta, and Harald Sack. "AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62466-8_9.

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Actes de conférences sur le sujet "Knowledge Graph (KG)"

Kini, Venkataramana, Ravi Divvela, Unmesh Phadke, Narayanan Sadagopan, Fei Wang та Zhen Wen. "Trajectory Boosted Transformer Model and KG/LLM based μ-Genre for PV Offer/Content Type Arbitration". У 2024 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00015.

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Hu, Shuhao, Xin Wang, Ji Xiang, Xiaobo Guo, Lei Wang, and Jiahui Shen. "CoMuS-KG: A Collaborative Framework of Multimodal Unstructured Data and Knowledge Graph." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033342.

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Qiao, Wanbang, Zhiying Geng, and Hongbo Liu. "KG-PEM: A Data Privacy Protection Assessment Framework Based on Knowledge Graph." In 2025 10th International Conference on Computer and Communication System (ICCCS). IEEE, 2025. https://doi.org/10.1109/icccs65393.2025.11069453.

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Xu, Yao, Shizhu He, Jiabei Chen, et al. "Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.1023.

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Chen, Hanzhu, Xu Shen, Qitan Lv, Jie Wang, Xiaoqi Ni, and Jieping Ye. "SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graph." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.238.

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Tian, Shiyu, Yangyang Luo, Tianze Xu, et al. "KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.229.

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Lai, Xuan, Lianggui Tang, Xiuling Zhu, Liyong Xiao, Zhuo Chen, and Jiajun Yang. "KG-CQAM: knowledge graph and mind-mapping-based complex question answering for large language models." In Fifth International Conference on Control, Robotics, and Intelligent System (2024), edited by Chenguang Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3050113.

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Li, Haotian, Congmin Xia, Youjuan Hou, Sile Hu, Jiang Quan, and Yanjun Liu. "TCMRD-KG: Design and Development of a Rheumatism Knowledge Graph Based on Ancient Chinese Literature." In 2024 IEEE International Conference on Medical Artificial Intelligence (MedAI). IEEE, 2024. https://doi.org/10.1109/medai62885.2024.00083.

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Zheng, Zaiyi, Yushun Dong, Song Wang, Haochen Liu, Qi Wang, and Jundong Li. "KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10826107.

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Morris, Matthew, David J. Tena Cucala, Bernardo Cuenca Grau, and Ian Horrocks. "Relational Graph Convolutional Networks Do Not Learn Sound Rules." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/84.

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Graph neural networks (GNNs) are frequently used to predict missing facts in knowledge graphs (KGs). Motivated by the lack of explainability for the outputs of these models, recent work has aimed to explain their predictions using Datalog, a widely used logic-based formalism. However, such work has been restricted to certain subclasses of GNNs. In this paper, we consider one of the most popular GNN architectures for KGs, R-GCN, and we provide two methods to extract rules that explain its predictions and are sound, in the sense that each fact derived by the rules is also predicted by the GNN, f

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