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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 environment. The R-KG firstly integrates knowledge from different sources to form a unified and standardized representation of a knowledge graph. Then, the deep logical relationship hidden in the knowledge graph is explored. To this end, a knowledge reasoning model based on a Markov logic network is proposed to realize the self-developmental ability of the knowledge graph and to further enrich it. Finally, as the strength of environment expression directly affects the efficiency of robots performing services, in order to verify the efficiency of the R-KG, it is used here as the semantic map that can be directly used by a robot for performing intelligent services. The final results prove that the R-KG can effectively express environmental information.

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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 recently we have seen a surge of research on KG querying and QA. The objectives of our survey are two-fold. First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing (NLP), with different focus and terminologies; and also in diverse topics ranging from graph databases, query languages, join algorithms, graph patterns matching, to more sophisticated KG embedding and natural language questions (NLQs). We aim at uniting different interdisciplinary topics and concepts that have been developed for KG querying. Second, many recent advances on KG and query embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and computer vision domains. We identify important challenges of KG querying that received less attention by graph databases, and by the DB community in general, e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude by discussing interesting opportunities for the data management community, for instance, KG as a unified data model and vector-based query processing.

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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 microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning. KCL framework consists of three modules. The first module, knowledge-guided graph augmentation, augments the original molecular graph based on the Chemical Element KG. The second module, knowledge-aware graph representation, extracts molecular representations with a common graph encoder for the original molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to encode complex information in the augmented molecular graph. The final module is a contrastive objective, where we maximize agreement between these two views of molecular graphs. Extensive experiments demonstrated that KCL obtained superior performances against state-of-the-art baselines on eight molecular datasets. Visualization experiments properly interpret what KCL has learned from atoms and attributes in the augmented molecular graphs.

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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 questions, we enrich them with users’ subsequent input questions within a session and expand the input question representation. For the candidate answers, we equip them with surrounding context structures, i.e., meta-paths within the targeting knowledge graph. On top of these, we design a cross-attention mechanism to improve the question and answer matching performance. An experimental study on real datasets verifies these improvements. The new approach is especially beneficial for specific knowledge graphs with complex questions.

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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, semantic information and NN (Neural Network) based. Earlier the various embedding models are test on mostly two well-known datasets WN18RR & FB15k-237. In this paper, new dataset i.e. ArtGraph has been utilised for link prediction using representation learning models to enhance the utilization of ArtGraph in various domains. Experimental results shown ConvKB performed better over the other models for link prediction task.

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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. Furthermore, we design a machine learning algorithm based on the attention mechanism to simultaneously optimize the original graph and its corresponding relation graph. Benchmark and experimental results on large-scale KGs demonstrate that the Relgraph framework improves KG reasoning performance. The framework exhibits a certain degree of versatility and can be seamlessly integrated with various traditional translation models.

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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-disciplinary nature of academic KG research. Arguably, because of the diversity of this research, a synthesis of how different KG research strands all tie together could serve a useful role in enabling more ‘moonshot’ research and large-scale collaborations. This review of the KG research landscape attempts to provide such a synthesis by first showing what the major strands of research are, and how those strands map to different communities, such as Natural Language Processing, Databases and Semantic Web. A unified framework is suggested in which to view the distinct, but overlapping, foci of KG research within these communities.

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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 first propose a novel base algorithm (DINGAL-B) with topology-invariant mask gate and highway gate, which consistently outperforms 14 existing knowledge graph alignment methods in the static setting. More importantly, it naturally leads to two effective and efficient algorithms to align dynamic knowledge graph, including (1) DINGAL-O which leverages previous parameter matrices to update the embeddings of affected entities; and (2) DINGAL-U which resorts to newly obtained anchor links to fine-tune parameter matrices. Compared with their static counterpart (DINGAL-B), DINGAL-U and DINGAL-O are 10× and 100× faster respectively, with little alignment accuracy loss.

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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 pour limiter le risque d'hallucinations est la génération augmentée par récupération (RAG), qui permet d'injecter des connaissances lors de la génération de texte. Ces connaissances peuvent être extraites de graphes de connaissances (KG), qui sont des représentations structurées et accessibles pour les systèmes informatiques. Ainsi, nous explorons les architectures de KG-RAG pour construire des agents conversationnels de confiance. Nous démontrons l'intérêt de notre approche pour un cas d'usage réel de support citoyen·ne en construisant un agent conversationnel traitant les mesures autour du handicap dans des villes françaises. Nous présentons d'abord un historique des agents conversationnels, en introduisant les méthodes mises en œuvre au fil des années et les techniques d'évaluation. Nous définissons ensuite les KG et les ontologies, et explorons les techniques de construction et d'évaluation. Ne trouvant pas de KG directement exploitable, notre première contribution introduit OLAF : Ontology Learning Applied Framework. Ce système modulaire est conçu pour une construction automatisée et reproductible de KG à partir de textes non structurés. OLAF intègre des techniques linguistiques, statistiques et basées sur des LLM pour générer des ontologies minimales viables sur des domaines spécifiques. Appliqué à des ensembles de données réels, OLAF démontre des performances robustes grâce à des évaluations basées sur des ontologies de référence et des questions de compétence spécifiques à une tâche. Nous détaillons le processus de construction d'un KG sur la thématique du handicap dans une ville française. Nous proposons ensuite une architecture pour les systèmes de KG-RAG afin d'améliorer la recherche d'information en alignant les requêtes des utilisateur·rice·s avec les structures des graphes via la liaison d'entités, les patrons de requêtes et les méthodes de récupération basées sur les LLM. Nous démontrons l'intérêt de notre architecture sur différents cas d'utilisation, que nous évaluons selon des critères tels que la performance, les préférences humaines et l'impact environnemental. Bien que les préférences des utilisateur·rice·s avantagent l'architecture de Text-RAG, l'impact environnemental réduit de l'architecture de KG-RAG souligne son potentiel pour des pratiques d'IA durables. Enfin, nous identifions comme élément clé de l'architecture la partie concernant la recherche d'information. Nous abordons donc cette tâche dans notre architecture en explorant les techniques de vectorisation dans divers contextes, c'est-à-dire en améliorant la liaison d'entités, la recherche des données contextuelles et en fournissant un système de cache. Nous proposons également des mécanismes pour gérer les conversations multi-tours. Ce travail établit un cadre complet pour les systèmes de KG-RAG, combinant la sémantique des KG avec les capacités génératives des LLM pour construire des agents conversationnels précis, spécialisés et durables. Les contributions incluent OLAF pour une construction automatisée de KG, un pipeline de KG-RAG robuste, et des améliorations basées sur des représentations vectorielles pour la précision de la recherche d'information et la qualité des interactions. En répondant aux défis industriels des agents conversationnels, ces travaux posent les bases du déploiement de systèmes de KG-RAG dans des domaines spécialisés et variés<br>Conversational agents have become widespread in recent years. Today, they have transcended their initial purpose of simulating a conversation with a computer program and are now valuable tools for accessing information and carrying out various tasks, from customer service to personal assistance. With the rise of text-generative models and Large Language Models (LLMs), the capabilities of conversational agents have increased tenfold. However, they are now subject to hallucinations, producing false information. A popular technique to limit the risk of hallucinations is Retrieval Augmented Generation (RAG), which injects knowledge into a text generation process. Such injected knowledge can be drawn from Knowledge Graphs (KGs), which are structured machine-readable knowledge representations. Therefore, we explore Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) to build trusted conversational agents. We demonstrate our approach on a real-world use case for citizen support by building conversational agents for disability management in cities. We first present a history of conversational agents, introducing the approaches implemented over the years and the evaluation techniques. We then define KGs and ontologies, and explore construction and evaluation techniques. As we could not find a directly exploitable KG, our first contribution introduces the Ontology Learning Applied Framework (OLAF). This modular system is built for automated and repeatable KG construction from unstructured text. OLAF integrates linguistic, statistical, and LLM-based techniques to generate Minimum Viable Ontologies for specific domains. Applied to real-world datasets, OLAF demonstrates robust performance through gold-standard evaluations and task-specific Competency Questions. We detail the construction process for a KG about disability management in a French city. We then propose an architecture for KG-RAG systems to enhance information retrieval by aligning user queries with KG structures through entity linking, graph queries, and LLM-based retrieval approaches. We demonstrate our architecture on different use cases, which we evaluate using criteria such as performance, human preference, and environmental impact. While user preferences advantage Text-RAG, KG-RAG's reduced computational footprint underscores its potential for sustainable AI practices. Finally, we identify the critical part of the architecture as the retriever. Therefore, we tackle the retrieval task in our architecture by exploring embeddings in various contexts, i.e. improving EL, retrieval, and providing a caching system. We also propose mechanisms for handling multi-turn conversations. This work establishes a comprehensive framework for KG-RAG systems, combining the semantic depth of KGs with the generative capabilities of LLMs to deliver accurate, contextual, and sustainable conversational agents. Contributions include OLAF for scalable KG construction, a robust KG-RAG pipeline, and embedding-based enhancements for retrieval and interaction quality. By addressing conversational agents' industrial challenges, such as scalability, retrieval precision, and conversational coherence, this research lays the foundation for deploying KG-RAG systems in diverse and specialised domains

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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 content of KGs led to the design and development of a number of new specialized data management systems. Although progress has been made around building efficient KG data management systems, developing high-performance systems continues to pose research challenges. In this research, we studied the efficiency of existing systems for storing and processing KG content. Our results pointed to performance inconsistencies in representative systems across diverse query types. We address this by introducing a polyglot model of KG query processing to analyze each query and match it to the best-performing available systems. Experimental evaluation highlighted that our proposed approach provides consistently high performance. Finally, we investigated leveraging emerging hardware and its benefits to RDF data management and performance. To this end, we introduced a novel index structure, RDFix, that utilizes Persistent Memory (PM) to outperform existing read-optimized indexes as shown experimentally.

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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 complet pour les PMEs, offrant des solutions dédiées pour l’ensemble des activités de GC, de l'acquisition et la représentation des connaissances à leur exploitation: (1) un processus d'acquisition de connaissances dédié basé sur le cadre Scrum, une méthodologie agile, (2) un modèle de représentation des connaissances dédié basé sur des graphes de connaissances semi-structurés, et (3) un processus d'exploitation des connaissances dédié basé sur le système de recommandation établi sur les liens entre les connaissances. Cette recherche a été menée en collaboration avec Axsens-bte, une PME spécialisée dans le conseil et la formation. Le partenariat avec Axsens-bte a fourni des retours précieux et des expériences pratiques, contribuant au développement du cadre de GC proposé et soulignant sa pertinence et son applicabilité dans des contextes réels de PME<br>Knowledge is vital for organizations, particularly in today’s Industry 4.0 context. Knowledge Management (KM) plays a critical role in an organization's success. Although KM has been relatively well-studied in large organizations, Small and Medium-sized Enterprises (SMEs) receive less attention. SMEs face unique challenges in KM, requiring a tailored KM framework. Our study aims to define a framework addressing their challenges while leveraging their inherent strengths. This thesis presents a dedicated and comprehensive SME KM framework, offering dedicated solutions from knowledge acquisition and representation to exploitation: (1) a dedicated knowledge acquisition process based on the Scrum framework, an agile methodology, (2) a dedicated knowledge representation model based on semi-structured KG, and (3) a dedicated knowledge exploitation process based on knowledge-relatedness RS. This research was conducted in collaboration with Axsens-bte, an SME specializing in consultancy and training. The partnership with Axsens-bte has provided invaluable insights and practical experiences, contributing to developing the proposed KM framework and highlighting its relevance and applicability in real-world SME contexts

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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 line of research, KG embedding methods (KGEs) have been proposed to embed the entities and relations in the KG in low-dimensional vector space. These methods aim to learn representations that can be then utilized by various scoring functions to predict the plausibility of triples (facts) in the KG. Applications of KG embeddings include link prediction and KG completion. Such KG embedding methods, even though highly relevant, have not been explored for KGQA so far. In this work, we focus on 2 aspects of KGQA: (i) Temporal reasoning, and (ii) KG incompleteness. Here, we leverage recent advances in KG embeddings to improve model reasoning in the temporal domain, as well as use the robustness of embeddings to KG sparsity to improve incomplete KG question answering performance. We do this through the following contributions: Improving Multi-Hop KGQA using KG Embeddings We first tackle a subset of KGQA queries – multi-hop KGQA. We propose EmbedKGQA, a method which uses ComplEx embeddings and scoring function to answer these queries. We find that EmbedKGQA is particularly effective at KGQA over sparse KGs, while it also relaxes the requirement of answer selection from a pre-specified local neighborhood, an undesirable constraint imposed by GNN-based for this task. Experiments show that EmbedKGQA is superior to several GNN-based methods on incomplete KGs across a variety of dataset scales. Question Answering over Temporal Knowledge Graphs We then extend our method to temporal knowledge graphs (TKG), where each edge in the KG is accompanied by a time scope (i.e. start and end times). Here, instead of KGEs, we make use of temporal KGEs (TKGE) to enable the model to make use of these time annotations and perform temporal reasoning. We also propose a new dataset - CronQuestions - which is one of the largest publicly available temporal KGQA dataset with over 400k template-based temporal reasoning questions. Through extensive experiments we show the superiority of our method, CronKGQA, over several language-model baselines on the challenging task of temporal KGQA on CronQuestions. Sequence-to-Sequence Knowledge Graph Completion and Question Answering So far, integrating KGE into the KGQA pipeline had required separate training of the KGE and KGQA modules. In this work, we show that an off-the-shelf encoder-decoder Transformer model can serve as a scalable and versatile KGE model obtaining state-of-the-art results for KG link prediction and incomplete KG question answering. We achieve this by posing KG link prediction as a sequence-to-sequence task and exchange the triple scoring approach taken by prior KGE methods with autoregressive decoding. Such a simple but powerful method reduces the model size up to 98% compared to conventional KGE models while keeping inference time tractable. It also allows us to answer a variety of KGQA queries, not being restricted by query type.

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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 non-trivial task to distinguish workers (who contribute positively) from idlers (who do not contribute to group task) among the participants using only group's performance. The first part of this thesis studies the problem of distinguishing workers from idlers, without assuming any prior knowledge of individual skills and considers \groups" as the smallest observable unit for evaluation. We draw upon literature from group-testing and give bounds over minimum number of groups required to identify quality of subsets of individuals with high confidence. We validate our theory experimentally and report insights for the number of workers and idlers that can be identified for a given number of group-tasks with significant probability. In most crowdsourcing applications, there exist dependencies among the pool of Human Intelligence Tasks (HITs) and often in practical scenarios there are far too many HITs available than what can realistically be covered by limited available budget. Estimating the accuracy of automatically constructed Knowledge Graphs (KG) is one such important application. Automatic construction of large knowledge graphs has gained wide popularity in recent times. These KGs, such as NELL, Google Knowledge Vault, etc., consist of thousands of predicate-relations (e.g., is Person, is Mayor Of) and millions of their instances (e.g., (Bill de Blasio, is Mayor Of, New York City)). Estimating accuracy of such KGs is a challenging problem due to their size and diversity. In the second part of this study, we show that standard single-task crowdsourc- ing is sub-optimal and very expensive as it ignores dependencies among various predicates and instances. We propose Relational Crowdsourcing (RelCrowd) to overcome this challenge, where the tasks are created while taking dependencies among predicates and instances into account. We apply this framework in the context of large-scale Knowledge Graph Evaluation (KGEval) and demonstrate its effectiveness through extensive experiments on real-world datasets.

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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, for any input dataset. Furthermore, we provide a method that can verify that certain classes of Datalog rules are not sound for the R-GCN. In our experiments, we train R-GCNs on KG completion benchmarks, and we are able to verify that no Datalog rule is sound for these models, even though the models often obtain high to near-perfect accuracy. This raises some concerns about the ability of R-GCN models to generalise and about the explainability of their predictions. We further provide two variations to the training paradigm of R-GCN that encourage it to learn sound rules and find a trade-off between model accuracy and the number of learned sound rules.

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