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Journal articles on the topic 'Knowledge graph profiling'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Munir, Siraj, Syed Imran Jami, and Shaukat Wasi. "Towards the Modelling of Veillance based Citizen Profiling using Knowledge Graphs." Open Computer Science 11, no. 1 (2021): 294–304. http://dx.doi.org/10.1515/comp-2020-0209.

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Abstract In this work we have proposed a model for Citizen Profiling. It uses veillance (Surveillance and Sousveillance) for data acquisition. For representation of Citizen Profile Temporal Knowledge Graph has been used through which we can answer semantic queries. Previously, most of the work lacks representation of Citizen Profile and have used surveillance for data acquisition. Our contribution is towards enriching the data acquisition process by adding sousveillance mechanism and facilitating semantic queries through representation of Citizen Profiles using Temporal Knowledge Graphs. Our p

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Sai, Kiran Reddy Malikireddy, Algubelli Bipinkumarreddy, and Tadanki Snigdha. "Knowledge Graph-Driven Real-Time Data Engineering for Context-Aware Machine Learning Pipelines." European Journal of Advances in Engineering and Technology 8, no. 5 (2021): 65–76. https://doi.org/10.5281/zenodo.14600600.

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The novel context-aware machine learning is based on state-of-the-art real-time data engineering processes that operate in shifting entity correlations. To this end, this paper presents a new architecture that combines knowledge graph construction with real-time stream processing to underpin the machine learning flow in a context-aware manner. The proposed system uses graph neural networks (GNNs) for updates and embeddings in real-time for dynamic integration of contextual information into the other machine learning models. This makes the approach ideal as changes in the relations of entities

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Nakib, Arman Mohammad, Prottoy Khan, Md Mahib Ullah, Md Labib Kawser,, A. K. M. Jayed, and Sazzad Kadir Zim. "Harnessing Advanced NLP Techniques for Automated Personality Analysis and Future Behavior Prediction from Social Media Posts." Middle East Research Journal of Engineering and Technology 4, no. 04 (2024): 98–106. https://doi.org/10.36348/merjet.2024.v04i04.001.

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This work offers an integrated multitool approach that relies on state-of-the-art NLP methods for real-time text analysis, specifically in sentiment analysis, personality profiling, and knowledge graph construction. The pipeline uses abstractive summarization skills from PEGASUS model to condense long inputs from the users. That is followed by a sentiment analysis process that applies BERTs to classify the summarized text’s emotional sentiment as either positive, negative, or neutral. The framework also derives personality traits from emotion and expects probable future behaviors by mapping th

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Wu, Yang-Han, Yu-An Huang, Jian-Qiang Li, et al. "Knowledge graph embedding for profiling the interaction between transcription factors and their target genes." PLOS Computational Biology 19, no. 6 (2023): e1011207. http://dx.doi.org/10.1371/journal.pcbi.1011207.

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Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learnin

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Gao, Hao, Yongqing Wang, Jiangli Shao, Huawei Shen, and Xueqi Cheng. "User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function." Entropy 24, no. 11 (2022): 1603. http://dx.doi.org/10.3390/e24111603.

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Users participate in multiple social networks for different services. User identity linkage aims to predict whether users across different social networks refer to the same person, and it has received significant attention for downstream tasks such as recommendation and user profiling. Recently, researchers proposed measuring the relevance of user-generated content to predict identity linkages of users. However, there are two challenging problems with existing content-based methods: first, barely considering the word similarities of texts is insufficient where the semantical correlations of na

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Meng, Lingwen, Yulin Wang, Guobang Ban, Yuanjun Huang, Xinshan Zhu, and Shumei Zhang. "A Multi-Source Embedding-Based Named Entity Recognition Model for Knowledge Graph and Its Application to On-Site Operation Violations in Power Grid Systems." Electronics 14, no. 13 (2025): 2511. https://doi.org/10.3390/electronics14132511.

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With the increasing complexity of power grid field operations, frequent operational violations have emerged as a major concern in the domain of power grid field operation safety. To support dispatchers in accurately identifying and addressing violation risks, this paper introduces a profiling approach for power grid field operation violations based on knowledge graph techniques. The method enables deep modeling and structured representation of violation behaviors. In the structured data processing phase, statistical analysis is conducted based on predefined rules, and mutual information is emp

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Du, Hongyan, Dejun Jiang, Junbo Gao, et al. "Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network." Research 2022 (July 22, 2022): 1–15. http://dx.doi.org/10.34133/2022/9873564.

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Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in

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Yuan, Zixuan, Hao Liu, Renjun Hu, Denghui Zhang, and Hui Xiong. "Self-Supervised Prototype Representation Learning for Event-Based Corporate Profiling." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4644–52. http://dx.doi.org/10.1609/aaai.v35i5.16594.

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Event-based corporate profiling aims to assess the evolving operational status of the corresponding corporate from its event sequence. Existing studies on corporate profiling have partially addressed the problem via (i) case-by-case empirical analysis by leveraging traditional financial methods, or (ii) the automatic profile inference by reformulating the problem into a supervised learning task. However, both approaches heavily rely on domain knowledge and are labor-intensive. More importantly, the task-specific nature of both approaches prevents the obtained corporate profiles from being appl

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Sejal, Mishra, and Shukla Abhinav. "Use of Graph Technology to Identify Criminal Activity Using Call Data Record." ACCST RESEARCH JOURNAL XXI, no. 1, January 2023 (2023): 31–39. https://doi.org/10.5281/zenodo.7896257.

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              <em>Crime is a global issue and the first move must be to control it. For a nation to experience healthy, long-term growth, it is essential. We are well aware of the challenges in identifying the criminal domains in the digital world that are constantly influenced by their misdeeds. To stay up with crimes, offenders, and their tactics, police forces across the globe pace themselves continually. The difficulty of sifting through a large amount of data on crimes and criminals has grown significantly for the police department labor force. There is

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Li, Zhuliu, Tianci Song, Jeongsik Yong, and Rui Kuang. "Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion." PLOS Computational Biology 17, no. 4 (2021): e1008218. http://dx.doi.org/10.1371/journal.pcbi.1008218.

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High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sp

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Usachov, Volodymyr, and Ihor Shubin. "A structural-functional model of learning in computerized learning systems." INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, no. 1(31) (March 31, 2025): 127–42. https://doi.org/10.30837/2522-9818.2025.1.127.

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The subject matter of the article is the development of a universal structural-functional model of computerized learning systems that integrates learner, learning, and explanation models. This model addresses the task of personalizing the learning process, considering the individual characteristics of the learner, and ensuring long-term knowledge retention. The goal of the work is to develop a universal structural-functional learning system model that combines modern adaptive algorithms, integrates psychological and cognitive aspects, and introduces new approaches to long-term knowledge retent

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Mežnar, Sebastian, Nada Lavrač, and Blaž Škrlj. "SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations." IEEE Access 8 (December 9, 2020): 212568–88. https://doi.org/10.1109/ACCESS.2020.3039541.

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Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations. However, state-of-the-art methods are not necessarily interpretable and are therefore not fully applicable to sensitive settings in biomedical or user profiling tasks, where explicit bias detection is highly relevant. The proposed SNoRe (Symbolic Node Representations) algorithm is capable of learning symbolic, human-understandable representations of individual network nodes, based on the similarity of neighborhood hashes which serv

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Putri, Ne, Mi Diana, and Ke Lee. "The Flooding issues are facing by small-scale rice farmers in South Sumatra, Indonesia." IOP Conference Series: Earth and Environmental Science 1477, no. 1 (2025): 012032. https://doi.org/10.1088/1755-1315/1477/1/012032.

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Abstract Flood become most vurnerable climate change hazard in Indonesia and, most frequent climate change impact in Indonesia. This affecting rice farmers’ ability to maintain their sustainable livelihood, it causes harvest loss, fluctuation of productivity and loss of income for their livelihood. The limited facilities of farming because there no budget from the government to build farming infrastructure in hitterland were the small-scale farmers’ in, with lower income and lower knowledge. This study aims are to identify the issues of flood are facing by small scale farmers in South Sumatra

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Zhang, Xiang, Qingqing Yang, Jinru Ding, and Ziyue Wang. "Entity Profiling in Knowledge Graphs." IEEE Access 8 (2020): 27257–66. http://dx.doi.org/10.1109/access.2020.2971567.

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Amir, Muhammad Bilal, Yan Shi, Hehe Cao, et al. "Short Neuropeptide F and Its Receptor Regulate Feeding Behavior in Pea Aphid (Acyrthosiphon pisum)." Insects 13, no. 3 (2022): 282. http://dx.doi.org/10.3390/insects13030282.

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Insect short neuropeptide F (sNPF), an ortholog of prolactin-releasing peptide of invertebrates, regulates diverse biological processes, including feeding, olfaction, locomotion, and sleep homeostasis in insects. However, its function is still unclear in an important model insect and agricultural pest, the pea aphid (Acyrthosiphon pisum). Here, we investigated short neuropeptide F (ApsNPF) and its receptor (ApsNPFR) in A. pisum. The sNPF gene contains three exons and two long introns. In addition, the genome contains a single sNPF receptor with seven transmembrane domains. Stage- and tissue-sp

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Jeong, Mira, Sangbae Kim, Yumei Li, Rui Chen, Premal Lulla, and Margaret Goodell. "Single Cell Profiling of DNMT3A-Mutant Progenitors Reveals LY86 As a Novel Pre-Leukemia Marker and Potential Therapeutic Target." Blood 134, Supplement_1 (2019): 2724. http://dx.doi.org/10.1182/blood-2019-123597.

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Acute Myeloid Leukemia (AML) is a clonal disease of the hematopoietic system that initiated and sustained by self-renewing hematopoietic stem and progenitor cells (HSPC). Mutations in the de novo DNA methyltransferase 3A (DNMT3A) gene occur in approximately 25% of adult acute myeloid leukemias (AML). Although the mechanisms through which such mutations promote leukemogenesis remain unclear, we have previously shown that loss of the DNMT3A can inhibit normal hematopoietic differentiation (Challen, Nature Genetics, 2011), accounting for the emergence of DNMT3A-HSC clones as a predisposition to h

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Tang, Xulong, Mahmut Taylan Kandemir, and Mustafa Karakoy. "Mix and Match: Reorganizing Tasks for Enhancing Data Locality." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 2 (2021): 1–24. http://dx.doi.org/10.1145/3460087.

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Application programs that exhibit strong locality of reference lead to minimized cache misses and better performance in different architectures. However, to maximize the performance of multithreaded applications running on emerging manycore systems, data movement in on-chip network should also be minimized. Unfortunately, the way many multithreaded programs are written does not lend itself well to minimal data movement. Motivated by this observation, in this paper, we target task-based programs (which cover a large set of available multithreaded programs), and propose a novel compiler-based ap

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Jain, Swachi, Ritesh Sachdev, Pranav Dorwal, et al. "Cosmic Mutational Analysis in Suspected Myeloproliferative Neoplasms Using Next Generation Sequencing with a Fifty Gene Panel." Blood 126, no. 23 (2015): 5209. http://dx.doi.org/10.1182/blood.v126.23.5209.5209.

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Abstract Introduction: As per the 2008 World Health Organization (WHO) classification, Myeloproliferative neoplasms (MPN) are subclassified into eight clinicopathological groups.1 The discovery of activating JAK2 mutations revolutionized the approach to diagnosis of MPN. Recently there have been studies suggesting an increasing number of mutations distinct from JAK2 associated with MPN. The new mutations being studied are MPL with a mutation frequency of 1-5% commonly seen in Essential Thrombocytosis (ET) and Primary Myelofibrosis (PMF).2 IDH1 has a mutational frequency of 21% for blast phase

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Wang, Xiaxia, Tengteng Lin, Weiqing Luo, Gong Cheng, and Yuzhong Qu. "CKGSE: A Prototype Search Engine for Chinese Knowledge Graphs." Data Intelligence 4, no. 1 (2022): 41–65. http://dx.doi.org/10.1162/dint_a_00118.

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Abstract Nowadays, with increasing open knowledge graphs (KGs) being published on the Web, users depend on open data portals and search engines to find KGs. However, existing systems provide search services and present results with only metadata while ignoring the contents of KGs, i.e., triples. It brings difficulty for users' comprehension and relevance judgement. To overcome the limitation of metadata, in this paper we propose a content-based search engine for open KGs named CKGSE. Our system provides keyword search, KG snippet generation, KG profiling and browsing, all based on KGs' detaile

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Jung, Haemin, Heesung Park, and Kwangyon Lee. "Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs." Applied Sciences 13, no. 18 (2023): 10041. http://dx.doi.org/10.3390/app131810041.

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Recommender systems play a crucial role in personalizing online user experiences by creating user profiles based on user–item interactions and preferences. Knowledge graphs (KGs) are intricate data structures that encapsulate semantic information, expressing users and items in a meaningful way. Although recent deep learning-based recommendation algorithms that embed KGs have demonstrated impressive performance, the richness of semantics and explainability embedded in the KGs are often lost due to the opaque nature of vector representations in deep neural networks. To address this issue, we pro

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Toader, Bogdan, Assaad Moawad, Thomas Hartmann, and Francesco Viti. "A Data-Driven Scalable Method for Profiling and Dynamic Analysis of Shared Mobility Solutions." Journal of Advanced Transportation 2021 (January 18, 2021): 1–15. http://dx.doi.org/10.1155/2021/5943567.

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The advent of Internet of Things will revolutionise the sharing mobility by enabling high connectivity between passengers and means of transport. This generates enormous quantity of data which can reveal valuable knowledge and help understand complex travel behaviour. At the same time, it challenges analytics platforms to discover knowledge from data in motion (i.e., the analytics occur in real time as the event happens), extract travel habits, and provide reliable and faster sharing mobility services in dynamic contexts. In this paper, a scalable method for dynamic profiling is introduced, wh

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Matarese, Fabiola, Giancarlo Scalabrelli, and Claudio D'Onofrio. "Analysis of the expression of terpene synthase genes in relation to aroma content in two aromatic Vitis vinifera varieties." Functional Plant Biology 40, no. 6 (2013): 552. http://dx.doi.org/10.1071/fp12326.

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Grape (Vitis vinifera L.) flavour management in the vineyard requires knowledge of the derivation of individual flavour and aroma characteristics. Some of the most prevalent wine grape aroma constituents are terpenoids and this study represents a wide report about grape terpene synthase (TPS) gene transcript profiling in different tissues of two aromatic grapevine varieties, particularly flowers and developing berries, correlated with the accumulation patterns of free aroma compounds. All investigated genes belonging to the TPS-a and TPS-b subfamilies reached the highest expression in accordan

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Nebish, Anna, Javier Tello, Yolanda Ferradás, Rouben Aroutiounian, José Miguel Martínez-Zapater, and Javier Ibáñez. "SSR and SNP genetic profiling of Armenian grape cultivars gives insights into their identity and pedigree relationships." OENO One 55, no. 4 (2021): 101–14. http://dx.doi.org/10.20870/oeno-one.2021.55.4.4815.

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The South Caucasus is recognised as the primary Vitis vinifera L. (grapevine) domestication centre and has a high diversity of wild and cultivated grapevines. Archaeological findings indicate that winemaking activities have existed in Armenia for more than 6,000 years, viticulture being one of the most important activities of the modern Armenian agricultural sector. Despite this relevance, some grapevines in local collections have not yet been properly identified, thus hindering the efficient conservation, characterisation and eventual use of autochthonous genetic resources. In the present stu

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Fuentes-Fayos, A. C., M. L. Gandía-González, A. Cano-Rojas, et al. "P13.11 Metabolomics and molecular profiling in glioma patients: an interactomic approach." Neuro-Oncology 21, Supplement_3 (2019): iii64—iii65. http://dx.doi.org/10.1093/neuonc/noz126.232.

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Abstract BACKGROUND A wider view of the interaction between different omic-domains is needed to identify potential biomarkers of low- and high-grade gliomas. Using an interactomic approach, we analyzed the correlation between radiological data, IDH mutation, gene expression profiling and metabolic signature in glioma samples. MATERIAL AND METHODS Tumor biopsies from 25 patients with clinical diagnosis of glioma were surgically collected during 2017–2019 at the senior author’s institution. Metabolomic data was obtained by high resolution 31P and 1H magnetic resonance spectroscopy (MRS, 19 metab

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Qi, Jinwei, Kang Li, Yunxia Shi, et al. "Cross-Species Comparison of Metabolomics to Decipher the Metabolic Diversity in Ten Fruits." Metabolites 11, no. 3 (2021): 164. http://dx.doi.org/10.3390/metabo11030164.

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Fruits provide humans with multiple kinds of nutrients and protect humans against worldwide nutritional deficiency. Therefore, it is essential to understand the nutrient composition of various fruits in depth. In this study, we performed LC-MS-based non-targeted metabolomic analyses with ten kinds of fruit, including passion fruit, mango, starfruit, mangosteen, guava, mandarin orange, grape, apple, blueberry, and strawberry. In total, we detected over 2500 compounds and identified more than 300 nutrients. Although the ten fruits shared 909 common-detected compounds, each species accumulated a

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Ackermann, Martin Daniel, John Andrew van der Poll, and Huibrecht Margaretha van der Poll. "Re-evaluating the Definition of Intelligence in Business Intelligence." GATR Journal of Management and Marketing Review 1, no. 1 (2016): 33–44. http://dx.doi.org/10.35609/jmmr.2016.1.1(5).

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Objective - Business Intelligence has little bearing with graphs and dashboards of traditionally defined Business Intelligence. Rather it is all about experience and sound judgement of the person at the helm of the decision-making process. In line with this view, we evaluate and subsequently, reposition the current definition of Business Intelligence in the literature. Methodology/Technique - The initial development of the data, information, knowledge and wisdom (DIKW) hierarchy excluded intelligence and so it never questioned the accepted definition of Business Intelligence. The extended DIKI

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Ilnitskaya, E. T., M. V. Makarkina, I. V. Stepanov, et al. "Genetic polymorphism of local Abkhazian grape cultivars." Vavilov Journal of Genetics and Breeding 25, no. 8 (2022): 797–804. http://dx.doi.org/10.18699/vj21.092.

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Local grape cultivars from different countries of the world are an important part of the gene pool of this culture. Of particular interest are the genotypes of the most ancient regions of viticulture. The territories of the subtropical zone of Georgia and the central part of Abkhazia belong to one of the centers of origin of the cultural grapevine. The purpose of the work was to genotype native Abkhazian grape cultivars, to study their genetic diversity based on DNA profiling data and to compare them with the genotypes of local varieties of other viticultural regions. Samples of plants were ta

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Mezei, Laura V., Trent E. Johnson, Steven Goodman, Cassandra Collins, and Susan E. P. Bastian. "Meeting the demands of climate change: Australian consumer acceptance and sensory profiling of red wines produced from non-traditional red grape varieties." OENO One 55, no. 2 (2021): 29–46. http://dx.doi.org/10.20870/oeno-one.2021.55.2.4571.

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To endure the challenge of climate change, the Australian wine industry could adopt new wine grape varieties more tolerant of these pending conditions. The aims of this study were to (i) generate sensory profiles and (ii) gain knowledge about Australian wine consumers’ liking of Australian and international wines made from selected drought-resistant, red wine grape varieties not traditionally grown in Australia but better suited for a changing Australian climate. A Rate-All-That-Apply (RATA) sensory panel (n = 43) profiled 24 commercial red wines made from 9 purportedly drought-tolerant red gr

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Ruiz Mejia, Jose M., and Danda B. Rawat. "MedScrubCrew: A Medical Multi-Agent Framework for Automating Appointment Scheduling Based on Patient-Provider Profile Resource Matching." Healthcare 13, no. 14 (2025): 1649. https://doi.org/10.3390/healthcare13141649.

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Background: With advancements in Generative Artificial Intelligence, various industries have made substantial efforts to integrate this technology to enhance the efficiency and effectiveness of existing processes or identify potential weaknesses. Context, however, remains a crucial factor in leveraging intelligence, especially in high-stakes sectors such as healthcare, where contextual understanding can lead to life-changing outcomes. Objective: This research aims to develop a practical medical multi-agent system framework capable of automating appointment scheduling and triage classification,

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Miliordos, Dimitrios Evangelos, Georgios Merkouropoulos, Charikleia Kogkou, et al. "Explore the Rare—Molecular Identification and Wine Evaluation of Two Autochthonous Greek Varieties: “Karnachalades” and “Bogialamades”." Plants 10, no. 8 (2021): 1556. http://dx.doi.org/10.3390/plants10081556.

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Wines produced from autochthonous Vitis vinifera varieties have an essential financial impact on the national economy of Greece. However, scientific data regarding characteristics and quality aspects of these wines is extremely limited. The aim of the current study is to define the molecular profile and to describe chemical and sensory characteristics of the wines produced by two autochthonous red grapevine varieties—“Karnachalades” and “Bogialamades”—grown in the wider area of Soufli (Thrace, Greece). We used seven microsatellites to define the molecular profile of the two varieties, and then

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Liu, Yu, Zhilun Zhou, Yong Li, and Depeng Jin. "Urban Knowledge Graph Aided Mobile User Profiling." ACM Transactions on Knowledge Discovery from Data, July 3, 2023. http://dx.doi.org/10.1145/3596604.

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Nowadays, the explosive growth of personalized web applications and the rapid development of artificial intelligence technology have flourished the recent research on mobile user profiling, i.e., inferring the user profile from mobile behavioral data. Particularly, existing studies mainly follow the data-driven paradigm to develop feature engineering and representation learning on such data, which however suffer from the robustness issue, i.e., generalizing poorly across datasets and profiles without considering semantic knowledge therein. In comparison, the rising knowledge-driven paradigm bu

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Lars, Vogt, Kuhn Tobias, and Hoehndorf Robert. "Semantic units: organizing knowledge graphs into semantically meaningful units of representation." Journal of Biomedical Semantics 15, no. 1 (2024). http://dx.doi.org/10.1186/s13326-024-00310-5.

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Abstract Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results We introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningf

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Ramalli, Edoardo, and Barbara Pernici. "Knowledge graph embedding for experimental uncertainty estimation." Information Discovery and Delivery, February 8, 2023. http://dx.doi.org/10.1108/idd-06-2022-0060.

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Purpose Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some dat

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Spahiu, Blerina, Matteo Palmonari, Renzo Arturo Alva Principe, and Anisa Rula. "Understanding the structure of knowledge graphs with ABSTAT profiles." Semantic Web, March 9, 2023, 1–27. http://dx.doi.org/10.3233/sw-223181.

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While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we

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Diop, Lamine, Béatrice Markhoff, and Arnaud Soulet. "TTProfiler: Types and Terms Profile Building for Online Cultural Heritage Knowledge Graphs." Journal on Computing and Cultural Heritage, March 18, 2023. http://dx.doi.org/10.1145/3588443.

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As more and more knowledge graphs (KG) are published on the Web, there is a need for tools that show their content. This implies showing the schema-level patterns instantiated in the graph, but also the terms used to qualify its entities. In this paper, we present a new profiling tool that we call TTprofiler. It shows the predicates that relate types in the KG, and also the terms present in this KG, because of their paramount importance in most KGs, especially in the Cultural Heritage (CH) domain. We recall the role of terminologies and how they are implemented and used on the Web, we give the

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Ishan, Shekhar Prasad Joseph Rejo Mathew Garv Agarwalla Mayur Kumar K. N. and G. Vijaya Kumar. "AI-Driven Pathogen Profiling in Scientific Literature using NLP." January 12, 2025. https://doi.org/10.5281/zenodo.14635012.

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Rapid identification and understanding of pathogens is vital for mitigation of infectious diseases and ensuring public health safety. The accelerating pace of emerging infectious diseases calls for the use of advanced tools such as Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP). This paper proposes an innovative framework that integrates NLP techniques and database platforms, to extract, and analyze pathogen characteristics from a vast corpus of already available scientific literature. The framework capture

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Yang, Chao, Libin Wang, Lida Feng, et al. "Research on the Application of Knowledge Graph in Demand-Side Flexible Resource Profiling and Aggregation Techniques." Applied Mathematics and Nonlinear Sciences 9, no. 1 (2024). http://dx.doi.org/10.2478/amns-2024-1331.

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Abstract This paper commences by assessing the current landscape of power system development, focusing on the theory, principles, and structures of demand-side flexible resources and their aggregation technology. Utilizing network crawler technology within a knowledge graph framework, the research data pertinent to demand-side flexible resources and aggregation technology are extracted. These data undergo a meticulous cleaning process before being stored, culminating in the development of a knowledge graph tailored to the imaging and technology of demand-side flexible resources. The findings r

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Zhou, Jilei, Guanran Jiang, Wei Du, and Cong Han. "Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation." Electronic Commerce Research, March 3, 2022. http://dx.doi.org/10.1007/s10660-022-09541-z.

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Wu, Chunlian, Sen Chen, Jiaming Li, et al. "Beyond Decision: Android Malware Description Generation through Profiling Malicious Behavior Trajectory." ACM Transactions on Software Engineering and Methodology, January 31, 2025. https://doi.org/10.1145/3715909.

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Malware family labels and key features used for the decision-making of Android malware detection models fall short of precise comprehension of malicious behaviors due to their coarse granularity. To solve these problems, in this paper, we first introduce the concept of the malicious behavior trajectory ( MBT ) and propose an innovative approach called ProMal . ProMal aims to automatically generate malware descriptions with fine granularity through extracted MBTs from malware for users. Specifically, a labeled dataset of MBTs is constructed through substantial human efforts to build a behaviora

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Nagpal, Rishub, Gaëtan Cassiers, Robert Primas, Christian Knoll, Franz Pernkopf, and Stefan Mangard. "On Loopy Belief Propagation for SASCAs." IACR Communications in Cryptology 1, no. 4 (2025). https://doi.org/10.62056/ayl8ksdja.

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Profiled power analysis is one of the most powerful forms of passive side-channel attacks. Over the last two decades, many works have analyzed their impact on cryptographic implementations as well as corresponding countermeasure techniques. To date, the most advanced variants of profiled power analysis are based on Soft-analytical Side-Channel Attacks (SASCA). After the initial profiling phase, a SASCA adversary creates a probabilistic graphical model, called a factor graph, of the target implementation and encodes the results of the previous step as prior information. Then, an inference algor

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Pu, Limeng, Manali Singha, Hsiao-Chun Wu, Costas Busch, J. Ramanujam, and Michal Brylinski. "An integrated network representation of multiple cancer-specific data for graph-based machine learning." npj Systems Biology and Applications 8, no. 1 (2022). http://dx.doi.org/10.1038/s41540-022-00226-9.

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AbstractGenomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibit

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FENG, FAN, FEITONG TANG, YIJIA GAO, et al. "1999-LB: GenomicKB—A Knowledge Graph for Human Genomic Data to Advance Understanding of Diabetes." Diabetes 73, Supplement_1 (2024). http://dx.doi.org/10.2337/db24-1999-lb.

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With rapid advancements in technologies involving genetic testing, epigenomic and transcriptomic profiling, many landmark consortia have generated rich multimodal data for the human genome. Among those, the consortia focused on human pancreas and islet research have greatly enhanced our understanding of different forms of diabetes. Despite new insights provided at an unprecedented scale and depth, current islet and pancreas data are fragmented across different portals with lack of common metadata/ontology standards, varying maturity of quality control pipelines, and disconnected from clinical

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Kim, Meen Chul, Yuanyuan Feng, and Yongjun Zhu. "Mapping scientific profile and knowledge diffusion of Library Hi Tech." Library Hi Tech ahead-of-print, ahead-of-print (2020). http://dx.doi.org/10.1108/lht-08-2019-0164.

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PurposeLibrary Hi Tech is one of the most influential journals that publish leading research in library and information science (LIS). The present study aims to understand the scholarly communication in Library Hi Tech by profiling its historic footprint, emerging trends and knowledge diffusion.Design/methodology/approachA total of 3,131 bibliographic records between 1995 and 2018 were collected from the Web of Science. Text mining, graph analysis and data visualization were used to analyze subject category assignment, domain-level citation trends, co-occurrence of keywords, keyword bursts, ne

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Shen, Rongbo, Meiling Cheng, Wencang Wang, et al. "Graph domain adaptation–based framework for gene expression enhancement and cell type identification in large-scale spatially resolved transcriptomics." Briefings in Bioinformatics 25, no. 6 (2024). http://dx.doi.org/10.1093/bib/bbae576.

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Abstract Spatially resolved transcriptomics (SRT) technologies facilitate gene expression profiling with spatial resolution in a naïve state. Nevertheless, current SRT technologies exhibit limitations, manifesting as either low transcript detection sensitivity or restricted gene throughput. These constraints result in diminished precision and coverage in gene measurement. In response, we introduce SpaGDA, a sophisticated deep learning–based graph domain adaptation framework for both scenarios of gene expression imputation and cell type identification in spatially resolved transcriptomics data

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Du, Wei, Guanran Jiang, Wei Xu, and Jian Ma. "Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)." Journal of Information Science, June 14, 2021, 016555152110239. http://dx.doi.org/10.1177/01655515211023937.

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With the rapid development of the patent marketplace, patent trading recommendation is required to mitigate the technology searching cost of patent buyers. Current research focuses on the recommendation based on existing patents of a company; a few studies take into account the sequential pattern of patent acquisition activities and the possible diversity of a company’s business interests. Moreover, the profiling of patents based on solely patent documents fails to capture the high-order information of patents. To bridge the gap, we propose a knowledge-aware attentional bidirectional long shor

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Manik, Mia Md Tofayel Gonee. "Multi-Omics Integration with Machine Learning for Early Detection of Ischemic Stroke Through Biomarkers Discovery." Journal of Ecohumanism 2, no. 2 (2023). https://doi.org/10.62754/joe.v2i2.6800.

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A complicated and diverse neurological condition, ischemic stroke (IS) is marked by a high death rate and substantial long-term impairment. Despite extensive research, reliable biomarkers for the clinical diagnosis and prognosis of ischemic stroke remain elusive, and the underlying molecular pathways remain obscure. This study uses a physiologically informed Convolutional Neural Network (BioCNN) to integrate multi-omics and present a novel method for the early identification and classification of ischemic stroke subtypes. Multi-omics Thirty acute ischemic stroke patients who were hospitalized

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Santos, Veronica, Daniel Schwabe, and Sérgio Lifschitz. "Can you trust Wikidata?" Semantic Web, March 7, 2024, 1–22. http://dx.doi.org/10.3233/sw-243577.

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In order to use a value retrieved from a Knowledge Graph (KG) for some computation, the user should, in principle, ensure that s/he trusts the veracity of the claim, i.e., considers the statement as a fact. Crowd-sourced KGs, or KGs constructed by integrating several different information sources of varying quality, must be used via a trust layer. The veracity of each claim in the underlying KG should be evaluated, considering what is relevant to carrying out some action that motivates the information seeking. The present work aims to assess how well Wikidata (WD) supports the trust decision p

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Wang, Jiye, Chaofeng Lou, Guixia Liu, Weihua Li, Zengrui Wu, and Yun Tang. "Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening." Briefings in Bioinformatics, August 23, 2022. http://dx.doi.org/10.1093/bib/bbac351.

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Abstract Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and mult

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Ma, Teng, and Jianxin Wang. "GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network." Bioinformatics, March 26, 2024. http://dx.doi.org/10.1093/bioinformatics/btae165.

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Abstract Motivation Studying the molecular heterogeneity of cancer is essential for achieving personalized therapy. At the same time, understanding the biological processes that drive cancer development can lead to the identification of valuable therapeutic targets. Therefore, achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. Results Here, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements

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Alva Principe, Renzo Arturo, Andrea Maurino, Matteo Palmonari, Michele Ciavotta, and Blerina Spahiu. "ABSTAT-HD: a scalable tool for profiling very large knowledge graphs." VLDB Journal, September 29, 2021. http://dx.doi.org/10.1007/s00778-021-00704-2.

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AbstractProcessing large-scale and highly interconnected Knowledge Graphs (KG) is becoming crucial for many applications such as recommender systems, question answering, etc. Profiling approaches have been proposed to summarize large KGs with the aim to produce concise and meaningful representation so that they can be easily managed. However, constructing profiles and calculating several statistics such as cardinality descriptors or inferences are resource expensive. In this paper, we present ABSTAT-HD, a highly distributed profiling tool that supports users in profiling and understanding big

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