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Journal articles on the topic 'Vectorial embeddings'

Author: Grafiati

Published: 25 May 2024

Last updated: 19 July 2025

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1

Rydhe, Eskil. "Vectorial Hankel operators, Carleson embeddings, and notions of BMOA." Geometric and Functional Analysis 27, no. 2 (2017): 427–51. http://dx.doi.org/10.1007/s00039-017-0400-4.

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Ion, Radu, Vasile Păiș, Verginica Barbu Mititelu, et al. "Unsupervised Word Sense Disambiguation Using Transformer’s Attention Mechanism." Machine Learning and Knowledge Extraction 7, no. 1 (2025): 10. https://doi.org/10.3390/make7010010.

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Transformer models produce advanced text representations that have been used to break through the hard challenge of natural language understanding. Using the Transformer’s attention mechanism, which acts as a language learning memory, trained on tens of billions of words, a word sense disambiguation (WSD) algorithm can now construct a more faithful vectorial representation of the context of a word to be disambiguated. Working with a set of 34 lemmas of nouns, verbs, adjectives and adverbs selected from the National Reference Corpus of Romanian (CoRoLa), we show that using BERT’s attention head

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Podda, Marco, Castrense Savojardo, Pier Luigi Martelli, et al. "A descriptor-free machine learning framework to improve antigen discovery for bacterial pathogens." PLOS One 20, no. 6 (2025): e0323895. https://doi.org/10.1371/journal.pone.0323895.

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Identifying protective antigens (PAs), i.e., targets for bacterial vaccines, is challenging as conducting in-vivo tests at the proteome scale is impractical. Reverse Vaccinology (RV) aids in narrowing down the pool of candidates through computational screening of proteomes. Within RV, one prominent approach is to train Machine Learning (ML) models to classify PAs. These models can be used to predict unseen protein sequences and assist researchers in selecting promising candidates. Traditionally, proteins are fed into these models as vectors of biological and physico-chemical descriptors derive

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Szymański, Piotr. "A broadband multistate interferometer for impedance measurement." Journal of Telecommunications and Information Technology, no. 2 (June 30, 2005): 29–33. http://dx.doi.org/10.26636/jtit.2005.2.311.

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We present a new four-state interferometer for measuring vectorial reflection coefficient from 50 to 1800 MHz. The interferometer is composed of a four-state phase shifter, a double-directional coupler and a spectrum analyzer with an in-built tracking generator. We describe a design of the interferometer and methods developed for its calibration and de-embedding the measurements. Experimental data verify good accuracy of the impedance measurement.

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Hammer, Barbara, and Alexander Hasenfuss. "Topographic Mapping of Large Dissimilarity Data Sets." Neural Computation 22, no. 9 (2010): 2229–84. http://dx.doi.org/10.1162/neco_a_00012.

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Topographic maps such as the self-organizing map (SOM) or neural gas (NG) constitute powerful data mining techniques that allow simultaneously clustering data and inferring their topological structure, such that additional features, for example, browsing, become available. Both methods have been introduced for vectorial data sets; they require a classical feature encoding of information. Often data are available in the form of pairwise distances only, such as arise from a kernel matrix, a graph, or some general dissimilarity measure. In such cases, NG and SOM cannot be applied directly. In thi

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RIESEN, KASPAR, and HORST BUNKE. "GRAPH CLASSIFICATION BASED ON VECTOR SPACE EMBEDDING." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 06 (2009): 1053–81. http://dx.doi.org/10.1142/s021800140900748x.

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Graphs provide us with a powerful and flexible representation formalism for pattern classification. Many classification algorithms have been proposed in the literature. However, the vast majority of these algorithms rely on vectorial data descriptions and cannot directly be applied to graphs. Recently, a growing interest in graph kernel methods can be observed. Graph kernels aim at bridging the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. In the present paper, we propos

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Ji, Jiayi, Yunpeng Luo, Xiaoshuai Sun, et al. "Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1655–63. http://dx.doi.org/10.1609/aaai.v35i2.16258.

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Transformer-based architectures have shown great success in image captioning, where object regions are encoded and then attended into the vectorial representations to guide the caption decoding. However, such vectorial representations only contain region-level information without considering the global information reflecting the entire image, which fails to expand the capability of complex multi-modal reasoning in image captioning. In this paper, we introduce a Global Enhanced Transformer (termed GET) to enable the extraction of a more comprehensive global representation, and then adaptively g

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Zhu, Huiming, Chunhui He, Yang Fang, Bin Ge, Meng Xing, and Weidong Xiao. "Patent Automatic Classification Based on Symmetric Hierarchical Convolution Neural Network." Symmetry 12, no. 2 (2020): 186. http://dx.doi.org/10.3390/sym12020186.

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With the rapid growth of patent applications, it has become an urgent problem to automatically classify the accepted patent application documents accurately and quickly. Most previous patent automatic classification studies are based on feature engineering and traditional machine learning methods like SVM, and some even rely on the knowledge of domain experts, hence they suffer from low accuracy problem and have poor generalization ability. In this paper, we propose a patent automatic classification method via the symmetric hierarchical convolution neural network (CNN) named PAC-HCNN. We use t

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Dutta, Anjan, Pau Riba, Josep Lladós, and Alicia Fornés. "Hierarchical stochastic graphlet embedding for graph-based pattern recognition." Neural Computing and Applications 32, no. 15 (2019): 11579–96. http://dx.doi.org/10.1007/s00521-019-04642-7.

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AbstractDespite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of infor

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Szemenyei, Márton, and Ferenc Vajda. "3D Object Detection and Scene Optimization for Tangible Augmented Reality." Periodica Polytechnica Electrical Engineering and Computer Science 62, no. 2 (2018): 25–37. http://dx.doi.org/10.3311/ppee.10482.

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Object recognition in 3D scenes is one of the fundamental tasks in computer vision. It is used frequently in robotics or augmented reality applications [1]. In our work we intend to apply 3D shape recognition to create a Tangible Augmented Reality system that is able to pair virtual and real objects in natural indoors scenes. In this paper we present a method for arranging virtual objects in a real-world scene based on primitive shape graphs. For our scheme, we propose a graph node embedding algorithm for graphs with vectorial nodes and edges, and genetic operators designed to improve the qual

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Shrock, R. "Recent results on renormalization-group evolution of theories with gauge, fermion, and scalar fields." International Journal of Modern Physics A 32, no. 35 (2017): 1747007. http://dx.doi.org/10.1142/s0217751x17470078.

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We discuss recent results on renormalization-group evolution of several types of theories. First, we consider asymptotically free vectorial gauge theories with various fermion contents and discuss higher-loop calculations of the UV to IR evolution in these theories, including an IR zero of the beta function and the value of the anomalous dimension [Formula: see text] at this point, together with comparisons with lattice measurements. Effects of scheme transformations are discussed. We then present a novel way to determine the value of [Formula: see text] in an [Formula: see text] technicolor m

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Merma Yucra, Jhan Piero Paulo, David Juan Cerezo Quina, German Alberto Echaiz Espinoza, Manuel Alejandro Valderrama Solis, Daniel Domingo Yanyachi Aco Cardenas, and Andrés Ortiz Salazar. "Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological Variables." Sensors 25, no. 12 (2025): 3601. https://doi.org/10.3390/s25123601.

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The use of recurrent neural networks has proven effective in time series prediction tasks such as weather. However, their use in resource-limited systems such as MCUs presents difficulties in terms of both size and stability with longer prediction windows. In this context, we propose a variant of the LSTM model, which we call SE-LSTM (Single Embedding LSTM), which uses embedding techniques to vectorially represent seasonality and latent patterns through variables such as temperature and humidity. The proposal is systematically compared in two parts: The first compares it against other referenc

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MACHET, B. "COMMENTS ON THE STANDARD MODEL OF ELECTROWEAK INTERACTIONS." International Journal of Modern Physics A 11, no. 01 (1996): 29–63. http://dx.doi.org/10.1142/s0217751x96000031.

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The Standard Model of electroweak interactions is shown to include a gauge theory for the observed scalar and pseudoscalar mesons. This is done by exploiting the consequences of embedding the SU(2)L×U(1) group into the chiral group of strong interactions and by explicitly considering as composite the Higgs boson and its three companions inside the standard scalar four-plet. No extra scale of interaction is needed. Quantizing by the Feynman path integral reveals how, in the “Nambu-Jona-Lasinio approximation,” the quarks and the Higgs boson become unobservable, and the theory anomaly-free. Never

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GÜZEL, Mehmet, Hakan ERTEN, and Erkan BOSTANCİ. "GENERATING TURKISH LYRICS WITH LONG SHORT TERM MEMORY." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62, no. 1 (2020): 71–78. http://dx.doi.org/10.33769/aupse.584380.

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Long Short Term Memory (LSTM) has gained a serious achievement on sequential data which have been used generally videos, text and time-series. In this paper, we aim for generating lyrics with newly created “Turkish Lyrics” dataset. By this time, there have been studies for creating Turkish Lyrics with character-level. Unlike previous studies, we propose to Turkish Lyrics generator working with word-level instead on character-level. Also, for employing LSTM, we can’t send the words as string and words must be vectorized. To vectorize, we tried two ways for encoding the words that are used in da

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Basaad , Abdullah, Shadi Basurra , Edlira Vakaj , Ahmed Karam Eldaly , and Mohammed M. Abdelsamea . "A BERT-GNN Approach for Metastatic Breast Cancer Prediction Using Histopathology Reports." Diagnostics 14, no. 13 (2024): 1365. http://dx.doi.org/10.3390/diagnostics14131365.

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Metastatic breast cancer (MBC) continues to be a leading cause of cancer-related deaths among women. This work introduces an innovative non-invasive breast cancer classification model designed to improve the identification of cancer metastases. While this study marks the initial exploration into predicting MBC, additional investigations are essential to validate the occurrence of MBC. Our approach combines the strengths of large language models (LLMs), specifically the bidirectional encoder representations from transformers (BERT) model, with the powerful capabilities of graph neural networks

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Winahyu, Sri Kusuma, Fawwaz Zaini Ahmad, Achril Zalmansyah, et al. "Sentence Classification Using Machine Learning and Word Embedding: An Innovation in Indonesian Language Learning." Journal of Language Teaching and Research 16, no. 4 (2025): 1225–39. https://doi.org/10.17507/jltr.1604.17.

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In applied linguistics, writing assessment examines language learning. There are various genres in writing, but the evaluation always includes a syntactic component or sentence structure. This research focuses on classifying sentence structure in the Indonesian language using the Random Forest Classifier algorithm on five different experiment models, which are trained using different vectorization techniques, including bag of word (BoW), hashing, Term Frequency-Inverse Document Frequency (TF-IDF), CBoW, and skipgram vectorizers. The results showed that the accuracy of the models varied signifi

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Olotu, Samuel Ibukun, and Oladunni Abosede Daramola. "Hybrid spam message detection using convolutional neural network and long short-term memory techniques." Applied and Computational Engineering 2, no. 1 (2023): 265–75. http://dx.doi.org/10.54254/2755-2721/2/20220601.

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Short Message Service (SMS) is a feature of a mobile phone that enable convenient and instant way of sending electronic messages between users. As SMS usage increases fraudulent text messages, known as spam, are becoming more common. Spam SMS may result in leaking personal information, invasion of privacy or accessing unauthorized data from mobile devices. Users of mobile phones can mistakingly give away personal information with the assumption that they are sharing it with the right recipients. This work propose a SMS spam detection method that combines convolutional neural network (CNN) and

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Mutinda, James, Waweru Mwangi, and George Okeyo. "Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network." Applied Sciences 13, no. 3 (2023): 1445. http://dx.doi.org/10.3390/app13031445.

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Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications, such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques that can convert words into precise vectors that represent the input text. There are two categories of text representation techniques: lexicon-based techniques and machine learning-based techniques. From research, both techniques have limitations. For instance, pre-trained wo

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Faraz, Anum, Fardin Ahsan, Jinane Mounsef, Ioannis Karamitsos, and Andreas Kanavos. "Enhancing Child Safety in Online Gaming: The Development and Application of Protectbot, an AI-Powered Chatbot Framework." Information 15, no. 4 (2024): 233. http://dx.doi.org/10.3390/info15040233.

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This study introduces Protectbot, an innovative chatbot framework designed to improve safety in children’s online gaming environments. At its core, Protectbot incorporates DialoGPT, a conversational Artificial Intelligence (AI) model rooted in Generative Pre-trained Transformer 2 (GPT-2) technology, engineered to simulate human-like interactions within gaming chat rooms. The framework is distinguished by a robust text classification strategy, rigorously trained on the Publicly Available Natural 2012 (PAN12) dataset, aimed at identifying and mitigating potential sexual predatory behaviors throu

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Roth, Jan-Philipp, Thomas Kühler, and Elmar Griese. "Utilizing multimode interference effects in integrated graded-index optical waveguides for efficient power splitting." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 4 (2018): 1556–63. http://dx.doi.org/10.1108/compel-09-2017-0374.

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Purpose For the realization of optical waveguide components, needed for photonic integrated circuits, multimode-interference based (MMI-based) devices are an excellent component class for the realization of low loss optical splitters. A promising approach to the manufacturing of these components is their embedding in thin glass sheets by ion-exchange diffusion processes, which has not yet been extensively studied. This study aims to significantly enhance the modeling of the diffusion process to support manufacturing of graded-index, MMI-based optical splitters. Design/methodology/approach The

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Liu, Xinda, and Lili Wang. "Multi-granularity sequence generation for hierarchical image classification." Computational Visual Media 10, no. 2 (2024): 243–60. http://dx.doi.org/10.1007/s41095-022-0332-2.

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AbstractHierarchical multi-granularity image classification is a challenging task that aims to tag each given image with multiple granularity labels simultaneously. Existing methods tend to overlook that different image regions contribute differently to label prediction at different granularities, and also insufficiently consider relationships between the hierarchical multi-granularity labels. We introduce a sequence-to-sequence mechanism to overcome these two problems and propose a multi-granularity sequence generation (MGSG) approach for the hierarchical multi-granularity image classificatio

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Goldstein, Ariel, Avigail Grinstein-Dabush, Mariano Schain, et al. "Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-46631-y.

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AbstractContextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. We hypothesize that language areas in the human brain, similar to DLMs, rely on a continuous embedding space to represent language. To test this hypothesis, we densely record the neural activity patterns in the inferior frontal gyrus (IFG) of three participants using dense intracranial arrays while they listened to a 30-minute podcast. From these

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Chersoni, Emmanuele, Enrico Santus, Chu-Ren Huang, and Alessandro Lenci. "Decoding Word Embeddings with Brain-Based Semantic Features." Computational Linguistics, August 26, 2021, 1–36. http://dx.doi.org/10.1162/coli_a_00412.

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Abstract Word embeddings are vectorial semantic representations built with either counting or predicting techniques aimed at capturing shades of meaning from word co-occurrences. Since their introduction, these representations have been criticized for lacking interpretable dimensions. This property of word embeddings limits our understanding of the semantic features they actually encode. Moreover, it contributes to the “black box” nature of the tasks in which they are used, since the reasons for word embedding performance often remain opaque to humans. In this contribution, we explore the sema

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le Gorrec, Luce, Philip A. Knight, and Auguste Caen. "Learning network embeddings using small graphlets." Social Network Analysis and Mining 12, no. 1 (2021). http://dx.doi.org/10.1007/s13278-021-00846-9.

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AbstractTechniques for learning vectorial representations of graphs (graph embeddings) have recently emerged as an effective approach to facilitate machine learning on graphs. Some of the most popular methods involve sophisticated features such as graph kernels or convolutional networks. In this work, we introduce two straightforward supervised learning algorithms based on small-size graphlet counts, combined with a dimension reduction step. The first relies on a classic feature extraction method powered by principal component analysis (PCA). The second is a feature selection procedure also ba

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Xenos, A., N. Malod-Dognin, S. Milinković, and N. Pržulj. "Linear functional organization of the omic embedding space." Bioinformatics, July 2, 2021. http://dx.doi.org/10.1093/bioinformatics/btab487.

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Abstract Motivation We are increasingly accumulating complex omics data that capture different aspects of cellular functioning. A key challenge is to untangle their complexity and effectively mine them for new biomedical information. To decipher this new information, we introduce algorithms based on network embeddings. Such algorithms represent biological macromolecules as vectors in d-dimensional space, in which topologically similar molecules are embedded close in space and knowledge is extracted directly by vector operations. Recently, it has been shown that neural networks used to obtain v

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Rullo, Antonino, Farhana Alam, and Edoardo Serra. "Trace Encoding Techniques for Multi‐Perspective Process Mining: A Comparative Study." WIREs Data Mining and Knowledge Discovery, December 10, 2024. https://doi.org/10.1002/widm.1573.

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ABSTRACTProcess mining (PM) comprises a variety of methods for discovering information about processes from their execution logs. Some of them, such as trace clustering, trace classification, and anomalous trace detection require a preliminary preprocessing step in which the raw data is encoded into a numerical feature space. To this end, encoding techniques are used to generate vectorial representations of process traces. Most of the PM literature provides trace encoding techniques that look at the control flow, that is, only encode the sequence of activities that characterize a process trace

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Zhang, Fei, Bo Sun, Xiaolin Diao, Wei Zhao, and Ting Shu. "Prediction of adverse drug reactions based on knowledge graph embedding." BMC Medical Informatics and Decision Making 21, no. 1 (2021). http://dx.doi.org/10.1186/s12911-021-01402-3.

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Abstract Background Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data. Method Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a log

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Alonso-Álvarez, Gonzalo, and James M. Cline. "Gauging lepton flavor SU(3) for the muon g − 2." Journal of High Energy Physics 2022, no. 3 (2022). http://dx.doi.org/10.1007/jhep03(2022)042.

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Abstract Gauging a specific difference of lepton numbers such as Lμ− Lτ is a popular model-building option, which gives rise to economical explanations for the muon anomalous magnetic moment. However, this choice of gauge group seems rather arbitrary, and additional physics is required to reproduce the observed neutrino masses and mixings. We address these shortcomings by embedding Lμ− Lτ in the vectorial SU(3) gauge symmetry of lepton flavor. The vacuum expectation values (VEVs) of scalar fields in the fundamental, six-dimensional and adjoint representations allow for phenomenologically viabl

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Biringa, Chidera, and Gokhan Kul. "Detecting Hard-Coded Credentials in Software Repositories via LLMs." Digital Threats: Research and Practice, July 7, 2025. https://doi.org/10.1145/3744756.

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Software developers frequently hard-code credentials such as passwords, generic secrets, private keys, and generic tokens in software repositories, even though it is strictly advised against due to the severe threat to the security of the software. These credentials create attack surfaces exploitable by a potential adversary to conduct malicious exploits such as backdoor attacks. Recent detection efforts utilize embedding models to vectorize textual credentials before passing them to classifiers for predictions. However, these models struggle to discriminate between credentials with contextual

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Wu, Shujiao, Lingling Luo, Houtian Luo, et al. "Combining Protein Phase Separation and Bio‐orthogonal Linking to Coimmobilize Enzymes for Cascade Biocatalysis." Small, August 12, 2024. http://dx.doi.org/10.1002/smll.202404018.

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AbstractThe designed and ordered co‐immobilization of multiple enzymes for vectorial biocatalysis is challenging. Here, a combination of protein phase separation and bioorthogonal linking is used to generate a zeolitic imidazole framework (ZIF‐8) containing co‐immobilized enzymes. Zn2+ ions induce the clustering of minimal protein modules, such as 6‐His tag, proline‐rich motif (PRM) and SRC homology 3 (SH3) domains, and allow for phase separation of the coupled aldoketoreductase (AKR) and alcohol dehydrogenase (ADH) at low concentrations. This is achieved by fusing SpyCatcher and PRM‐SH3‐6His

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Chen, Ji, and Pengtao Cui. "The Application of Deep Learning in Sports Competition Data Prediction." Scalable Computing: Practice and Experience 25, no. 6 (2024). http://dx.doi.org/10.12694/scpe.v25i6.3307.

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In order to predict sports competition data, the author needs to implement the structure and related processes of the relevant competition victory and defeat prediction system, and specifically introduce and plan the implementation of each functional module. The data collection and storage module adopts Alibaba Cloud servers and combines Python to remotely and automatically collect data on a scheduled basis, according to the actual situation of game wins and losses, data cleaning and filtering are carried out, and multiple encoding forms are used to vectorize the data in order to find the best

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Peñafiel-Saiz, Carmen, Jordi Morales-i-Gras, and Lázaro Echegaray-Eizaguirre. "Las imágenes como recurso fundamental de la información durante la covid-19 y la fase de vacunación en medios digitales españoles." Revista de Comunicación, January 31, 2024. http://dx.doi.org/10.26441/rc23.1-2024-3427.

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El estudio tiene como objetivo caracterizar las imágenes que acompañan a las informaciones de la pandemia de la covid-19, la vacunación y los tratamientos contra el coronavirus en los medios de comunicación digitales: ABC, Deia, EITB.eus, El Correo, elDiario.es, El Mundo, La Razón, La Vanguardia, Naiz y Público (2020-2022). Se ha trabajado con una muestra de 15.654 imágenes únicas, sobre las que se ha procedido a identificar 15 clústeres con técnicas de Inteligencia Artificial, entre las que consta el algoritmo Inception V3ylas incrustaciones en espacios vectoriales o embeddings. Se opta por u

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Chen, Zhao, Yin Jiang, Xiaoyu Zhang, et al. "ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18." Briefings in Bioinformatics 23, no. 1 (2021). http://dx.doi.org/10.1093/bib/bbab503.

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Abstract Drug-induced liver injury (DILI) has always been the focus of clinicians and drug researchers. How to improve the performance of the DILI prediction model to accurately predict liver injury was an urgent problem for researchers in the field of medical research. In order to solve this scientific problem, this research collected a comprehensive and accurate dataset of DILI with high recognition and high quality based on clinically confirmed DILI compound datasets, including 1446 chemical compounds. Then, the residual neural network with 18-layer by using more 5-layer blocks (ResNet18) w

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Ouyang, Ningjing. "Analyze IMDb movies by sentiment and topic analysis." Environment and Social Psychology 8, no. 3 (2023). http://dx.doi.org/10.54517/esp.v8i3.1958.

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Movie is an important cultural form, carrying multiple levels and meanings such as art, entertainment and social value. Movie review and rating data sets are huge, and deep learning and natural language processing methods are widely used today. Advances in big data and deep learning offer unprecedented opportunities to understand moviegoer behavior and preferences while providing a cost-effective way to gain insights relevant to the entertainment industry. This project conducts sentiment analysis, topic modeling, and visual statistical analysis based on the IMDb movie data set to identify key

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Matougui, Brahim, Abdelbasset Boukelia, Hacene Belhadef, Clovis Galiez, and Mohamed Batouche. "NLP-MeTaxa: A Natural Language Processing approach for Metagenomic Taxonomic Binning based on deep learning." Current Bioinformatics 16 (June 21, 2021). http://dx.doi.org/10.2174/1574893616666210621101150.

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Background: Metagenomics is the study of genomic content in mass from an environment of interest such as the human gut or soil. Taxonomy is one of the most important fields of metagenomics, which is the science of defining and naming groups of microbial organisms that share the same characteristics. The problem of taxonomy classification is the identification and quantification of microbial species or higher-level taxa sampled by high throughput sequencing. Objective: Although many methods exist to deal with the taxonomic classification problem, assignment to low taxonomic ranks remains an imp

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Xu, Liang, Lu Lu, Minglu Liu, Chengxuan Song, and Lizhen Wu. "Nanjing Yunjin intelligent question-answering system based on knowledge graphs and retrieval augmented generation technology." Heritage Science 12, no. 1 (2024). http://dx.doi.org/10.1186/s40494-024-01231-3.

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AbstractNanjing Yunjin, a traditional Chinese silk weaving craft, is celebrated globally for its unique local characteristics and exquisite workmanship, forming an integral part of the world's intangible cultural heritage. However, with the advancement of information technology, the experiential knowledge of the Nanjing Yunjin production process is predominantly stored in text format. As a highly specialized and vertical domain, this information is not readily convert into usable data. Previous studies on a knowledge graph-based Nanjing Yunjin Question-Answering System have partially addressed

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