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Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into three fundamental paradigms: KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks. The evaluation examines each paradigm’s methodology, strengths, drawbacks, and practical applications in real-life scenarios. The findings highlight the substantial impact of these integrations in fundamentally improving real-time data analysis, efficient decision-making, and promoting innovation across various domains. In this paper, we also describe essential evaluation metrics and benchmarks for assessing the performance of these integrations, addressing challenges like scalability and computational overhead, and providing potential solutions. This comprehensive analysis underscores the profound impact of these integrations on improving real-time data analysis, enhancing decision-making efficiency, and fostering innovation across various domains.

This paper examines the integration of large language models (LLMs) with knowledge graphs (KGs) through a systematic four-layer framework that includes data, model, technology, and application dimensions. We analyze the capabilities and limitations of LLMs in natural language processing along with the strengths and challenges of KGs in knowledge representation. We address fundamental weaknesses in each approach and identify complementary integration methods. Our analysis reveals that LLMs excel at contextual understanding and generation but struggle with factual consistency and reasoning transparency. In contrast, KGs provide structured and verifiable knowledge but lack adaptability to unstructured inputs. We review integration strategies, including knowledge injection techniques, retrieval-augmented generation, and neuro-symbolic approaches. The combined methods demonstrate significant performance improvements. Through the case study of the GLM architecture, we demonstrate how the integration of KGs and LLMs improves accuracy, interpretability, and factual grounding in specialized domains and also shows substantial performance improvements in knowledge-intensive tasks (15–20% on MedQA and 14–17% on the medical MMLU benchmarks). The resulting hybrid systems offer concrete advantages in critical applications requiring precision and adaptability, including healthcare diagnostics, financial compliance, and educational technology. Lightweight knowledge representation, adaptive update mechanisms, and unified cross-modal frameworks are promising research directions to advance KG–LLM integration.

Large-scale knowledge graphs (KGs) play a critical role in question answering over KGs (KGs-QA). Despite of large scale, KGs suffer from incompleteness, which has fueled a lot of research on relation prediction. Since existing researches of relation prediction process each triple independently, the hidden relations which are inherently present can not be captured. Complementarily, to simultaneously capture both entity features and relation features in a given entity’s neighborhood, an entity importance estimation network of attention-based graph embedding is proposed, which consists of the attention-based graph embedding module and the entity importance estimation module. Firstly, the new embedding of an entity from its n-hop neighbor is learned by an attention-based graph embedding module. Then, the learned new embedding is integrated into the entity importance estimation module to find entities of high importance in n-hop neighbors of the central entity. Finally, multi-hop relations are encapsulated and an auxiliary edge of n-hop neighbors is introduced, which realizes the relation prediction task. To the best our knowledge, we are the first to realize KGs-QA while realizing relation prediction, which alleviates the phenomenon of missing relations and the low-precision problem of KGs-QA. On the SQ datasets, the proposed method obtains a high F1 score (49.3%) in 10% missing relation, compared to QASE and MCCNNs with F1 scores of 44.2% and 46.3%, respectively.

Efforts to enrich Knowledge Graphs (KGs) typically seek to augment data quality, semantic comprehension, and functional capabilities via the integration of various data sources. However, the inherent evolution of these sources over time potentially compromises the quality of the KGs. This paper provides a systematic exploration of the temporal challenges intrinsic to the progression of KGs, including the dynamics of changes, anomaly detection, the estimation of repair costs, and the delicate balance between changes and consistency. The complexities associated with the accurate representation of time in KGs are addressed, providing a critical assessment and understanding of this issue. A correction framework, bolstered by temporal considerations, is proposed, with an intent to scrutinize these techniques using various datasets in future research endeavors. This work represents a step forward in comprehending the quality of KGs by delving into their temporal aspects.

Traditional craft education relies heavily on hands-on practice; however, novice learners often struggle with procedural complexity, material behavior, and the tacit knowledge typically transmitted through prolonged apprenticeship. This paper presents an integrated framework that combines semantic Knowledge Graphs (KGs), real-time Finite Element Method (FEM) simulation, and high-fidelity physically based rendering (PBR) to support the teaching, understanding, and preservation of traditional crafts. Craft processes are modelled as ontologically grounded KGs that capture tools, materials, actions, decision points, and common procedural errors through an extensible representation aligned with CIDOC-CRM. These semantic structures drive an interactive FEM-based simulation that enables learners to enact craft actions in a virtual environment while receiving predictive feedback and corrective guidance derived from expert-defined execution parameters. The resulting workpiece states are visualized using PBR techniques, providing perceptually accurate cues essential for assessing surface changes, deformation patterns, and material conditions. The methodology is embedded within an eLearning ecosystem that supports the generation of structured courses, multimodal exemplars, and instructional design informed by Cognitive Load Theory. A use case involving wood and aluminum carving demonstrates the system’s ability to simulate realistic tool–material interactions and produce visually interpretable outcomes. The results indicate that coupling executable semantic knowledge modelling with physically grounded simulation offers a viable pathway toward scalable, safe, and contextually rich craft training while supporting the long-term preservation of domain expertise.

Human beings have the ability to quickly recognize novel concepts with the help of scene semantics. This kind of ability is meaningful and full of challenge for the field of machine learning. At present, object recognition methods based on deep learning have achieved excellent results with the use of large-scale labeled data. However, the data scarcity of novel objects significantly affects the performance of these recognition methods. In this work, we investigated utilizing knowledge reasoning with visual information in the training of a novel object detector. We trained a detector to project the image representations of objects into an embedding space. Knowledge subgraphs were extracted to describe the semantic relation of the specified visual scenes. The spatial relationship, function relationship, and the attribute description were defined to realize the reasoning of novel classes. The designed few-shot detector, named KR-FSD, is robust and stable to the variation of shots of novel objects, and it also has advantages when detecting objects in a complex environment due to the flexible extensibility of KGs. Experiments on VOC and COCO datasets showed that the performance of the detector was increased significantly when the novel class was strongly associated with some of the base classes, due to the better knowledge propagation between the novel class and the related groups of classes.

In the era of \"big data,\" science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that is of particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use.After defining an ontology as a representation of the types of entities in a given domain, the book distinguishes between different kinds of ontologies and taxonomies, and shows how applied ontology draws on more traditional ideas from metaphysics. It presents the core features of the Basic Formal Ontology (BFO), now used by over one hundred ontology projects around the world, and offers examples of domain ontologies that utilize BFO. The book also describes Web Ontology Language (OWL), a common framework for Semantic Web technologies. Throughout, the book provides concrete recommendations for the design and construction of domain ontologies.

The practice of engineering is continually changing. Engineers today must be able not only to thrive in an environment of rapid technological change and globalization, but also to work on interdisciplinary teams. Cutting-edge research is being done at the intersections of engineering disciplines, and successful researchers and practitioners must be aware of developments and challenges in areas that may not be familiar to them. At the U.S. Frontiers of Engineer Symposium, engineers have the opportunity to learn from their peers about pioneering work being done in many areas of engineering. Frontiers of Engineering 2011: Reports on Leading-Edge Engineering from the 2011 Symposium highlights the papers presented at the event. This book covers four general topics from the 2011 symposium: additive manufacturing, semantic processing, engineering sustainable buildings, and neuro-prosthetics. The papers from these presentations provide an overview of the challenges and opportunities of these fields of inquiry, and communicate the excitement of discovery.

The U.S. military has committed to a strategy of network-centric warfare. As a result, the Army has become increasingly interested in the critical role of network science. To a significant extent, this interest was stimulated by an earlier NRC report, Network Science. To build on that book, the Army asked the NRC to conduct a study to define advanced operating models and architectures for future Army laboratories and centers focused on network science, technologies, and experimentation (NSTE). The challenges resulting from base realignment and closure (BRAC) relocations of Army research, development, and engineering resources-as they affected the NSTE program-were also to be a focus of the study. This book provides a discussion of what NSTE is needed by the Army; an examination of the NSTE currently carried out by the Army; an assessment of needed infrastructure resources for Army NSTE; and an analysis of goals, models, and alternatives for an NSTE center.