Relevant bibliographies by topics / Knowledge Graph (KG)

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

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Published: 7 July 2024

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

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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 (March 2, 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 (August 10, 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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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 (November 11, 2019): 250–59. http://dx.doi.org/10.1007/s42486-019-00020-3.

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Fang, Yin, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan, and Huajun Chen. "Molecular Contrastive Learning with Chemical Element Knowledge Graph." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 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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Tong, Peihao, Qifan Zhang, and Junjie Yao. "Leveraging Domain Context for Question Answering Over Knowledge Graph." Data Science and Engineering 4, no. 4 (November 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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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 (April 8, 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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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 (May 18, 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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Kejriwal, Mayank. "Knowledge Graphs: A Practical Review of the Research Landscape." Information 13, no. 4 (March 23, 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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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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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 (August 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 an input graph database with intensional definitions of new nodes and edges and turn it into a KG, via the metaphor of reasoning widgets---user-defined or off-the-shelf code snippets that capture business definitions in the Vadalog language. Then, the user can seamlessly browse the original and the derived nodes and edges within a "Virtual Knowledge Graph", which is reasoned upon and generated interactively at runtime, thanks to the scalability and responsiveness of Vadalog. KG-Roar is domain-independent but domain aware, as exploration controls are contextually generated based on the intensional definitions. We walk the audience through KG-Roar showcasing the construction of certain business definitions and putting it into action on a real-world financial KG, from our work with the Bank of Italy.
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Dissertations / Theses on the topic "Knowledge Graph (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 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
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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Book chapters on the topic "Knowledge Graph (KG)"

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Krause, Franz, Kabul Kurniawan, Elmar Kiesling, Jorge Martinez-Gil, Thomas Hoch, Mario Pichler, Bernhard Heinzl, and Bernhard Moser. "Leveraging Semantic Representations via Knowledge Graph Embeddings." In Artificial Intelligence in Manufacturing, 71–85. Cham: 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, 628–42. Cham: 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, Lei Zhang, Chuanqi Dong, He Liu, and Du Zhang. "KG-Buddhism: The Chinese Knowledge Graph on Buddhism." In Semantic Technology, 259–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70682-5_17.

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Möller, Cedric. "Knowledge Graph Population with Out-of-KG Entities." In The Semantic Web: ESWC 2022 Satellite Events, 199–214. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11609-4_35.

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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, 56–70. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23515-3_5.

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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, 481–97. Cham: 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, 129–37. Cham: 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, Norman Radtke, Kurt Junghanns, Roy Meissner, Gordian Dziwis, Kirill Bulert, and Michael Martin. "LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT." In Informatik aktuell, 103–15. Wiesbaden: 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, 127–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62466-8_9.

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Anand, Avinash, Mohit Gupta, Kritarth Prasad, Ujjwal Goel, Naman Lal, Astha Verma, and Rajiv Ratn Shah. "KG-CTG: Citation Generation Through Knowledge Graph-Guided Large Language Models." In Big Data and Artificial Intelligence, 37–49. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49601-1_3.

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Conference papers on the topic "Knowledge Graph (KG)"

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Wei, Xing, and Jiangjiang Liu. "Effects of Nonlinear Functions on Knowledge Graph Convolutional Networks for Recommender Systems with Yelp Knowledge Graph." In 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110715.

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Knowledge Graph (KG) related recommendation method is advanced in dealing with cold start problems and sparse data. Knowledge Graph Convolutional Network (KGCN) is an end-to-end framework that has been proved to have the ability to capture latent item-entity features by mining their associated attributes on the KG. In KGCN, aggregator plays a key role for extracting information from the high-order structure. In this work, we proposed Knowledge Graph Processor (KGP) for pre-processing data and building corresponding knowledge graphs. A knowledge graph for the Yelp Open dataset was constructed with KGP. In addition, we investigated the impacts of various aggregators with three nonlinear functions on KGCN with Yelp Open dataset KG.
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Ristoski, Petar, Zhizhong Lin, and Qunzhi Zhou. "KG-ZESHEL: Knowledge Graph-Enhanced Zero-Shot Entity Linking." In K-CAP '21: Knowledge Capture Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460210.3493549.

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Chen, Mingyang, Wen Zhang, Zhen Yao, Xiangnan Chen, Mengxiao Ding, Fei Huang, and Huajun Chen. "Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/273.

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We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve this problem, we introduce the meta-learning setting, where a set of tasks are sampled on the existing KG to mimic the link prediction task on the emerging KG. Based on sampled tasks, we meta-train a graph neural network framework that can construct features for unseen components based on structural information and output embeddings for them. Experimental results show that our proposed method can effectively embed unseen components and outperforms models that consider inductive settings for KGs and baselines that directly use conventional KG embedding methods.
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Wei, Jiaqi, Shuo Han, and Lei Zou. "VISION-KG: Topic-centric Visualization System for Summarizing Knowledge Graph." In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3336191.3371863.

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Qiu, Yuchen, Yuanyuan Qiao, Shuo Yang, and Jie Yang. "Tax-KG: Taxation Big Data Visualization System for Knowledge Graph." In 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). IEEE, 2020. http://dx.doi.org/10.1109/icsip49896.2020.9339403.

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Cai, Jinglun, Mingda Li, Ziyan Jiang, Eunah Cho, Zheng Chen, Yang Liu, Xing Fan, and Chenlei Guo. "KG-ECO: Knowledge Graph Enhanced Entity Correction For Query Rewriting." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096826.

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Liu, Shuwen, Bernardo Cuenca Grau, Ian Horrocks, and Egor V. Kostylev. "Revisiting Inferential Benchmarks for Knowledge Graph Completion." In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/45.

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Knowledge Graph (KG) completion is the problem of extending an incomplete KG with missing facts. A key feature of Machine Learning approaches for KG completion is their ability to learn inference patterns, so that the predicted facts are the results of applying these patterns to the KG. Standard completion benchmarks, however, are not well-suited for evaluating models' abilities to learn patterns, because the training and test sets of these benchmarks are a random split of a given KG and hence do not capture the causality of inference patterns. We propose a novel approach for designing KG completion benchmarks based on the following principles: there is a set of logical rules so that the missing facts are the results of the rules' application; the training set includes both premises matching rule antecedents and the corresponding conclusions; the test set consists of the results of applying the rules to the training set; the negative examples are designed to discourage the models from learning rules not entailed by the rule set. We use our methodology to generate several benchmarks and evaluate a wide range of existing KG completion systems. Our results provide novel insights on the ability of existing models to induce inference patterns from incomplete KGs.
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Huang, Yu-Xuan, Zequn Sun, Guangyao Li, Xiaobin Tian, Wang-Zhou Dai, Wei Hu, Yuan Jiang, and Zhi-Hua Zhou. "Enabling Abductive Learning to Exploit Knowledge Graph." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/427.

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Most systems integrating data-driven machine learning with knowledge-driven reasoning usually rely on a specifically designed knowledge base to enable efficient symbolic inference. However, it could be cumbersome for the nonexpert end-users to prepare such a knowledge base in real tasks. Recent years have witnessed the success of large-scale knowledge graphs, which could be ideal domain knowledge resources for real-world machine learning tasks. However, these large-scale knowledge graphs usually contain much information that is irrelevant to a specific learning task. Moreover, they often contain a certain degree of noise. Existing methods can hardly make use of them because the large-scale probabilistic logical inference is usually intractable. To address these problems, we present ABductive Learning with Knowledge Graph (ABL-KG) that can automatically mine logic rules from knowledge graphs during learning, using a knowledge forgetting mechanism for filtering out irrelevant information. Meanwhile, these rules can form a logic program that enables efficient joint optimization of the machine learning model and logic inference within the Abductive Learning (ABL) framework. Experiments on four different tasks show that ABL-KG can automatically extract useful rules from large-scale and noisy knowledge graphs, and significantly improve the performance of machine learning with only a handful of labeled data.
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Wu, Zhanglin, Min Zhang, Ming Zhu, Yinglu Li, Ting Zhu, Hao Yang, Song Peng, and Ying Qin. "KG-BERTScore: Incorporating Knowledge Graph into BERTScore for Reference-Free Machine Translation Evaluation." In IJCKG 2022: 11th International Joint Conference On Knowledge Graphs. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3579051.3579065.

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Tu, Yamei, Rui Qiu, and Han-Wei Shen. "KG-PRE-view: Democratizing a TVCG Knowledge Graph through Visual Explorations." In 2024 IEEE 17th Pacific Visualization Conference (PacificVis). IEEE, 2024. http://dx.doi.org/10.1109/pacificvis60374.2024.00026.

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