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Journal articles on the topic 'Knowledge-based Information Models'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Cuzzocrea, Alfredo. "Intelligent knowledge-based models and methodologies for complex information systems." Information Sciences 194 (July 2012): 1–3. http://dx.doi.org/10.1016/j.ins.2012.02.037.

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Hsu, Hsieh-Chih, Shen Chang, Chien-Chih Chen, and I.-Chen Wu. "Knowledge-based system for resolving design clashes in building information models." Automation in Construction 110 (February 2020): 103001. http://dx.doi.org/10.1016/j.autcon.2019.103001.

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Coffey, John W., Robert Hoffman, and Alberto Cañas. "Concept Map-Based Knowledge Modeling: Perspectives from Information and Knowledge Visualization." Information Visualization 5, no. 3 (2006): 192–201. http://dx.doi.org/10.1057/palgrave.ivs.9500129.

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This article explores the idea of knowledge modeling as defined at the Florida Institute for Human and Machine Cognition. The notion of knowledge modeling is described to illustrate a particular method by which concept maps might be employed to create a useful structure and organization of other information and knowledge resources. Knowledge model structuring and navigational schemes afforded by the approach are described and illustrated. An example of a knowledge model pertaining to weather forecasting on the Gulf coast of the United States is presented to illustrate these ideas. Examples of

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Ai, Zhuming, and Yu Wei. "Knowledge based method for building molecular models." Journal of Chemical Information and Modeling 33, no. 4 (1993): 635–38. http://dx.doi.org/10.1021/ci00014a018.

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Qu, Zhi Ming. "Investigation and Impact of Knowledge-Based Information on Programming Languages Based on Probabilistic Models." Applied Mechanics and Materials 39 (November 2010): 436–40. http://dx.doi.org/10.4028/www.scientific.net/amm.39.436.

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In recent years, much research has been devoted to the refinement of IPv6; on the other hand, few have investigated the confusing unification of interrupts and Internet QoS. In this position paper, it demonstrates the emulation of interrupts. In order to overcome this quagmire, a novel system is presented for the intuitive unification of expert systems and massive multiplayer online role-playing games. It is concluded that erasure coding can be verified to make heterogeneous, interposable, and event-driven, which is proved to be applicable.

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Weiten, Moritz, and Gunter Wozny. "Advanced information management for process sciences: knowledge-based documentation of mathematical models." International Journal of Internet and Enterprise Management 2, no. 2 (2004): 178. http://dx.doi.org/10.1504/ijiem.2004.004938.

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Kowalczuk, Zdzisław, and Cezary Orłowski. "KNOWLEDGE MANAGEMENT BASED ON FORMAL MODELS OF SOFTWARE PROJECT MANAGEMENT." Cybernetics and Systems 38, no. 5-6 (2007): 611–26. http://dx.doi.org/10.1080/01969720701346538.

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Ying, Peng, Zhongnian Li, Renke Sun, and Xinzheng Xu. "Complementary label learning based on knowledge distillation." Mathematical Biosciences and Engineering 20, no. 10 (2023): 17905–18. http://dx.doi.org/10.3934/mbe.2023796.

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<abstract><p>Complementary label learning (CLL) is a type of weakly supervised learning method that utilizes the category of samples that do not belong to a certain class to learn their true category. However, current CLL methods mainly rely on rewriting classification losses without fully leveraging the supervisory information in complementary labels. Therefore, enhancing the supervised information in complementary labels is a promising approach to improve the performance of CLL. In this paper, we propose a novel framework called Complementary Label Enhancement based on Knowledge

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Peng, Haoliang, and Yue Wu. "A Dynamic Convolutional Network-Based Model for Knowledge Graph Completion." Information 13, no. 3 (2022): 133. http://dx.doi.org/10.3390/info13030133.

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Knowledge graph embedding can learn low-rank vector representations for knowledge graph entities and relations, and has been a main research topic for knowledge graph completion. Several recent works suggest that convolutional neural network (CNN)-based models can capture interactions between head and relation embeddings, and hence perform well on knowledge graph completion. However, previous convolutional network models have ignored the different contributions of different interaction features to the experimental results. In this paper, we propose a novel embedding model named DyConvNE for kn

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GARG, PANKAJ K., THUAN Q. PHAM, BRIAN BEACH, et al. "MATISSE: A KNOWLEDGE-BASED TEAM PROGRAMMING ENVIRONMENT." International Journal of Software Engineering and Knowledge Engineering 04, no. 01 (1994): 17–59. http://dx.doi.org/10.1142/s0218194094000039.

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The downstream activities of large scale software systems life-cycle require team effort of several people over extended time periods. Current computing environments, exemplified by Unix, provide limited support for such team activities. Matisse is a knowledge-based team programming environment that addresses this issue. Matisse models source code, associated documents, people, and tasks as objects in an information system. Relationships between these objects provide associative information retrieval and navigational support; rules defined on object attributes facilitate team coordination and

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Li, Jun, Lu Qian, Peifeng Liu, and Taoxiong Liu. "Construction of Legal Knowledge Graph Based on Knowledge-Enhanced Large Language Models." Information 15, no. 11 (2024): 666. http://dx.doi.org/10.3390/info15110666.

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Legal knowledge involves multidimensional heterogeneous knowledge such as legal provisions, judicial interpretations, judicial cases, and defenses, which requires extremely high relevance and accuracy of knowledge. Meanwhile, the construction of a legal knowledge reasoning system also faces challenges in obtaining, processing, and sharing multisource heterogeneous knowledge. The knowledge graph technology, which is a knowledge organization form with triples as the basic unit, is able to efficiently transform multisource heterogeneous information into a knowledge representation form close to hu

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Santos, Leopoldo Santos, Miguel-Angel Sicilia, and Elena Garcia-Barriocanal. "Ontology-Based Modeling of Effect-Based Knowledge in Disaster Response." International Journal on Semantic Web and Information Systems 15, no. 1 (2019): 102–18. http://dx.doi.org/10.4018/ijswis.2019010105.

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Emergency response and management requires the coordination of agencies and different services in a complex evolving situation. This in turn, requires diverse models representing detailed knowledge about the types of adverse events, their potential impact and the means and resources that are best suited for an effective response. The basic formal infrastructure incident assessment ontology (BFiaO) is oriented towards fulfilling these needs. BFiaO is a meta-model for handling infrastructure-related situations, but it did not provide models for a catalogue of adverse events and the means necessa

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Vergassola, R., W. Zong, M. R. Berthold, and R. Silipo. "Knowledge-based and Data-driven Models in Arrhythmia Fuzzy Classification." Methods of Information in Medicine 40, no. 05 (2001): 397–402. http://dx.doi.org/10.1055/s-0038-1634199.

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Summary Objectives: Fuzzy rules automatically derived from a set of training examples quite often produce better classification results than fuzzy rules translated from medical knowledge. This study aims to investigate the difference in domain representation between a knowledge-based and a data-driven fuzzy system applied to an electrocardiography classification problem. Methods: For a three-class electrocardiographic arrhythmia classification task a set of fifteen fuzzy rules is derived from medical expertise on the basis of twelve electrocardiographic measures. A second set of fuzzy rules is

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Varadarajan, Udaya, and Biswanath Dutta. "Models for Narrative Information: A Study." KNOWLEDGE ORGANIZATION 49, no. 3 (2022): 172–91. http://dx.doi.org/10.5771/0943-7444-2022-3-172.

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From the literature study, it was observed that there are significantly fewer studies that review ontology- based narrative models. This motivates the current work. A parametric approach was adopted to report the existing ontology-driven models for narrative information. The work considers the narrative and ontology components as parameters. This study hopes to encompass the relevant literature and ontology models together. The work adopts a systematic literature review methodology for an extensive literature selection. The models were selected from the literature using a stratified random sam

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Ivanovski, Aleksandar, Milos Jovanovik, Riste Stojanov, and Dimitar Trajanov. "Knowledge Graph Based Recommender for Automatic Playlist Continuation." Information 14, no. 9 (2023): 510. http://dx.doi.org/10.3390/info14090510.

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In this work, we present a state-of-the-art solution for automatic playlist continuation through a knowledge graph-based recommender system. By integrating representational learning with graph neural networks and fusing multiple data streams, the system effectively models user behavior, leading to accurate and personalized recommendations. We provide a systematic and thorough comparison of our results with existing solutions and approaches, demonstrating the remarkable potential of graph-based representation in improving recommender systems. Our experiments reveal substantial enhancements over

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Sandkuhl, Kurt, Ulf Seigerroth, Alexander Smirnov, Tatiana Levashova, and Nikolay Shilov. "Service configuration in SOA-based enterprise representation using role knowledge." Management and Production Engineering Review 5, no. 1 (2014): 51–64. http://dx.doi.org/10.2478/mper-2014-0007.

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Abstract This work investigates the use of enterprise models for service configuration with a focus on knowledge related to organizational roles. Starting from an industrial case, it presents an approach to extract a role’s information demand from an enterprise model and to use this as initial configuration for agent-based services. In addition to the meta-model included in the enterprise modeling language, the use of a common ontology is proposed which captures both, the perspective of information demand and services. The architecture of the agentbased services is following the idea to charac

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Šuman, Sabrina, Alen Jakupović, and Francesca Gržinić Kuljanac. "Knowledge-Based Systems for Data Modelling." International Journal of Enterprise Information Systems 12, no. 2 (2016): 1–13. http://dx.doi.org/10.4018/ijeis.2016040101.

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Data modelling is a complex process that depends on the knowledge and experience of the designers who carry it out. The quality of created models has a significant impact on the quality of successive phases of information systems development. This paper, in short, reviews the data modelling process, the entity-relationship method (ERM) and actors in the data modelling process. Further, in more detail it presents systems, methods, and tools for the data modelling process and identifies problems that occur during the development phase of an information system. These problems also represent the a

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Ye, Yutao. "An overview of knowledge graph-based recommendation systems." Applied and Computational Engineering 73, no. 1 (2024): 57–68. http://dx.doi.org/10.54254/2755-2721/73/20240363.

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Recommendation systems have emerged as effective tools for mitigating information overload. Traditionally, recommendation systems employ various models such as Collaborative Filtering, Matrix Decomposition, and Logic Decomposition. Among these, Collaborative Filtering stands out due to its high efficiency. However, it encounters challenges related to cold start and sparse data. To address these challenges, the integration of Knowledge Graphs with recommendation systems has demonstrated significant advantages. This paper classifies Knowledge Graph-based recommendation systems into two categorie

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Savolainen, Reijo. "Conceptual growth in integrated models for information behaviour." Journal of Documentation 72, no. 4 (2016): 648–73. http://dx.doi.org/10.1108/jdoc-09-2015-0114.

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Purpose – The purpose of this paper is to elaborate the picture of the nature of integrated models for information behaviour from the perspective of conceptual growth in this field of study. Design/methodology/approach – Conceptual analysis focusing on the ways in which the researchers have developed integrated models. The study concentrates on seven key models proposed by Bates, Choo and associates, Godbold, Robson and Robinson, and Wilson. Findings – Researchers have employed four main approaches to develop integrated models. First, such frameworks are based on the juxtaposition of individua

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Morsy, Sara, and George Karypis. "Context-aware Nonlinear and Neural Attentive Knowledge-based Models for Grade Prediction." Journal of Educational Data Mining 12, no. 1 (2020): 1–18. https://doi.org/10.5281/zenodo.3911795.

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Grade prediction can help students and their advisers select courses and design personalized degree programs based on predicted future course performance. One of the successful approaches for accu- rately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. However, there can be more complex interactions among prior courses taken by a student, which cannot be captured by the current linear CKRM model. More-

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Uspenskij, Mikhail B. "Log mining and knowledge-based models in data storage systems diagnostics." E3S Web of Conferences 140 (2019): 03006. http://dx.doi.org/10.1051/e3sconf/201914003006.

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Modern data storage systems have a sophisticated hardware and software architecture, including multiple storage processors, storage fabrics, network equipment and storage media and contain information, which can be damaged or lost because of hardware or software fault. Approach to storage software diagnostics, presented in current paper, combines a log mining algorithms for fault detection based on natural language processing text classification methods, and usage of the diagnostic model for a task of fault source detection. Currently existing approaches to computational systems diagnostics ar

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SIM, ADELENE Y. L., OLIVIER SCHWANDER, MICHAEL LEVITT, and JULIE BERNAUER. "EVALUATING MIXTURE MODELS FOR BUILDING RNA KNOWLEDGE-BASED POTENTIALS." Journal of Bioinformatics and Computational Biology 10, no. 02 (2012): 1241010. http://dx.doi.org/10.1142/s0219720012410107.

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Ribonucleic acid (RNA) molecules play important roles in a variety of biological processes. To properly function, RNA molecules usually have to fold to specific structures, and therefore understanding RNA structure is vital in comprehending how RNA functions. One approach to understanding and predicting biomolecular structure is to use knowledge-based potentials built from experimentally determined structures. These types of potentials have been shown to be effective for predicting both protein and RNA structures, but their utility is limited by their significantly rugged nature. This ruggedne

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Steyn, Trudie. "Creating knowledge-based organisations by means of knowledge management and organisational learning." Acta Academica: Critical views on society, culture and politics 35, no. 1 (2003): 206–28. https://doi.org/10.38140/aa.v35i1.786.

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Knowledge-creating organisations are successful. They have the ability consistently to produce new knowledge, circulate it through their organisations, and embody it in new products and services. Knowledge is a certain source of competitive advantage and the challenge for organisations, therefore, is to share and manage it. This article distinguishes between information, learning and knowledge and describes various models of knowledge management. These models that focus on social construction share common ground with those on learning organisations. For knowledge management to be successful it

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Dolgonosov, Boris M. "Modeling the Knowledge Production Function Based on Bibliometric Information." Knowledge 5, no. 2 (2025): 7. https://doi.org/10.3390/knowledge5020007.

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An integral indicator of the development of society is the amount of knowledge, which can be measured by the number of accumulated publications in the form of patents, articles, and books. Knowledge production is examined on a global scale. We analyze existing econometric models and develop a generalized model that expresses the per capita knowledge production rate (called productivity) as a function of the amount of accumulated knowledge. The function interpolates two extreme cases, the first of which describes an underdeveloped society with very little knowledge and non-zero productivity, an

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Marchesin, Stefano, Giorgio Maria Di Nunzio, and Maristella Agosti. "Simple but Effective Knowledge-Based Query Reformulations for Precision Medicine Retrieval." Information 12, no. 10 (2021): 402. http://dx.doi.org/10.3390/info12100402.

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In Information Retrieval (IR), the semantic gap represents the mismatch between users’ queries and how retrieval models answer to these queries. In this paper, we explore how to use external knowledge resources to enhance bag-of-words representations and reduce the effect of the semantic gap between queries and documents. In this regard, we propose several simple but effective knowledge-based query expansion and reduction techniques, and we evaluate them for the medical domain. The query reformulations proposed are used to increase the probability of retrieving relevant documents through the a

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Charles Chen, Charles Chen, Sai-Sai Shi Charles Chen, and Sheng-Lung Peng Sai-Sai Shi. "A Construction of Knowledge Graph for Semiconductor Industry Chain Based on Lattice-LSTM and PCNN Models." 網際網路技術學刊 25, no. 2 (2024): 313–29. http://dx.doi.org/10.53106/160792642024032502013.

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<p>This paper mainly focuses on building the knowledge graph of semiconductor industry chain. The main research contents include knowledge extraction, knowledge storage, and construction of knowledge graph in semiconductor field. The crawler technology and character recognition technology are used to obtain semiconductor industry chain information from the Internet, magazines, and institutions to establish the original data set. Then, Lattice Long Short-Term Memory (Lattice-LSTM) model is used to implement the entity extraction and recognition. The piecewise convolutional neural network

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Patil, Archana, Shashikant Ghumbre, and Vahida Attar. "Knowledge graph based multi domain Dialogue System using Large Language models." Journal of Integrated Science and Technology 13, no. 6 (2025): 1130. https://doi.org/10.62110/sciencein.jist.2025.v13.1130.

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Recently, with the rapid advancement of Large Language Models (LLMs), various applications such as Natural Language Generation (NLG) have garnered significant attention. NLG has found use across multiple domains, including machine translation, text summarization, and dialogue systems. Dialogue systems facilitate communication between humans and machines, and they are typically classified into task-oriented and open-domain systems. To enable dialogue systems to interact in a human-like manner, large volumes of training data are necessary. However, these systems often lack the reasoning capabili

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Li, Wen, Junfei Xu, and Qi Chen. "Knowledge Distillation-Based Multilingual Code Retrieval." Algorithms 15, no. 1 (2022): 25. http://dx.doi.org/10.3390/a15010025.

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Semantic code retrieval is the task of retrieving relevant codes based on natural language queries. Although it is related to other information retrieval tasks, it needs to bridge the gaps between the language used in the code (which is usually syntax-specific and logic-specific) and the natural language which is more suitable for describing ambiguous concepts and ideas. Existing approaches study code retrieval in a natural language for a specific programming language, however it is unwieldy and often requires a large amount of corpus for each language when dealing with multilingual scenarios.

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Guo, Yufan, Roi Reichart, and Anna Korhonen. "Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents." Transactions of the Association for Computational Linguistics 3 (December 2015): 131–43. http://dx.doi.org/10.1162/tacl_a_00128.

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Inferring the information structure of scientific documents is useful for many NLP applications. Existing approaches to this task require substantial human effort. We propose a framework for constraint learning that reduces human involvement considerably. Our model uses topic models to identify latent topics and their key linguistic features in input documents, induces constraints from this information and maps sentences to their dominant information structure categories through a constrained unsupervised model. When the induced constraints are combined with a fully unsupervised model, the res

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Gao, Shang, and John Krogstie. "Capturing Process Knowledge for Multi-Channel Information Systems." International Journal of Information System Modeling and Design 3, no. 1 (2012): 78–98. http://dx.doi.org/10.4018/jismd.2012010104.

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In this paper, a case study is used to evaluate the business process characterizing modeling (BPCM) language. The BPCM-framework is meant to guide both business stakeholders and model developers during model-based development. The focus of the approach is the use of BPCM as a starting point for capturing process knowledge when planning and developing information system support. Based on information within the BPCM models, goal models and process models can be developed and used for further development of the BPCM model. The approach in this paper is evaluated using a case study related to the

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Sartori, Fabio, and Riccardo Melen. "Wearable expert system development: definitions, models and challenges for the future." Program 51, no. 3 (2017): 235–58. http://dx.doi.org/10.1108/prog-09-2016-0061.

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Purpose A wearable expert system (WES) is an expert system designed and implemented to obtain input from and give outputs to wearable devices. Among its distinguishing features are the direct cooperation between domain experts and users, and the interaction with a knowledge maintenance system devoted to dynamically update the knowledge base taking care of the evolving scenario. The paper aims to discuss these issues. Design/methodology/approach The WES development method is based on the Knowledge Acquisition Framework based on Knowledge Artifact (KAFKA) framework. KAFKA employs multiple knowle

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Atazadeh, Behnam, Abbas Rajabifard, Yibo Zhang, and Maryam Barzegar. "Querying 3D Cadastral Information from BIM Models." ISPRS International Journal of Geo-Information 8, no. 8 (2019): 329. http://dx.doi.org/10.3390/ijgi8080329.

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There has been significant research on the intersection of 3D cadastre and building information modelling (BIM) over the recent years. BIM provides a multidimensional environment for capturing, curating and communicating the physical and functional aspects during a building’s lifecycle. A BIM-based solution for 3D cadastre provides a rich repository of legal and physical datasets in a common environment. The knowledge encapsulated inside a cadastral BIM model should be tapped to unlock the value of 3D cadastral information. Therefore, this article aims to develop BIM-based queries for interrog

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He, Qiang, Shuobo Xu, Zhenfang Zhu, et al. "KRP-DS: A Knowledge Graph-Based Dialogue System with Inference-Aided Prediction." Sensors 23, no. 15 (2023): 6805. http://dx.doi.org/10.3390/s23156805.

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With the popularity of ChatGPT, there has been increasing attention towards dialogue systems. Researchers are dedicated to designing a knowledgeable model that can engage in conversations like humans. Traditional seq2seq dialogue models often suffer from limited performance and the issue of generating safe responses. In recent years, large-scale pretrained language models have demonstrated their powerful capabilities across various domains. Many studies have leveraged these pretrained models for dialogue tasks to address concerns such as safe response generation. Pretrained models can enhance

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Zhang, Pan, Shaowu Shen, Wenping Deng, Shusong Mao, and Yan Wang. "The Construction Model of the TCM Clinical Knowledge Coding Database Based on Knowledge Organization." BioMed Research International 2022 (January 17, 2022): 1–7. http://dx.doi.org/10.1155/2022/2503779.

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Based on the knowledge organization method, this paper explores the construction method of the traditional Chinese medicine (TCM) clinical knowledge coding model by taking TCM clinical electronic medical record data as the research object. Firstly, extracting technology is used to obtain the required data in the electronic medical record. Then, by constructing the clinical knowledge coding model, the tacit knowledge is made explicit, establishing the clinical knowledge base and exploring the connotation of TCM clinical knowledge. It provides necessary data resources for deepening the expressio

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Guan, Kainan, Yang Sun, Guang Yang, and Xinhua Yang. "Knowledge Acquisition and Reasoning Model for Welding Information Integration Based on CNN and Knowledge Graph." Electronics 12, no. 6 (2023): 1275. http://dx.doi.org/10.3390/electronics12061275.

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Knowledge acquisition and reasoning are essential in intelligent welding decisions. However, the challenges of unstructured knowledge acquisition and weak knowledge linkage across phases limit the development of welding intelligence, especially in the integration of domain information engineering. This paper proposes a cognitive model combining image recognition and a knowledge graph. A CNN is used as the perception layer to obtain direct information. Automated logic rules based on a knowledge graph are described to enable information integration in the knowledge reasoning domain. In addition,

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Rubin, Stuart H. "Knowledge-Based Software Prototyping and Reuse." International Journal on Artificial Intelligence Tools 06, no. 01 (1997): 127–47. http://dx.doi.org/10.1142/s0218213097000074.

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Models are executable prototypes. Modeling is closely tied to simulation, which refers to the exercise of a model over a variable parametric space. Model simulations not only provide the engineer with feedback pertaining to the validity of a proposed design, but additionally allow competing designs to be compared on one or more parameters (i.e., sensitivity analysis). Models are defined from a base of several hundred primitive constructs. These constructs can define additional constructs hierarchically. An expertn–system was constructed, which retrieves software for reuse. This expert system i

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Xiaohui, Pan, Zhao Yang, Chen Guang, and Fan Qing. "A knowledge representation based recognition method on power grid alarm monitoring data." Journal of Physics: Conference Series 2253, no. 1 (2022): 012001. http://dx.doi.org/10.1088/1742-6596/2253/1/012001.

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Abstract The identification of power grid monitoring and alarm information is an essential guarantee for the stable operation of the power grid. Identifying current power grid alarms is based on an expert rule system. This method has the problems of cumbersome rule matching, difficulty in maintaining the rule base, and low matching efficiency. In order to solve the above problems, this paper proposes a method of identifying power grid monitoring and warning information based on knowledge representation. The method first uses grid topology information to construct a knowledge graph. It then use

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Zhao, Gui Fen. "The Study on Knowledge Service Models in Digital Library." Advanced Materials Research 143-144 (October 2010): 43–47. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.43.

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The integration of DL and distance education is a significant topic of theoretical and practical research. This paper mainly discusses the principles of knowledge service of DL and four knowledge service models for distance education under the principles, namely, specialized service model based on subject knowledge base, content-based reference service model, personalized custom-made service model, and teamwork service model. The key point of realizing these models is to take distance education users as the center, set up subject information service platform, personalized information push plat

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Rajabi, Enayat, and Somayeh Kafaie. "Knowledge Graphs and Explainable AI in Healthcare." Information 13, no. 10 (2022): 459. http://dx.doi.org/10.3390/info13100459.

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Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This paper highlights the role of knowledge graphs in XAI models in healthcare, considering a state-of-the-art review. Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interac

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Li, Ronghan, Zejun Jiang, Lifang Wang, Xinyu Lu, Meng Zhao, and Daqing Chen. "Enhancing Transformer-based language models with commonsense representations for knowledge-driven machine comprehension." Knowledge-Based Systems 220 (May 2021): 106936. http://dx.doi.org/10.1016/j.knosys.2021.106936.

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Peng, Bo, Feng Jang, Shen Wang, et al. "A Knowledge Distillation Method Based on Evidence Theory to Prevent Catastrophic Forgetting." Journal of Physics: Conference Series 2868, no. 1 (2024): 012023. http://dx.doi.org/10.1088/1742-6596/2868/1/012023.

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Abstract Incremental learning is an emerging machine-learning approach designed to prevent catastrophic forgetting while learning a new task with knowledge retained from previous tasks. However, existing methods often must fully utilize the distributional information of the old task model’s outputs. To alleviate this, we propose a class incremental learning method grounded in evidence theory to leverage the distributional information of outputs from the old task model, which integrates uncertainty estimation into the knowledge distillation process to ensure the new task model effectively learn

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Asal, Burçak, and Ahmet Burak Can. "Ensemble-Based Knowledge Distillation for Video Anomaly Detection." Applied Sciences 14, no. 3 (2024): 1032. http://dx.doi.org/10.3390/app14031032.

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Video anomaly detection has become a vital task for smart video surveillance systems because of its significant potential to minimize the video data to be analyzed by choosing unusual and critical patterns in the scenes. In this paper, we introduce three novel ensemble and knowledge distillation-based adaptive training methods to handle robust detection of different abnormal patterns in video scenes. Our approach leverages the adaptation process by providing information transfer from multiple teacher models with different network structures and further alleviates the catastrophic forgetting is

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Ren, Zhihan, Lijun He, and Peipei Zhu. "Super-Resolution Learning Strategy Based on Expert Knowledge Supervision." Remote Sensing 16, no. 16 (2024): 2888. http://dx.doi.org/10.3390/rs16162888.

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Existing Super-Resolution (SR) methods are typically trained using bicubic degradation simulations, resulting in unsatisfactory results when applied to remote sensing images that contain a wide variety of object shapes and sizes. The insufficient learning approach reduces the focus of models on critical object regions within the images. As a result, their practical performance is significantly hindered, especially in real-world applications where accuracy in object reconstruction is crucial. In this work, we propose a general learning strategy for SR models based on expert knowledge supervisio

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Liu, Ying, Peng Wang, Di Yang, and Ningjia Qiu. "A knowledge graph embedding model based attention mechanism for enhanced node information integration." PeerJ Computer Science 10 (January 22, 2024): e1808. http://dx.doi.org/10.7717/peerj-cs.1808.

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The purpose of knowledge embedding is to extract entities and relations from the knowledge graph into low-dimensional dense vectors, in order to be applied to downstream tasks, such as connection prediction and intelligent classification. Existing knowledge embedding methods still have many limitations, such as the contradiction between the vast amount of data and limited computing power, and the challenge of effectively representing rare entities. This article proposed a knowledge embedding learning model, which incorporates a graph attention mechanism to integrate key node information. It ca

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Andrus, Berkeley R., Yeganeh Nasiri, Shilong Cui, Benjamin Cullen, and Nancy Fulda. "Enhanced Story Comprehension for Large Language Models through Dynamic Document-Based Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 10436–44. http://dx.doi.org/10.1609/aaai.v36i10.21286.

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Large transformer-based language models have achieved incredible success at various tasks which require narrative comprehension, including story completion, answering questions about stories, and generating stories ex nihilo. However, due to the limitations of finite context windows, these language models struggle to produce or understand stories longer than several thousand tokens. In order to mitigate the document length limitations that come with finite context windows, we introduce a novel architecture that augments story processing with an external dynamic knowledge graph. In contrast to

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Heidary Dahooie, Jalil, Abbas Afrazeh, Seyed Mohammad Hosseini, and Mohamad Reza Ghezel Arsalan. "Knowledge work difficulty factors: An empirical study based on different groups of knowledge workers." South African Journal of Economic and Management Sciences 15, no. 1 (2012): 1–15. http://dx.doi.org/10.4102/sajems.v15i1.118.

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The determination of the difficulty factor in knowledge work can be important for improving the performance of knowledge workers. In this article a regression model for investigating the difficulty of knowledge based activities (KBAs) is proposed. Four factors are considered in the model: Uncertainty, Variability of information, Amount of information and Level of skill and expertise. An empirical study based on 119 jobs from three different groups of knowledge workers (i.e. managerial, professional and clerical) shows that there are significant differences between the difficulty of the KBAs in

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Shi, Ming, Jing Zhao, and Donglin Wu. "Convolutional Neural Network Knowledge Graph Link Prediction Model Based on Relational Memory." Computational Intelligence and Neuroscience 2023 (January 31, 2023): 1–9. http://dx.doi.org/10.1155/2023/3909697.

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A knowledge graph is a collection of fact triples, a semantic network composed of nodes and edges. Link prediction from knowledge graphs is used to reason about missing parts of triples. Common knowledge graph link prediction models include translation models, semantics matching models, and neural network models. However, the translation models and semantic matching models have relatively simple structures and poor expressiveness. The neural network model can easily ignore the overall structural characteristics of triples and cannot capture the links between entities and relations in low-dimen

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Fan, Cunhang, Yujie Chen, Jun Xue, Yonghui Kong, Jianhua Tao, and Zhao Lv. "Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8380–88. http://dx.doi.org/10.1609/aaai.v38i8.28680.

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In recent years, knowledge graph completion (KGC) models based on pre-trained language model (PLM) have shown promising results. However, the large number of parameters and high computational cost of PLM models pose challenges for their application in downstream tasks. This paper proposes a progressive distillation method based on masked generation features for KGC task, aiming to significantly reduce the complexity of pre-trained models. Specifically, we perform pre-distillation on PLM to obtain high-quality teacher models, and compress the PLM network to obtain multi-grade student models. Ho

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Ivanova, Tatyana, and Petya Petkova. "Semantic knowledge models of non-crisp knowledge." International Journal on Information Technologies and Security 17, no. 2 (2025): 65–76. https://doi.org/10.59035/ismv6176.

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Many practical applications, such as medical diagnosis, business decision-making, information searching and retrieval, etc., require usage of uncertain or ambiguous knowledge. Classical ontology-based technologies can represent and reasoning only with cri Several fuzzy or probabilistic extensions of classical description logics and languages for semantic knowledge representation have been proposed recently, but high reasoning complexity of its decision procedures make difficult its usage in real applications. It is of great importance to select the knowledge representation technology, ensuring

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Tan, Zhen, Xiang Zhao, Yang Fang, Bin Ge, and Weidong Xiao. "Knowledge Graph Representation via Similarity-Based Embedding." Scientific Programming 2018 (July 15, 2018): 1–12. http://dx.doi.org/10.1155/2018/6325635.

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Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict the missing facts. Among various solutions, almost all embedding models have high time and memory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs. Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features

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