Log in

Relevant bibliographies by topics / Graph-based representation and reasoning / Journal articles

To see the other types of publications on this topic, follow the link: Graph-based representation and reasoning.

Journal articles on the topic 'Graph-based representation and reasoning'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Graph-based representation and reasoning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

Zhou, Xiaojie, Pengjun Zhai, and Yu Fang. "Learning Description-Based Representations for Temporal Knowledge Graph Reasoning via Attentive CNN." Journal of Physics: Conference Series 2025, no. 1 (2021): 012003. http://dx.doi.org/10.1088/1742-6596/2025/1/012003.

Full text

Abstract:

Abstract Knowledge graphs have played a significant role in various applications and knowledge reasoning is one of the key tasks. However, the task gets more challenging when each fact is associated with a time annotation on temporal knowledge graph. Most of the existing temporal knowledge graph representation learning methods exploit structural information to learn the entity and relation representations. By these methods, those entities with similar structural information cannot be easily distinguished. Incorporating other information is an effective way to solve such problems. To address th

APA, Harvard, Vancouver, ISO, and other styles

3

Feng, Siling, Cong Zhou, Qian Liu, Xunyang Ji, and Mengxing Huang. "Temporal Knowledge Graph Reasoning Based on Entity Relationship Similarity Perception." Electronics 13, no. 12 (2024): 2417. http://dx.doi.org/10.3390/electronics13122417.

Full text

Abstract:

Temporal knowledge graphs (TKGs) are used for dynamically modeling facts in the temporal dimension, and are widely used in various fields. However, existing reasoning models often fail to consider the similarity features between entity relationships and static attributes, making it difficult for them to effectively handle these temporal attributes. Therefore, these models have limitations in dealing with previously invisible entities that appear over time and the implicit associations of static attributes between entities. To address this issue, we propose a temporal knowledge graph reasoning

APA, Harvard, Vancouver, ISO, and other styles

4

Zhao, Xiaojuan, Aiping Li, Rong Jiang, Kai Chen, and Zhichao Peng. "Householder Transformation-Based Temporal Knowledge Graph Reasoning." Electronics 12, no. 9 (2023): 2001. http://dx.doi.org/10.3390/electronics12092001.

Full text

Abstract:

Knowledge graphs’ reasoning is of great significance for the further development of artificial intelligence and information retrieval, especially for reasoning over temporal knowledge graphs. The rotation-based method has been shown to be effective at modeling entities and relations on a knowledge graph. However, due to the lack of temporal information representation capability, existing approaches can only model partial relational patterns and they cannot handle temporal combination reasoning. In this regard, we propose HTTR: Householder Transformation-based Temporal knowledge graph Reasoning

APA, Harvard, Vancouver, ISO, and other styles

5

Tang, Yaling, and Peng Yang. "Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification." Journal of Physics: Conference Series 2303, no. 1 (2022): 012030. http://dx.doi.org/10.1088/1742-6596/2303/1/012030.

Full text

Abstract:

Abstract Tabular fact verification is a challenging task that requires obtaining relevant evidence from the table and utilizing them to verify a given claim. The main difficulty in tabular fact verification is that traditional language models cannot capture the underlying information carried in tabular data. To solve this problem, we propose GraERR, a Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification. It consists of two modules: a data initial representation module based on the DeBERTa model and a graph-augmented representation and inference module. The former imp

APA, Harvard, Vancouver, ISO, and other styles

6

Lv, Shangwen, Daya Guo, Jingjing Xu, et al. "Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8449–56. http://dx.doi.org/10.1609/aaai.v34i05.6364.

Full text

Abstract:

Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on the evidence. Recent studies either learn to generate evidence from human-annotated evidence which is expensive to collect, or extract evidence from either structured or unstructured knowledge bases which fails to take advantages of both sources simultaneously. In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, an

APA, Harvard, Vancouver, ISO, and other styles

7

Chein, Michel, Marie-Laure Mugnier, and Madalina Croitoru. "Visual reasoning with graph-based mechanisms: the good, the better and the best." Knowledge Engineering Review 28, no. 3 (2013): 249–71. http://dx.doi.org/10.1017/s0269888913000234.

Full text

Abstract:

AbstractThis paper presents a graph-based knowledge representation and reasoning language. This language benefits from an important syntactic operation, which is called a graph homomorphism. This operation is sound and complete with respect to logical deduction. Hence, it is possible to do logical reasoning without using the language of logic but only graphical, thus visual, notions. This paper presents the main knowledge constructs of this language, elementary graph-based reasoning mechanisms, as well as the graph homomorphism, which encompasses all these elementary transformations in one glo

APA, Harvard, Vancouver, ISO, and other styles

8

Huang, Zhao, and Liu Yuan. "Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies." Discrete Dynamics in Nature and Society 2021 (July 30, 2021): 1–18. http://dx.doi.org/10.1155/2021/2857611.

Full text

Abstract:

People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexi

APA, Harvard, Vancouver, ISO, and other styles

9

Lee, Jae Yeol, and Kwangsoo Kim. "Geometric reasoning for knowledge-based parametric design using graph representation." Computer-Aided Design 28, no. 10 (1996): 831–41. http://dx.doi.org/10.1016/0010-4485(96)00016-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Jiang, Pin, and Yahong Han. "Reasoning with Heterogeneous Graph Alignment for Video Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11109–16. http://dx.doi.org/10.1609/aaai.v34i07.6767.

Full text

Abstract:

The dominant video question answering methods are based on fine-grained representation or model-specific attention mechanism. They usually process video and question separately, then feed the representations of different modalities into following late fusion networks. Although these methods use information of one modality to boost the other, they neglect to integrate correlations of both inter- and intra-modality in an uniform module. We propose a deep heterogeneous graph alignment network over the video shots and question words. Furthermore, we explore the network architecture from four steps

APA, Harvard, Vancouver, ISO, and other styles

11

Zou, Jun, Jing Wan, Hao Zhang, and Yunbing Zhang. "A Multi-hop Path Query Answering Model for Knowledge Graph based on Neighborhood Aggregation and Transformer." Journal of Physics: Conference Series 2560, no. 1 (2023): 012049. http://dx.doi.org/10.1088/1742-6596/2560/1/012049.

Full text

Abstract:

Abstract Multi-hop path query answering is a complex task in which a path needs to be inferred from a knowledge graph that contains a head entity and multi-hop relations. The objective is to identify the corresponding tail entity accurately. The representation of the path is a critical factor in this task. However, existing methods do not adequately consider the context information of the entities and relations in the path. To address this issue, this paper proposes a novel multi-hop path query answering model that utilizes an enhanced reasoning path feature representation to incorporate inter

APA, Harvard, Vancouver, ISO, and other styles

12

Chen, Yonghong, Hao Li, Han Li, et al. "An Overview of Knowledge Graph Reasoning: Key Technologies and Applications." Journal of Sensor and Actuator Networks 11, no. 4 (2022): 78. http://dx.doi.org/10.3390/jsan11040078.

Full text

Abstract:

In recent years, with the rapid development of Internet technology and applications, the scale of Internet data has exploded, which contains a significant amount of valuable knowledge. The best methods for the organization, expression, calculation, and deep analysis of this knowledge have attracted a great deal of attention. The knowledge graph has emerged as a rich and intuitive way to express knowledge. Knowledge reasoning based on knowledge graphs is one of the current research hot spots in knowledge graphs and has played an important role in wireless communication networks, intelligent que

APA, Harvard, Vancouver, ISO, and other styles

13

Zong, Jiachuang, Zhao Li, Tong Chen, Liguo Zhang, and Yiming Zhan. "Parallel Fusion of Graph and Text with Semantic Enhancement for Commonsense Question Answering." Electronics 13, no. 23 (2024): 4618. http://dx.doi.org/10.3390/electronics13234618.

Full text

Abstract:

Commonsense question answering (CSQA) is a challenging task in the field of knowledge graph question answering. It combines the context of the question with the relevant knowledge in the knowledge graph to reason and give an answer to the question. Existing CSQA models combine pretrained language models and graph neural networks to process question context and knowledge graph information, respectively, and obtain each other’s information during the reasoning process to improve the accuracy of reasoning. However, the existing models do not fully utilize the textual representation and graph repr

APA, Harvard, Vancouver, ISO, and other styles

14

Li, Mingxiao, and Marie-Francine Moens. "Dynamic Key-Value Memory Enhanced Multi-Step Graph Reasoning for Knowledge-Based Visual Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 10983–92. http://dx.doi.org/10.1609/aaai.v36i10.21346.

Full text

Abstract:

Knowledge-based visual question answering (VQA) is a vision-language task that requires an agent to correctly answer image-related questions using knowledge that is not presented in the given image. It is not only a more challenging task than regular VQA but also a vital step towards building a general VQA system. Most existing knowledge-based VQA systems process knowledge and image information similarly and ignore the fact that the knowledge base (KB) contains complete information about a triplet, while the extracted image information might be incomplete as the relations between two objects a

APA, Harvard, Vancouver, ISO, and other styles

15

Chen, Jiahui, Yi Yang, Ling Peng, Luanjie Chen, and Xingtong Ge. "Knowledge Graph Representation Learning-Based Forest Fire Prediction." Remote Sensing 14, no. 17 (2022): 4391. http://dx.doi.org/10.3390/rs14174391.

Full text

Abstract:

Forest fires destroy the ecological environment and cause large property loss. There is much research in the field of geographic information that revolves around forest fires. The traditional forest fire prediction methods hardly consider multi-source data fusion. Therefore, the forest fire predictions ignore the complex dependencies and correlations of the spatiotemporal kind that usually bring valuable information for the predictions. Although the knowledge graph methods have been used to model the forest fires data, they mainly rely on artificially defined inference rules to make prediction

APA, Harvard, Vancouver, ISO, and other styles

16

Li, Tao, Hao Li, Sheng Zhong, et al. "Knowledge Graph Representation Reasoning for Recommendation System." Journal of New Media 2, no. 1 (2020): 21–30. http://dx.doi.org/10.32604/jnm.2020.09767.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Wan, Guojia, and Bo Du. "GaussianPath:A Bayesian Multi-Hop Reasoning Framework for Knowledge Graph Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4393–401. http://dx.doi.org/10.1609/aaai.v35i5.16565.

Full text

Abstract:

Recently, multi-hop reasoning over incomplete Knowledge Graphs (KGs) has attracted wide attention due to its desirable interpretability for downstream tasks, such as question answer and knowledge graph completion. Multi-Hop reasoning is a typical sequential decision problem, which can be formulated as a Markov decision process (MDP). Subsequently, some reinforcement learning (RL) based approaches are proposed and proven effective to train an agent for reasoning paths sequentially until reaching the target answer. However, these approaches assume that an entity/relation representation follows a

APA, Harvard, Vancouver, ISO, and other styles

18

Sha, Yuchen, Yujian Feng, Miao He, Shangdong Liu, and Yimu Ji. "Retrieval-Augmented Knowledge Graph Reasoning for Commonsense Question Answering." Mathematics 11, no. 15 (2023): 3269. http://dx.doi.org/10.3390/math11153269.

Full text

Abstract:

Existing knowledge graph (KG) models for commonsense question answering present two challenges: (i) existing methods retrieve entities related to questions from the knowledge graph, which may extract noise and irrelevant nodes, and (ii) there is a lack of interaction representation between questions and graph entities. However, current methods mainly focus on retrieving relevant entities with some noisy and irrelevant nodes. In this paper, we propose a novel retrieval-augmented knowledge graph (RAKG) model, which solves the above issues using two key innovations. First, we leverage the density

APA, Harvard, Vancouver, ISO, and other styles

19

Karimi Mamaghan, Amir Mohammad, Andrea Dittadi, Stefan Bauer, Karl Henrik Johansson, and Francesco Quinzan. "Diffusion-Based Causal Representation Learning." Entropy 26, no. 7 (2024): 556. http://dx.doi.org/10.3390/e26070556.

Full text

Abstract:

Causal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause–effect estimation and the identification of efficient and safe interventions. However, learning causal representations remains a major challenge, due to the complexity of many real-world systems. Previous works on causal representation learning have mostly focused on Variational Auto-Encoders (VAEs). These methods only provide representations from a point estimate, and they are less effective at handling high dimensions. To overcome these problems

APA, Harvard, Vancouver, ISO, and other styles

20

Hu, Jingjing, Dan Guo, Zhan Si, et al. "MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 317–25. https://doi.org/10.1609/aaai.v39i1.32009.

Full text

Abstract:

Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transformers (GTs) have shown potential in the realm of self-supervised pretraining. However, existing approaches often overlook the relationship between molecular structure and electronic information, as well as the internal semantic reasoning within molecules. This omission of fundamental chemical knowledge in graph semantics leads to incomplete molecular representations, missing th

APA, Harvard, Vancouver, ISO, and other styles

21

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

22

Ta'aseh, Nevo, and Offer Shai. "Network Graph Theory Perspective on Skeletal Structures for Theoretical and Educational Purposes." International Journal of Mechanical Engineering Education 36, no. 4 (2008): 294–319. http://dx.doi.org/10.7227/ijmee.36.4.3.

Full text

Abstract:

The paper introduces an approach to the analysis of skeletal structures in which they are represented by a discrete mathematical model called graph representation. The paper shows that the reasoning upon the structure can be performed solely upon the representation, which, besides the theoretical value, presents a powerful educational tool. Students can learn skeletal structures entirely through the graph representations and derive advanced structural topics, including the conjugate theorem and the unit force method from the theorems and principles of network graph theory. The graph representa

APA, Harvard, Vancouver, ISO, and other styles

23

Oh, Dongsuk, Jungwoo Lim, Kinam Park, and Heuiseok Lim. "Semantic Representation Using Sub-Symbolic Knowledge in Commonsense Reasoning." Applied Sciences 12, no. 18 (2022): 9202. http://dx.doi.org/10.3390/app12189202.

Full text

Abstract:

The commonsense question and answering (CSQA) system predicts the right answer based on a comprehensive understanding of the question. Previous research has developed models that use QA pairs, the corresponding evidence, or the knowledge graph as an input. Each method executes QA tasks with representations of pre-trained language models. However, the ability of the pre-trained language model to comprehend completely remains debatable. In this study, adversarial attack experiments were conducted on question-understanding. We examined the restrictions on the question-reasoning process of the pre

APA, Harvard, Vancouver, ISO, and other styles

24

Ferilli, Stefano, Eleonora Bernasconi, Davide Di Pierro, and Domenico Redavid. "A Graph DB-Based Solution for Semantic Technologies in the Future Internet." Future Internet 15, no. 10 (2023): 345. http://dx.doi.org/10.3390/fi15100345.

Full text

Abstract:

With the progressive improvements in the power, effectiveness, and reliability of AI solutions, more and more critical human problems are being handled by automated AI-based tools and systems. For more complex or particularly critical applications, the level of knowledge, not just information, must be handled by systems where explicit relationships among objects are represented and processed. For this purpose, the knowledge representation branch of AI proposes Knowledge Graphs, widely used in the Semantic Web, where different online applications may interact by understanding the meaning of the

APA, Harvard, Vancouver, ISO, and other styles

25

Wang, Yilin, Zhen Huang, Minghao Hu, et al. "Structure Enhanced Path Reasoning for Knowledge Graph Completion." International Journal of Intelligent Systems 2023 (May 17, 2023): 1–18. http://dx.doi.org/10.1155/2023/3022539.

Full text

Abstract:

Knowledge graphs are crucial foundations for building intelligent systems, such as question answering and recommendation. However, their performance is hampered by the incompleteness of KGs, so the knowledge graph completion arises to infer whether a triple of the form (head entity, relation, tail entity) is a missing fact. The path-based approach that encodes paths from the head entity to the tail entity for reasoning achieves good performance. Previous work suggests that entity type is beneficial for learning path representations. Nevertheless, the semantics of entities are not captured accu

APA, Harvard, Vancouver, ISO, and other styles

26

Cheng, Yuhao, Xiaoguang Zhu, Jiuchao Qian, Fei Wen, and Peilin Liu. "Cross-modal Graph Matching Network for Image-text Retrieval." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (2022): 1–23. http://dx.doi.org/10.1145/3499027.

Full text

Abstract:

Image-text retrieval is a fundamental cross-modal task whose main idea is to learn image-text matching. Generally, according to whether there exist interactions during the retrieval process, existing image-text retrieval methods can be classified into independent representation matching methods and cross-interaction matching methods. The independent representation matching methods generate the embeddings of images and sentences independently and thus are convenient for retrieval with hand-crafted matching measures (e.g., cosine or Euclidean distance). As to the cross-interaction matching metho

APA, Harvard, Vancouver, ISO, and other styles

27

Zhang, Liyuan, Kaitao Hu, Xianghua Ma, and Xiangyu Sun. "Combining Semantic and Structural Features for Reasoning on Patent Knowledge Graphs." Applied Sciences 14, no. 15 (2024): 6807. http://dx.doi.org/10.3390/app14156807.

Full text

Abstract:

To address the limitations in capturing complex semantic features between entities and the incomplete acquisition of entity and relationship information by existing patent knowledge graph reasoning algorithms, we propose a reasoning method that integrates semantic and structural features for patent knowledge graphs, denoted as SS-DSA. Initially, to facilitate the model representation of patent information, a directed graph representation model based on the patent knowledge graph is designed. Subsequently, structural information within the knowledge graph is mined using inductive learning, whic

APA, Harvard, Vancouver, ISO, and other styles

28

Lanchantin, Jack, Sainbayar Sukhbaatar, Gabriel Synnaeve, Yuxuan Sun, Kavya Srinet, and Arthur Szlam. "A Data Source for Reasoning Embodied Agents." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8438–46. http://dx.doi.org/10.1609/aaai.v37i7.26017.

Full text

Abstract:

Recent progress in using machine learning models for reasoning tasks has been driven by novel model architectures, large-scale pre-training protocols, and dedicated reasoning datasets for fine-tuning. In this work, to further pursue these advances, we introduce a new data generator for machine reasoning that integrates with an embodied agent. The generated data consists of templated text queries and answers, matched with world-states encoded into a database. The world-states are a result of both world dynamics and the actions of the agent. We show the results of several baseline models on inst

APA, Harvard, Vancouver, ISO, and other styles

29

Zeng, Zefan, Qing Cheng, and Yuehang Si. "Logical Rule-Based Knowledge Graph Reasoning: A Comprehensive Survey." Mathematics 11, no. 21 (2023): 4486. http://dx.doi.org/10.3390/math11214486.

Full text

Abstract:

With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management. However, the presence of biases in our cognitive and construction processes often leads to varying degrees of incompleteness and errors within knowledge graphs. To address this, reasoning becomes essential for supplementing and rectifying these shortcomings. Logical rule-based knowledge graph reasoning methods excel at performing inference by uncovering underlying logical rules, showcasing remarkable generalization ability a

APA, Harvard, Vancouver, ISO, and other styles

30

Kamsu-Foguem, Bernard, Gayo Diallo, and Clovis Foguem. "Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine." Engineering Applications of Artificial Intelligence 26, no. 4 (2013): 1348–65. http://dx.doi.org/10.1016/j.engappai.2012.12.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Wang, Xiaodong, Pei He, Hongjing Yao, Xiangnan Shi, Jiwei Wang, and Yangming Guo. "A Dual Fusion Pipeline to Discover Tactical Knowledge Guided by Implicit Graph Representation Learning." Mathematics 12, no. 4 (2024): 528. http://dx.doi.org/10.3390/math12040528.

Full text

Abstract:

Discovering tactical knowledge aims to extract tactical data derived from battlefield signal data, which is vital in information warfare. The learning and reasoning from battlefield signal information can help commanders make effective decisions. However, traditional methods are limited in capturing sequential and global representation due to their reliance on prior knowledge or feature engineering. The current models based on deep learning focus on extracting implicit behavioral characteristics from combat process data, overlooking the embedded martial knowledge within the recognition of comb

APA, Harvard, Vancouver, ISO, and other styles

32

Geng, Shijie, Peng Gao, Moitreya Chatterjee, et al. "Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1415–23. http://dx.doi.org/10.1609/aaai.v35i2.16231.

Full text

Abstract:

Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content. This task thus poses a challenging multi-modal representation learning and reasoning scenario, advancements into which could influence several human-machine interaction applications. To solve this task, we introduce a semantics-controlled multi-modal shuffled Transformer reasoning framework, consisting of a sequence of Transformer modules, each taking a modality as input and produci

APA, Harvard, Vancouver, ISO, and other styles

33

Chen, Yifei, Xuliang Duan, and Yan Guo. "GLR: Graph Chain-of-Thought with LoRA Fine-Tuning and Confidence Ranking for Knowledge Graph Completion." Applied Sciences 15, no. 13 (2025): 7282. https://doi.org/10.3390/app15137282.

Full text

Abstract:

In knowledge graph construction, missing facts often lead to incomplete structures, thereby limiting the performance of downstream applications. Although recent knowledge graph completion (KGC) methods based on representation learning have achieved notable progress, they still suffer from two fundamental limitations, namely the lack of structured reasoning capabilities and the inability to assess the confidence of their predictions, which often results in unreliable outputs. We propose the GLR framework, which integrates Graph Chain-of-Thought (Graph-CoT) reasoning, LoRA fine-tuning, and the P

APA, Harvard, Vancouver, ISO, and other styles

34

Daniela, Dănciulescu, Colhon Mihaela, and Grigoraş Gheorghe. "BRAIN Journal - Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 8, no. 1 (2017): 42–51. https://doi.org/10.5281/zenodo.1045027.

Full text

Abstract:

ABSTRACT In Tudor (Preda) (2010) a method for formal languages generation based on labeled stratified graph representations is sketched. The author proves that the considered method can generate regular languages and context-sensitive languages by considering an exemplification of the proposed method for a particular regular language and another one for a particular contextsensitive language. At the end of the study, the author highlights some open problems for future research among which we remind: (1) The study of the language families that can be generated by means of these structures; (2)

APA, Harvard, Vancouver, ISO, and other styles

35

Xuan, Ping, Hanwen Bi, Hui Cui, et al. "Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation." Physics in Medicine & Biology 67, no. 22 (2022): 225018. http://dx.doi.org/10.1088/1361-6560/ac9e3f.

Full text

Abstract:

Abstract Objective. Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks. Approach. We propose a novel deep graph reasoning model to learn from multi-order neighborhood topologies for volumetric image segmentation. A graph is first constructed with nodes representing image regions and graph topology to derive spatial dependencies and semantic connections across image regions. We propose a new node attribute embedding mechanism to formulate topological attributes for each image region

APA, Harvard, Vancouver, ISO, and other styles

36

Li, Zhenping, Zhen Cao, Pengfei Li, Yong Zhong, and Shaobo Li. "Multi-Hop Question Generation with Knowledge Graph-Enhanced Language Model." Applied Sciences 13, no. 9 (2023): 5765. http://dx.doi.org/10.3390/app13095765.

Full text

Abstract:

The task of multi-hop question generation (QG) seeks to generate questions that require a complex reasoning process that spans multiple sentences and answers. Beyond the conventional challenges of what to ask and how to ask, multi-hop QG necessitates sophisticated reasoning from dispersed evidence across multiple sentences. To address these challenges, a knowledge graph-enhanced language model (KGEL) has been developed to imitate human reasoning for multi-hop questions.The initial step in KGEL involves encoding the input sentence with a pre-trained GPT-2 language model to obtain a comprehensiv

APA, Harvard, Vancouver, ISO, and other styles

37

Zhong, Dong, Yi-An Zhu, Lanqing Wang, Junhua Duan, and Jiaxuan He. "A Cognition Knowledge Representation Model Based on Multidimensional Heterogeneous Data." Complexity 2020 (December 28, 2020): 1–17. http://dx.doi.org/10.1155/2020/8812459.

Full text

Abstract:

The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet the needs of in

APA, Harvard, Vancouver, ISO, and other styles

38

Yang, Yang, and Yifan Huang. "Knowledge Modeling of power grid regulation based on reasoning map." Journal of Physics: Conference Series 2087, no. 1 (2021): 012097. http://dx.doi.org/10.1088/1742-6596/2087/1/012097.

Full text

Abstract:

Abstract To contribute to the intelligence and knowledge of power grid regulation and control operations, this paper presents a method of power grid regulation knowledge modeling based on ELG (Event Logic Graph), which includes an event word extraction based on a predicate-argument model, an event chain extraction and fusion based on event similarity theory, an event generalization based on a soft-pattern algorithm, and an event relationship recognition based on rule pattern matching method and joint constraints. Finally, this paper uses events as nodes and event relationships as directed edge

APA, Harvard, Vancouver, ISO, and other styles

39

Tsai, Jhy-Cherng, and Mark R. Cutkosky. "Representation and reasoning of geometric tolerances in design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 4 (1997): 325–41. http://dx.doi.org/10.1017/s0890060400003255.

Full text

Abstract:

AbstractThe geometric dimensioning and tolerancing (GD&T) specifications of a design are directly associated with its performance and functional requirements. They also govern the manufacturing and quality control processes needed to achieve those requirements. This paper reviews recent work in geometric tolerance representation and reasoning and presents a generic and uniform graph-based representation scheme, called the Tolerance Network, to represent GD&T specifications across a part or assembly. The network can accommodate GD&T specifications related to the function, behavior,

APA, Harvard, Vancouver, ISO, and other styles

40

Fang, Yan, Jiayin Yu, Yumei Ding, and Xiaohua Lin. "Inferring Complementary and Substitutable Products Based on Knowledge Graph Reasoning." Mathematics 11, no. 22 (2023): 4709. http://dx.doi.org/10.3390/math11224709.

Full text

Abstract:

Complementarity and substitutability between products are essential concepts in retail and marketing. To achieve this, existing approaches take advantage of knowledge graphs to learn more evidence for inference. However, they often omit the knowledge that lies in the unstructured data. In this research, we concentrate on inferring complementary and substitutable products in e-commerce from mass structured and unstructured data. An improved knowledge-graph-based reasoning model has been proposed which cannot only derive related products but also provide interpretable paths to explain the relati

APA, Harvard, Vancouver, ISO, and other styles

41

Mavromatis, Costas, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, et al. "TempoQR: Temporal Question Reasoning over Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5825–33. http://dx.doi.org/10.1609/aaai.v36i5.20526.

Full text

Abstract:

Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG). Although many natural questions involve explicit or implicit time constraints, question answering (QA) over TKGs has been a relatively unexplored area. Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. This paper puts forth a comprehensive embedding

APA, Harvard, Vancouver, ISO, and other styles

42

Szwabe, Andrzej, Pawel Misiorek, Tadeusz Janasiewicz, and Przemyslaw Walkowiak. "Holistic Entropy Reduction for Collaborative Filtering." Foundations of Computing and Decision Sciences 39, no. 3 (2014): 209–29. http://dx.doi.org/10.2478/fcds-2014-0012.

Full text

Abstract:

Abstract We propose a collaborative filtering (CF) method that uses behavioral data provided as propositions having the RDF-compliant form of (user X, likes, item Y ) triples. The method involves the application of a novel self-configuration technique for the generation of vector-space representations optimized from the information-theoretic perspective. The method, referred to as Holistic Probabilistic Modus Ponendo Ponens (HPMPP), enables reasoning about the likelihood of unknown facts. The proposed vector-space graph representation model is based on the probabilistic apparatus of quantum In

APA, Harvard, Vancouver, ISO, and other styles

43

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

44

SHARMA, ARPIT. "Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge." Theory and Practice of Logic Programming 19, no. 5-6 (2019): 1021–37. http://dx.doi.org/10.1017/s1471068419000334.

Full text

Abstract:

AbstractThe Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011. In this work we attempt to solve WSC problems by reasoning with additional knowledge. By using an approach built on top of graph-subgraph isomorphism encoded using Answer Set Programming (ASP) we were able to handle 240 out of 291 WSC problems. The ASP encoding allows us to add additional constraints in an elaboration tolerant manner. In the process we present a graph based representation of WSC problems as well as relevant commonsense knowledge.

APA, Harvard, Vancouver, ISO, and other styles

45

Ma, Yunpu, and Volker Tresp. "Quantum Machine Learning Algorithm for Knowledge Graphs." ACM Transactions on Quantum Computing 2, no. 3 (2021): 1–28. http://dx.doi.org/10.1145/3467982.

Full text

Abstract:

Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computationally resource intensive. This article investigates how to capitalize on quantum resources to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for inference on tensorized data, i.e., on knowledge graphs.

APA, Harvard, Vancouver, ISO, and other styles

46

Jiang, Lei, and Zuqiang Meng. "Knowledge-Based Visual Question Answering Using Multi-Modal Semantic Graph." Electronics 12, no. 6 (2023): 1390. http://dx.doi.org/10.3390/electronics12061390.

Full text

Abstract:

The field of visual question answering (VQA) has seen a growing trend of integrating external knowledge sources to improve performance. However, owing to the potential incompleteness of external knowledge sources and the inherent mismatch between different forms of data, current knowledge-based visual question answering (KBVQA) techniques are still confronted with the challenge of effectively integrating and utilizing multiple heterogeneous data. To address this issue, a novel approach centered on a multi-modal semantic graph (MSG) is proposed. The MSG serves as a mechanism for effectively uni

APA, Harvard, Vancouver, ISO, and other styles

47

Song, Ningyuan, Hanghang Cheng, Huimin Zhou, and Xiaoguang Wang. "Linking Scholarly Contents: The Design and Construction of an Argumentation Graph." KNOWLEDGE ORGANIZATION 49, no. 4 (2022): 213–35. http://dx.doi.org/10.5771/0943-7444-2022-4-213.

Full text

Abstract:

In this study, we propose a way to link the scholarly contents of scientific papers by constructing a knowledge graph based on the semantic organization of argumentation units and relations in scientific papers. We carried out an argumentation graph data model aimed at linking multiple discourses, and also developed a semantic annotation platform for scientific papers and an argumentation graph visualization system. A construction experiment was performed using 12 articles. The final argumentation graph has 1,262 nodes and 1,628 edges, including 1,628 intra-article relations and 190 inter-arti

APA, Harvard, Vancouver, ISO, and other styles

48

Li, Li, Yongfang Xie, Xiaofang Chen, Weichao Yue, and Zhaohui Zeng. "Dynamic uncertain causality graph based on cloud model theory for knowledge representation and reasoning." International Journal of Machine Learning and Cybernetics 11, no. 8 (2020): 1781–99. http://dx.doi.org/10.1007/s13042-020-01072-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

BERGMANN, RALPH, JANET KOLODNER, and ENRIC PLAZA. "Representation in case-based reasoning." Knowledge Engineering Review 20, no. 3 (2005): 209–13. http://dx.doi.org/10.1017/s0269888906000555.

Full text

Abstract:

A case in case-based reasoning is a contextualized piece of experience, which can be represented in various forms. Traditional approaches can be classified into three main categories: feature vector representations, structured representations, and textual representations. More sophisticated approaches make use of hierarchical representations or generalized cases. For particular tasks such as design and planning highly specific representations have been developed.

APA, Harvard, Vancouver, ISO, and other styles

50

Sadeghian, Ali, Mohammadreza Armandpour, Anthony Colas, and Daisy Zhe Wang. "ChronoR: Rotation Based Temporal Knowledge Graph Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (2021): 6471–79. http://dx.doi.org/10.1609/aaai.v35i7.16802.

Full text

Abstract:

Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficult task due to data non-stationarity, data heterogeneity, and its complex temporal dependencies. We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. Learning dense representations is frequently used a

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!