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

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Bellomarini, Luigi, Marco Benedetti, Andrea Gentili, Davide Magnanimi, and Emanuel Sallinger. "KG-Roar: Interactive Datalog-Based Reasoning on Virtual Knowledge Graphs." Proceedings of the VLDB Endowment 16, no. 12 (2023): 4014–17. http://dx.doi.org/10.14778/3611540.3611609.

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Logic-based Knowledge Graphs (KGs) are gaining momentum in academia and industry thanks to the rise of expressive and efficient languages for Knowledge Representation and Reasoning (KRR). These languages accurately express business rules, through which valuable new knowledge is derived. A versatile and scalable backend reasoner, like Vadalog, a state-of-the-art system for logic-based KGs---based on an extension of Datalog---executes the reasoning. In this demo, we present KG-Roar, a web-based interactive development and navigation environment for logical KGs. The system lets the user augment a

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Monka, Sebastian, Lavdim Halilaj, and Achim Rettinger. "A survey on visual transfer learning using knowledge graphs." Semantic Web 13, no. 3 (2022): 477–510. http://dx.doi.org/10.3233/sw-212959.

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The information perceived via visual observations of real-world phenomena is unstructured and complex. Computer vision (CV) is the field of research that attempts to make use of that information. Recent approaches of CV utilize deep learning (DL) methods as they perform quite well if training and testing domains follow the same underlying data distribution. However, it has been shown that minor variations in the images that occur when these methods are used in the real world can lead to unpredictable and catastrophic errors. Transfer learning is the area of machine learning that tries to preve

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Cao, Keyan, and Chuang Zheng. "TBRm: A Time Representation Method for Industrial Knowledge Graph." Applied Sciences 12, no. 22 (2022): 11316. http://dx.doi.org/10.3390/app122211316.

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With the development of the artificial intelligence industry, Knowledge Graph (KG), as a concise and intuitive data presentation form, has received extensive attention and research from both academia and industry in recent years. At the same time, developments in the Internet of Things (IoT) have empowered modern industries to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). Using knowledge graphs (KG) to process data from the Industrial Internet of Things (IIoT) is a research field worthy of attention, but most of the researched knowledge graph technolog

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Wang, Yu, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, and Tyler Derr. "Knowledge Graph Prompting for Multi-Document Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19206–14. http://dx.doi.org/10.1609/aaai.v38i17.29889.

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The `pre-train, prompt, predict' paradigm of large language models (LLMs) has achieved remarkable success in open-domain question answering (OD-QA). However, few works explore this paradigm in multi-document question answering (MD-QA), a task demanding a thorough understanding of the logical associations among the contents and structures of documents. To fill this crucial gap, we propose a Knowledge Graph Prompting (KGP) method to formulate the right context in prompting LLMs for MD-QA, which consists of a graph construction module and a graph traversal module. For graph construction, we creat

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Liu, Ye, Yao Wan, Lifang He, Hao Peng, and Philip S. Yu. "KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (2021): 6418–25. http://dx.doi.org/10.1609/aaai.v35i7.16796.

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Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-tr

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Zhang, Peng, Yi Bu, Peng Jiang, et al. "Toward a Coronavirus Knowledge Graph." Genes 12, no. 7 (2021): 998. http://dx.doi.org/10.3390/genes12070998.

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This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 gen

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Yang, Zhenyu, Lei Wu, Peian Wen, and Peng Chen. "Visual Question Answering reasoning with external knowledge based on bimodal graph neural network." Electronic Research Archive 31, no. 4 (2023): 1948–65. http://dx.doi.org/10.3934/era.2023100.

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<abstract><p>Visual Question Answering (VQA) with external knowledge requires external knowledge and visual content to answer questions about images. The defect of existing VQA solutions is that they need to identify task-related information in the obtained pictures, questions, and knowledge graphs. It is necessary to properly fuse and embed the information between different modes identified, to reduce the noise and difficulty in cross-modality reasoning of VQA models. However, this process of rationally integrating information between different modes and joint reasoning to find re

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Wang, Meihong, Linling Qiu, and Xiaoli Wang. "A Survey on Knowledge Graph Embeddings for Link Prediction." Symmetry 13, no. 3 (2021): 485. http://dx.doi.org/10.3390/sym13030485.

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Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform

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Yang, Tong, Yifei Wang, Long Sha, Jan Engelbrecht, and Pengyu Hong. "Knowledgebra: An Algebraic Learning Framework for Knowledge Graph." Machine Learning and Knowledge Extraction 4, no. 2 (2022): 432–45. http://dx.doi.org/10.3390/make4020019.

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Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consistently represented. Dense embeddings trained from KG datasets benefit a variety of downstream tasks such as KG completion and link prediction. However, existing KG embedding methods fell short to provide a systematic solution for the global consistency of knowledge representation. We developed a mathematical language for KG based on an observation of their inherent algebraic structure, which we termed as Knowledgebra. B

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Ni, Bo, Yu Wang, Lu Cheng, Erik Blasch, and Tyler Derr. "Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12417–25. https://doi.org/10.1609/aaai.v39i12.33353.

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Recently, Knowledge Graphs (KGs) have been successfully coupled with Large Language Models (LLMs) to mitigate their hallucinations and enhance their reasoning capability, e.g., KG-based retrieval-augmented framework. However, current KG-LLM frameworks lack rigorous uncertainty estimation, limiting their reliable deployment in applications where the cost of errors is significant. Directly incorporating uncertainty quantification into KG-LLM frameworks presents a challenge due to their more complex architectures and the intricate interactions between the knowledge graph and language model compon

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Garifo, Giovanni, Giuseppe Futia, Antonio Vetrò, and Juan Carlos De Martin. "The Geranium Platform: A KG-Based System for Academic Publications." Information 12, no. 9 (2021): 366. http://dx.doi.org/10.3390/info12090366.

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Knowledge Graphs (KGs) have emerged as a core technology for incorporating human knowledge because of their capability to capture the relational dimension of information and of its semantic properties. The nature of KGs meets one of the vocational pursuits of academic institutions, which is sharing their intellectual output, especially publications. In this paper, we describe and make available the Polito Knowledge Graph (PKG) –which semantically connects information on more than 23,000 publications and 34,000 authors– and Geranium, a semantic platform that leverages the properties of the PKG

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Goel, Rishab, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. "Diachronic Embedding for Temporal Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3988–95. http://dx.doi.org/10.1609/aaai.v34i04.5815.

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Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones–a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embeddi

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Shi, Xiujin, Jun Hu, Naiwen Sun, and Shoujian Yu. "TrEKBQA:Traversing Knowledge Graph Embedding for Multi-hop Knowledge Base Question Answering." Journal of Physics: Conference Series 2424, no. 1 (2023): 012027. http://dx.doi.org/10.1088/1742-6596/2424/1/012027.

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Abstract Recent research apply KG embedding to multi-hop Knowledge Base Question Answering(KBQA) to predict missing links, however, it is often affected by the skewed distribution of nodes in the knowledge graph, resulting in poor generalization of the model. Therefore, we propose a method TrEKBQA based on traversing the knowledge graph embedding space for multi-hop KBQA, which performs path traversal in the KG embedding space instead of KG itself for link prediction to complete the knowledge graph, thus improving the accuracy of multi-hop KBQA.TrEKBQA model complex relationships using correla

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Zhang, Chuxu, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, and Nitesh V. Chawla. "Few-Shot Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 3041–48. http://dx.doi.org/10.1609/aaai.v34i03.5698.

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Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instances (i.e., head-tail entity pairs) for every relation. The real case is that for most of the relations, very few entity pairs are available. Existing work of one-shot learning limits method generalizability for few-shot scenarios and does not fully use the supervisory information; however, few-shot KG completion has not been well studied yet. In this work, we propose a novel few-shot relation learning model (FSRL) that

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Liu, Rui, Rong Fu, Kang Xu, Xuanzhe Shi, and Xiaoning Ren. "A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems." Applied Sciences 13, no. 7 (2023): 4357. http://dx.doi.org/10.3390/app13074357.

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Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize th

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Wang, Kai, Yuwei Xu, and Siqiang Luo. "TIGER: Training Inductive Graph Neural Network for Large-Scale Knowledge Graph Reasoning." Proceedings of the VLDB Endowment 17, no. 10 (2024): 2459–72. http://dx.doi.org/10.14778/3675034.3675039.

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Knowledge Graph (KG) Reasoning plays a vital role in various applications by predicting missing facts from existing knowledge. Inductive KG reasoning approaches based on Graph Neural Networks (GNNs) have shown impressive performance, particularly when reasoning with unseen entities and dynamic KGs. However, such state-of-the-art KG reasoning approaches encounter efficiency and scalability challenges on large-scale KGs due to the high computational costs associated with subgraph extraction - a key component in inductive KG reasoning. To address the computational challenge, we introduce TIGER, a

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Choi, Seungmin, and Yuchul Jung. "Knowledge Graph Construction: Extraction, Learning, and Evaluation." Applied Sciences 15, no. 7 (2025): 3727. https://doi.org/10.3390/app15073727.

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A Knowledge Graph (KG), which structurally represents entities (nodes) and relationships (edges), offers a powerful and flexible approach to knowledge representation in the field of Artificial Intelligence (AI). KGs have been increasingly applied in various domains—such as natural language processing (NLP), recommendation systems, knowledge search, and medical diagnostics—spurring continuous research on effective methods for their construction and maintenance. Recently, efforts to combine large language models (LLMs), particularly those aimed at managing hallucination symptoms, with KGs have g

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Gao, Zhenxiang, Pingjian Ding, and Rong Xu. "KG-Predict: A knowledge graph computational framework for drug repurposing." Journal of Biomedical Informatics 132 (August 2022): 104133. http://dx.doi.org/10.1016/j.jbi.2022.104133.

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Gu, Tianlong, Haohong Liang, Chenzhong Bin, and Liang Chang. "Combining user-end and item-end knowledge graph learning for personalized recommendation." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 9213–25. http://dx.doi.org/10.3233/jifs-201635.

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How to accurately model user preferences based on historical user behaviour and auxiliary information is of great importance in personalized recommendation tasks. Among all types of auxiliary information, knowledge graphs (KGs) are an emerging type of auxiliary information with nodes and edges that contain rich structural information and semantic information. Many studies prove that incorporating KG into personalized recommendation tasks can effectively improve the performance, rationality and interpretability of recommendations. However, existing methods either explore the independent meta-pa

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Yang, Xu, Ziyi Huan, Yisong Zhai, and Ting Lin. "Research of Personalized Recommendation Technology Based on Knowledge Graphs." Applied Sciences 11, no. 15 (2021): 7104. http://dx.doi.org/10.3390/app11157104.

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Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algor

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Yuan, Xu, Jiaxi Chen, Yingbo Wang, et al. "Semantic-Enhanced Knowledge Graph Completion." Mathematics 12, no. 3 (2024): 450. http://dx.doi.org/10.3390/math12030450.

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Knowledge graphs (KGs) serve as structured representations of knowledge, comprising entities and relations. KGs are inherently incomplete, sparse, and have a strong need for completion. Although many knowledge graph embedding models have been designed for knowledge graph completion, they predominantly focus on capturing observable correlations between entities. Due to the sparsity of KGs, potential semantic correlations are challenging to capture. To tackle this problem, we propose a model entitled semantic-enhanced knowledge graph completion (SE-KGC). SE-KGC effectively addresses the issue by

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Li, Tongxin, Xiaobo Li, Fei Wang, Weiping Wang, and Tao Wang. "Confidence Prediction Based on Uncertain Knowledge Graph Structure Embedding." Journal of Physics: Conference Series 2833, no. 1 (2024): 012001. http://dx.doi.org/10.1088/1742-6596/2833/1/012001.

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Abstract The development of large-scale knowledge graphs (KGs) has given rise to uncertain relational facts, leading to research on uncertain knowledge graph (KG) embeddings. While various studies have been conducted on the task of uncertain KG embeddings, they often employ simplistic scoring functions based on the internal interaction information among triplets to fit confidence scores, neglecting the rich neighborhood information. In light of this, we propose a novel model UKGSE for uncertain KG embeddings that captures the subgraph structural features formed by the neighbors of triplets, ai

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Tian, Shiyu, Shuyue Xing, Xingrui Li, et al. "A Systematic Exploration of Knowledge Graph Alignment with Large Language Models in Retrieval Augmented Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25291–99. https://doi.org/10.1609/aaai.v39i24.34716.

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Retrieval Augmented Generation (RAG) with Knowledge Graphs (KGs) is an effective way to enhance Large Language Models (LLMs). Due to the natural discrepancy between structured KGs and sequential LLMs, KGs must be linearized to text before being inputted into LLMs, leading to the problem of KG Alignment with LLMs (KGA). However, recent KG+RAG methods only consider KGA as a simple step without comprehensive and in-depth explorations, leaving three essential problems unclear: (1) What are the factors and their effects in KGA? (2) How do LLMs understand KGs? (3) How to improve KG+RAG by KGA? To fi

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Zhu, Yueqin, Wenwen Zhou, Yang Xu, Ji Liu, and Yongjie Tan. "Intelligent Learning for Knowledge Graph towards Geological Data." Scientific Programming 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/5072427.

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Knowledge graph (KG) as a popular semantic network has been widely used. It provides an effective way to describe semantic entities and their relationships by extending ontology in the entity level. This article focuses on the application of KG in the traditional geological field and proposes a novel method to construct KG. On the basis of natural language processing (NLP) and data mining (DM) algorithms, we analyze those key technologies for designing a KG towards geological data, including geological knowledge extraction and semantic association. Through this typical geological ontology extr

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Zhao, Ruilin, Feng Zhao, Liang Hu, and Guandong Xu. "Graph Reasoning Transformers for Knowledge-Aware Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19652–60. http://dx.doi.org/10.1609/aaai.v38i17.29938.

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Augmenting Language Models (LMs) with structured knowledge graphs (KGs) aims to leverage structured world knowledge to enhance the capability of LMs to complete knowledge-intensive tasks. However, existing methods are unable to effectively utilize the structured knowledge in a KG due to their inability to capture the rich relational semantics of knowledge triplets. Moreover, the modality gap between natural language text and KGs has become a challenging obstacle when aligning and fusing cross-modal information. To address these challenges, we propose a novel knowledge-augmented question answer

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Yu, Donghan, Chenguang Zhu, Yiming Yang, and Michael Zeng. "JAKET: Joint Pre-training of Knowledge Graph and Language Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11630–38. http://dx.doi.org/10.1609/aaai.v36i10.21417.

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Knowledge graphs (KGs) contain rich information about world knowledge, entities, and relations. Thus, they can be great supplements to existing pre-trained language models. However, it remains a challenge to efficiently integrate information from KG into language modeling. And the understanding of a knowledge graph requires related context. We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. The knowledge module and language module provide essential information to mutually assist each other: the knowledge module produces embeddings for entiti

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Morton, Kenneth, Patrick Wang, Chris Bizon, et al. "ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering." Bioinformatics 35, no. 24 (2019): 5382–84. http://dx.doi.org/10.1093/bioinformatics/btz604.

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Abstract Summary Knowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase

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Chen, Kai, Guohua Shen, Zhiqiu Huang, and Haijuan Wang. "Improved Entity Linking for Simple Question Answering Over Knowledge Graph." International Journal of Software Engineering and Knowledge Engineering 31, no. 01 (2021): 55–80. http://dx.doi.org/10.1142/s0218194021400039.

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Question Answering systems over Knowledge Graphs (KG) answer natural language questions using facts contained in a knowledge graph, and Simple Question Answering over Knowledge Graphs (KG-SimpleQA) means that the question can be answered by a single fact. Entity linking, which is a core component of KG-SimpleQA, detects the entities mentioned in questions, and links them to the actual entity in KG. However, traditional methods ignore some information of entities, especially entity types, which leads to the emergence of entity ambiguity problem. Besides, entity linking suffers from out-of-vocab

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Toroghi, Armin, and Scott Sanner. "Bayesian Inference with Complex Knowledge Graph Evidence." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 20550–58. http://dx.doi.org/10.1609/aaai.v38i18.30040.

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Knowledge Graphs (KGs) provide a widely used format for representing entities and their relationships and have found use in diverse applications including question answering and recommendation. A majority of current research on KG inference has focused on reasoning with atomic facts (triples) and has disregarded the possibility of making complex evidential observations involving logical operators (negation, conjunction, disjunction) and quantifiers (existential, universal). Further, while the application of complex evidence has been explored in KG-based query answering (KGQA) research, in many

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Cheng, Siyuan, Ningyu Zhang, Bozhong Tian, Xi Chen, Qingbin Liu, and Huajun Chen. "Editing Language Model-Based Knowledge Graph Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 17835–43. http://dx.doi.org/10.1609/aaai.v38i16.29737.

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Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, making them difficult to modify post-deployment without re-training after deployment. To address this issue, we propose a new task of editing language model-based KG embeddings in this paper. This task is designed to facilitate rapid, data-efficient updates to KG embeddings without compromising the performance of other aspects. We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and

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Debruyne, Christophe, Gary Munnelly, Lynn Kilgallon, Declan O’Sullivan, and Peter Crooks. "Creating a Knowledge Graph for Ireland’s Lost History: Knowledge Engineering and Curation in the Beyond 2022 Project." Journal on Computing and Cultural Heritage 15, no. 2 (2022): 1–25. http://dx.doi.org/10.1145/3474829.

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The Beyond 2022 project aims to create a virtual archive by digitally reconstructing and digitizing historical records lost in a catastrophic fire which consumed items in the Public Record Office of Ireland in 1922. The project is developing a knowledge graph (KG) to facilitate information retrieval and discovery over the reconstructed items. The project decided to adopt Semantic Web technologies to support its distributed KG and reasoning. In this article, we present our approach to KG generation and management. We elaborate on how we help historians contribute to the KG (via a suite of sprea

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Govindapillai, Sini, Lay-Ki Soon, and Su-Cheng Haw. "Resource Description Framework reification for trustworthiness in knowledge graphs." F1000Research 10 (September 2, 2021): 881. http://dx.doi.org/10.12688/f1000research.72843.1.

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Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed. Therefore, the provenance of knowledge can assist in building up the trust of these knowledge graphs. In this paper, we have provided an analysis of popular, general knowledge graphs Wikidata and YAGO4 with regard

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Govindapillai, Sini, Soon Lay-Ki, and Haw Su-Cheng. "Domain-Independent True Fact Identification from Knowledge Graph." JOIV : International Journal on Informatics Visualization 9, no. 3 (2025): 893. https://doi.org/10.62527/joiv.9.3.3690.

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The trustworthiness of information in the Knowledge Graph (KG) is determined by the trustworthiness of information at the fact level. KGs are incomplete and noisy. Yet, most existing error detection approaches were applied to specific KGs. A large percentage of error detection approaches work well on DBpedia, particularly. However, we do not have a single KG containing all the information regarding the entity relations of a specific entity from any random class. The main objective of this research is to increase the trustworthiness of entity relations from KGs. In this paper, we propose a fram

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Liu, Xiangyu, Yang Liu, and Wei Hu. "Knowledge Graph Error Detection with Contrastive Confidence Adaption." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8824–31. http://dx.doi.org/10.1609/aaai.v38i8.28729.

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Knowledge graphs (KGs) often contain various errors. Previous works on detecting errors in KGs mainly rely on triplet embedding from graph structure. We conduct an empirical study and find that these works struggle to discriminate noise from semantically-similar correct triplets. In this paper, we propose a KG error detection model CCA to integrate both textual and graph structural information from triplet reconstruction for better distinguishing semantics. We design interactive contrastive learning to capture the differences between textual and structural patterns. Furthermore, we construct r

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Zhang, Zhenyu, Lei Zhang, Dingqi Yang, and Liu Yang. "KRAN: Knowledge Refining Attention Network for Recommendation." ACM Transactions on Knowledge Discovery from Data 16, no. 2 (2022): 1–20. http://dx.doi.org/10.1145/3470783.

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Recommender algorithms combining knowledge graph and graph convolutional network are becoming more and more popular recently. Specifically, attributes describing the items to be recommended are often used as additional information. These attributes along with items are highly interconnected, intrinsically forming a Knowledge Graph (KG). These algorithms use KGs as an auxiliary data source to alleviate the negative impact of data sparsity. However, these graph convolutional network based algorithms do not distinguish the importance of different neighbors of entities in the KG, and according to

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Zeng, Yiping, and Shumin Liu. "Research on recommendation algorithm of Graph attention Network based on Knowledge graph." Journal of Physics: Conference Series 2113, no. 1 (2021): 012085. http://dx.doi.org/10.1088/1742-6596/2113/1/012085.

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Abstract The introduction of knowledge graph as the auxiliary information of recommendation system provides a new research idea for personalized intelligent recommendation. However, most of the existing knowledge graph recommendation algorithms fail to effectively solve the problem of unrelated entities, leading to inaccurate prediction of potential preferences of users. To solve this problem, this paper proposes a KG-IGAT model combining knowledge graph and graph attention network, and adds an interest evolution module to graph attention network to capture user interest changes and generate t

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Schwabe, Tim, and Maribel Acosta. "Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks." Proceedings of the ACM on Management of Data 2, no. 1 (2024): 1–26. http://dx.doi.org/10.1145/3639299.

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Cardinality Estimation over Knowledge Graphs (KG) is crucial for query optimization, yet remains a challenging task due to the semi-structured nature and complex correlations of data in typical KGs. In this work, we propose GNCE, a novel approach that leverages knowledge graph embeddings and Graph Neural Networks (GNN) to accurately predict the cardinality of conjunctive queries over KGs. GNCE first creates semantically meaningful embeddings for all entities in the KG, which are then used to learn a representation of a query using a GNN to estimate the cardinality of the query. We evaluate GNC

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Yu, Guangya, Qi Ye, and Tong Ruan. "Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label." Bioengineering 11, no. 3 (2024): 225. http://dx.doi.org/10.3390/bioengineering11030225.

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The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledg

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Yang, Cheng, Chunxia Zhang, and Yihao Chen. "An entity alignment method with attribute augmentation and contrastive learning." Journal of Physics: Conference Series 2858, no. 1 (2024): 012049. http://dx.doi.org/10.1088/1742-6596/2858/1/012049.

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Abstract The entity alignment(EA) task is to identify entities with the same semantics in the knowledge graph(KG), an essential issue in KG fusion and big data mining. Existing entity alignment methods mainly adopt graph embedding-based methods. However, they still have some shortcomings. First, they heavily rely on high-quality alignment seed and external semantic information. Secondly, the present attention mechanism focuses on the entire graph information, neglecting the noise of attribute information. This paper proposes an EA approach based on Attribute Augmentation and Contrastive Learni

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Chen, Liyi, Jie Liu, Yutai Duan, and Runze Wang. "KG-prompt: Interpretable knowledge graph prompt for pre-trained language models." Knowledge-Based Systems 311 (February 2025): 113118. https://doi.org/10.1016/j.knosys.2025.113118.

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