Relevant bibliographies by topics / Knowledge Graphs (KG)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Knowledge Graphs (KG)'

Author: Grafiati
Published: 6 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "Knowledge Graphs (KG)"

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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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Teern, Anna, Markus Kelanti, Tero Päivärinta, and Mika Karaila. "Design Objectives for Evolvable Knowledge Graphs." Complex Systems Informatics and Modeling Quarterly, no. 36 (October 31, 2023): 1–15. http://dx.doi.org/10.7250/csimq.2023-36.01.

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Knowledge graphs (KGs) structure knowledge to enable the development of intelligent systems across several application domains. In industrial maintenance, comprehensive knowledge of the factory, machinery, and components is indispensable. This study defines the objectives for evolvable KGs, building upon our prior research, where we initially identified the problem in industrial maintenance. Our contributions include two main aspects: firstly, the categorization of learning within the KG construction process and the identification of design objectives for the KG process focusing on supporting
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Chen, Xuelu, Muhao Chen, Weijia Shi, Yizhou Sun, and Carlo Zaniolo. "Embedding Uncertain Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3363–70. http://dx.doi.org/10.1609/aaai.v33i01.33013363.

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Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by
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Li, Tongxin, Weiping Wang, Xiaobo Li, Tao Wang, Xin Zhou, and Meigen Huang. "Embedding Uncertain Temporal Knowledge Graphs." Mathematics 11, no. 3 (2023): 775. http://dx.doi.org/10.3390/math11030775.

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Knowledge graph (KG) embedding for predicting missing relation facts in incomplete knowledge graphs (KGs) has been widely explored. In addition to the benchmark triple structural information such as head entities, tail entities, and the relations between them, there is a large amount of uncertain and temporal information, which is difficult to be exploited in KG embeddings, and there are some embedding models specifically for uncertain KGs and temporal KGs. However, these models either only utilize uncertain information or only temporal information, without integrating both kinds of informatio
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Bellomarini, Luigi, Marco Benedetti, Andrea Gentili, Davide Magnanimi, and Emanuel Sallinger. "KG-Roar: Interactive Datalog-Based Reasoning on Virtual Knowledge Graphs." Proceedings of the VLDB Endowment 16, no. 12 (2023): 4014–17. http://dx.doi.org/10.14778/3611540.3611609.

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Logic-based Knowledge Graphs (KGs) are gaining momentum in academia and industry thanks to the rise of expressive and efficient languages for Knowledge Representation and Reasoning (KRR). These languages accurately express business rules, through which valuable new knowledge is derived. A versatile and scalable backend reasoner, like Vadalog, a state-of-the-art system for logic-based KGs---based on an extension of Datalog---executes the reasoning. In this demo, we present KG-Roar, a web-based interactive development and navigation environment for logical KGs. The system lets the user augment a
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Bizon, Chris, Steven Cox, James Balhoff, et al. "ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources." Journal of Chemical Information and Modeling 59, no. 12 (2019): 4968–73. http://dx.doi.org/10.1021/acs.jcim.9b00683.

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Gu, Qianqian, Ben Scott, and Vincent Smith. "Enhancing Botanical Knowledge Graphs with Machine Learning." Biodiversity Information Science and Standards 6 (August 23, 2022): e91384. https://doi.org/10.3897/biss.6.91384.

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Integrating sparse and incomplete biodiversity data into a global, coherent data space and generating machine-readable data infrastructures is a challenge in biodiversity informatics. In recent years, biodiversity data researchers have started proposing Knowledge Graphs (KGs) as one approach to connecting biodiversity data worldwide (Page 2019), representing the connections between the what, when, and where of objects in natural history collections. At the Natural History Museum (NHM) we have constructed a KG of botanical specimens and collectors, encoded into numerical representations, and us
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Orogat, Abdelghny, and Ahmed El-Roby. "Maestro: Automatic Generation of Comprehensive Benchmarks for Question Answering Over Knowledge Graphs." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–24. http://dx.doi.org/10.1145/3589322.

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Recently, there has been an upsurge in the number of knowledge graphs (KG) that can only be accessed by experts. Non-expert users lack an adequate understanding of the queried knowledge graph's vocabulary and structure, as well as the syntax of the structured query language used to express the user's information needs. To increase the user base of these KGs, a set of Question Answering (QA) systems that use natural language to query these knowledge graphs have been introduced. However, finding a benchmark that accurately evaluates the quality of a QA system is a difficult task due to (1) the h
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Niu, Guanglin, Yongfei Zhang, Bo Li, et al. "Rule-Guided Compositional Representation Learning on Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2950–58. http://dx.doi.org/10.1609/aaai.v34i03.5687.

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Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability
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Cui, Yuanning, Yuxin Wang, Zequn Sun, et al. "Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4217–24. http://dx.doi.org/10.1609/aaai.v37i4.25539.

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Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG w
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Dissertations / Theses on the topic "Knowledge Graphs (KG)"

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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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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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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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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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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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Book chapters on the topic "Knowledge Graphs (KG)"

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Syed, Naima, Shadab Alam Siddiqui, and Hazra Imran. "Exploring Knowledge Graphs (KG): A Comprehensive Overview." In Transactions on Computer Systems and Networks. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3962-2_16.

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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
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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 log
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Ranganathan, Varun, and Natarajan Subramanyam. "SDE-KG: A Stochastic Dynamic Environment for Knowledge Graphs." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_39.

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Niazmand, Emetis, and Maria-Esther Vidal. "SAP-KG: Synonym Predicate Analyzer Across Multiple Knowledge Graphs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43458-7_9.

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Ghosh, Devanshika, and Enayat Rajabi. "KG-Visual: A Tool for Visualizing RDF Knowledge Graphs." In Metadata and Semantic Research. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98876-0_11.

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Martinez-Gil, Jorge, Thomas Hoch, Mario Pichler, et al. "Examining the Adoption of Knowledge Graphs in the Manufacturing Industry: A Comprehensive Review." In Artificial Intelligence in Manufacturing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_4.

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AbstractThe integration of Knowledge Graphs (KGs) in the manufacturing industry can significantly enhance the efficiency and flexibility of production lines and improve product quality. By integrating and contextualizing information about devices, equipment, production resources, location, usage, and related data, KGs can be a powerful operational tool. Moreover, KGs can contribute to the intelligence of manufacturing processes by providing insights into the complex and competitive manufacturing landscape. This research work presents a comprehensive analysis of the current trends utilizing KG
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Kacupaj, Endri, Kuldeep Singh, Maria Maleshkova, and Jens Lehmann. "Conversational Question Answering over Knowledge Graphs." In Event Analytics across Languages and Communities. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64451-1_9.

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AbstractQuestion answering (QA) over knowledge graphs (KGs) is an essential task that maps a user’s utterance to a query over a KG to retrieve the correct answer. Earlier methods in this field relied heavily on predefined templates and rules, which had limited adaptability and learning capability. Recent research has made significant strides in answering straightforward questions, and there has been notable success in tackling more intricate queries as well. However, a key challenge remains that, often, a single round of question and answer is not enough. Users might have follow-up questions t
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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 languag
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Hetfleisch, Ruben, Maximilian Nowak, and Fazel Ansari. "Automating EU Taxonomy Reporting: Can Generative AI Facilitate Corporate Sustainability Reporting?" In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93891-7_7.

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Abstract To achieve global sustainability goals, enterprises are obliged to declare sustainability of their economic activities as part of EU Taxonomy reporting. Due to a lack of capacity and expertise, SMEs are unable to adequately fulfil this reporting requirement. A significant degree of automation is required to enable SMEs to efficiently comply with reporting. Recent advances in generative AI entails potentials to overcome several technical challenges inter alia i) heterogeneity of data, ii) necessary semantic intelligence, iii) usability requirements, and iv) assuring quality and reprodu
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Conference papers on the topic "Knowledge Graphs (KG)"

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Zeng, Xinyuan, Yiqin Lu, Yang Su, Weijie Qiu, Xu Lin, and Kaiqiong Chen. "KG-BGCN: A Secondary Power Equipment Classification Model Based on Knowledge Graphs." In 2024 Boao New Power System International Forum - Power System and New Energy Technology Innovation Forum (NPSIF). IEEE, 2024. https://doi.org/10.1109/npsif64134.2024.10883458.

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Yang, Rui, Haoran Liu, Edison Marrese-Taylor, et al. "KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques." In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.bionlp-1.13.

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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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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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Reports on the topic "Knowledge Graphs (KG)"

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Law, Edward, Samuel Gan-Mor, Hazel Wetzstein, and Dan Eisikowitch. Electrostatic Processes Underlying Natural and Mechanized Transfer of Pollen. United States Department of Agriculture, 1998. http://dx.doi.org/10.32747/1998.7613035.bard.

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The project objective was to more fully understand how the motion of pollen grains may be controlled by electrostatic forces, and to develop a reliable mechanized pollination system based upon sound electrostatic and aerodynamic principles. Theoretical and experimental analyses and computer simulation methods which investigated electrostatic aspects of natural pollen transfer by insects found that: a) actively flying honeybees accumulate ~ 23 pC average charge (93 pC max.) which elevates their bodies to ~ 47 V likely by triboelectrification, inducing ~ 10 fC of opposite charge onto nearby poll
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