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Littérature scientifique sur le sujet « Knowledge graph profiling »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 10 mars 2023

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Articles de revues sur le sujet "Knowledge graph profiling"

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 proposed solution is storage efficient as we have only stored data logs for Citizen Profiling instead of storing images, audio, and video for profiling purposes. Our proposed system can be extended to Smart City, Smart Traffic Management, Workplace profiling etc. Agent based mechanism can be used for data acquisition where each Citizen has its own agent. Another improvement can be to incorporate a decentralized version of database for maintaining Citizen profile.

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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 named entities in the texts are ignored; second, most methods use time discretization technology, where the texts are divided into different time slices, resulting in failure of relevance modeling. To address these issues, we propose a user identity linkage model with the enhancement of a knowledge graph and continuous time decay functions that are designed for mitigating the influence of time discretization. Apart from modeling the correlations of the words, we extract the named entities in the texts and link them into the knowledge graph to capture the correlations of named entities. The semantics of texts are enhanced through the external knowledge of the named entities in the knowledge graph, and the similarity discrimination of the texts is also improved. Furthermore, we propose continuous time decay functions to capture the closeness of the posting time of texts instead of time discretization to avoid the matching error of texts. We conduct experiments on two real public datasets, and the experimental results show that the proposed method outperforms state-of-the-art methods.

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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 the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.

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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 applied to diversified downstream applications. To this end, in this paper, we propose a Self-Supervised Prototype Representation Learning (SePaL) framework for dynamic corporate profiling. By exploiting the topological information of an event graph and exploring self-supervised learning techniques, SePaL can obtain unified corporate representations that are robust to event noises and can be easily fine-tuned to benefit various down-stream applications with only a few annotated data. Specifically, we first infer the initial cluster distribution of noise-resistant event prototypes based on latent representations of events. Then, we construct four permutation-invariant self-supervision signals to guide the representation learning of the event prototype. In terms of applications, we exploit the learned time-evolving corporate representations for both stock price spike prediction and corporate default risk evaluation. Experimental results on two real-world corporate event datasets demonstrate the effectiveness of SePaL for these two applications.

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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 sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x,y) spatial coordinates (x-mode andy-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.

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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-specific transcript profiling by qRT-PCR revealed that ApsNPF and ApsNPFR were mainly expressed in the central nervous system. The receptor was also detected in antennae, midgut, and integument. The highest expression levels were found in first instar nymphs compared to other developmental stages. Besides, the starvation-induced pattern indicated that the sNPF network depends on the nutritional state of the insect. An electrical penetration graph showed that probing time and phloem duration of A. pisum on broad bean plants decreased in response to dssNPF and dssNPFR in RNAi assays. sNPF silencing reduced the number of nymphs per female but not aphid survival. We believe that our results advance in-depth knowledge of the sNPF/sNPFR signaling cascade and its place in regulating feeding behavior in insects. In turn, it may contribute to the potential design of new strategies to control aphids, with a focus on the sNPF system.

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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 hematological malignancies (Yang, Cancer Cell, 2015). Therapies that selectively eliminate the initiating pre-leukemic population would greatly improve outcomes for affected patients. However, the identification as well as selective elimination of such a distinct population has been problematic because of the considerable overlap in gene expression profiles with bulk normal hematopoietic stem cells. Molecular targets during leukemia development have not been well elucidated due to lack of the real definitive markers, which is a significant knowledge gap and barrier for understanding clonal leukemogenesis and therapeutic applications. Single-cell RNA sequencing has emerged as a powerful tool to analyze new cell types, cellular heterogeneity and cell differentiation routes. This technique made important contributions to our understanding of hematopoietic stem and cancer cell heterogeneity and selective resistance of cancer cell subpopulations to molecularly targeted cancer therapies. To identify early events involved in pre-leukemic transformation, we have performed single-cell RNA-sequencing (scRNA-seq) in WT and Dnmt3a KO mice. Flow cytometry sorted wild-type and pre-leukemic Dnmt3a KO HSPC cells were captured using 10X genomics chromium platform. After genome mapping, dimensional reduction, and clustering using Cell ranger pipeline, we generated transcriptome data and integrated the data sets using Seurat. Approximately 8,000 cells from each group were sequenced, and each cell expressed 1800-4500 genes. Graph-based clustering analysis revealed 16 unique cell clusters in both WT and DNMT3A KO mice. Interestingly, when compared with WT mice, we observed a 10-fold expansion of a single cell cluster in Dnmt3a KO cells before the advent of overt leukemia. This cluster co-expresses several stem cell genes including well-known leukemic stem cell surface markers such as CD47, as well as several novel genes. Some of these novel genes, encode cell surface proteins such as Ly6c2 and Ly86. We further validated protein expressions in AML cell lines and primary AML blast. In conclusion, the discovery of novel cluster in DNMT3A KO mice, and the relative abundance of this cluster in pre-leukemic stage of DNMT3A KO mice indicates that they promote leukemogenesis and offers an opportunity to specifically target DNMT3A mutant pre-leukemic cells using T cell immunotherapy. Disclosures No relevant conflicts of interest to declare.

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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 approach that consists of four complementary steps. First, we partition the original tasks in the target application into sub-tasks and build a data reuse graph at a sub-task granularity. Second, based on the intensity of temporal and spatial data reuses among sub-tasks, we generate new tasks where each such (new) task includes a set of sub-tasks that exhibit high data reuse among them. Third, we assign the newly-generated tasks to cores in an architecture-aware fashion with the knowledge of data location. Finally, we re-schedule the execution order of sub-tasks within new tasks such that sub-tasks that belong to different tasks but share data among them are executed in close proximity in time. The detailed experiments show that, when targeting a state of the art manycore system, our proposed compiler-based approach improves the performance of 10 multithreaded programs by 23.4% on average, and it also outperforms two state-of-the-art data access optimizations for all the benchmarks tested. Our results also show that the proposed approach i) improves the performance of multiprogrammed workloads, and ii) generates results that are close to maximum savings that could be achieved with perfect profiling information. Overall, our experimental results emphasize the importance of dividing an original set of tasks of an application into sub-tasks and constructing new tasks from the resulting sub-tasks in a data movement- and locality-aware fashion.

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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' detailed, informative contents rather than their brief, limited metadata. To evaluate its usability, we implement a prototype with Chinese KGs crawled from OpenKG.CN and report some preliminary results and findings.

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Thèses sur le sujet "Knowledge graph profiling"

PORRINI, RICCARDO. "Construction and Maintenance of Domain Specific Knowledge Graphs for Web Data Integration." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/126789.

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A Knowledge Graph (KG) is a semantically organized, machine readable collection of types, entities, and relations holding between them. A KG helps in mitigating semantic heterogeneity in scenarios that require the integration of data from independent sources into a so called dataspace, realized through the establishment of mappings between the sources and the KG. Applications built on top of a dataspace provide advanced data access features to end-users based on the representation provided by the KG, obtained through the enrichment of the KG with domain specific facets. A facet is a specialized type of relation that models a salient characteristic of entities of particular domains (e.g., the vintage of wines) from an end-user perspective. In order to enrich a KG with a salient and meaningful representation of data, domain experts in charge of maintaining the dataspace must be in possess of extensive knowledge about disparate domains (e.g., from wines to football players). From an end-user perspective, the difficulties in the definition of domain specific facets for dataspaces significantly reduce the user-experience of data access features and thus the ability to fulfill the information needs of end-users. Remarkably, this problem has not been adequately studied in the literature, which mostly focuses on the enrichment of the KG with a generalist, coverage oriented, and not domain specific representation of data occurring in the dataspace. Motivated by this challenge, this dissertation introduces automatic techniques to support domain experts in the enrichment of a KG with facets that provide a domain specific representation of data. Since facets are a specialized type of relations, the techniques proposed in this dissertation aim at extracting salient domain specific relations. The fundamental components of a dataspace, namely the KG and the mappings between sources and KG elements, are leveraged to elicitate such domain specific representation from specialized data sources of the dataspace, and to support domain experts with valuable information for the supervision of the process. Facets are extracted by leveraging already established mappings between specialized sources and the KG. After extraction, a domain specific interpretation of facets is provided by re-using relations already defined in the KG, to ensure tight integration of data. This dissertation introduces also a framework to profile the status of the KG, to support the supervision of domain experts in the above tasks. Altogether, the contributions presented in this dissertation provide a set of automatic techniques to support domain experts in the evolution of the KG of a dataspace towards a domain specific, end-user oriented representation. Such techniques analyze and exploit the fundamental components of a dataspace (KG, mappings, and source data) with an effectiveness not achievable with state-of-the-art approaches, as shown by extensive evaluations conducted in both synthetic and real world scenarios.

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Nishioka, Chifumi [Verfasser]. "Profiling Users and Knowledge Graphs on the Web / Chifumi Nishioka." Kiel : Universitätsbibliothek Kiel, 2018. http://d-nb.info/115188071X/34.

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Chapitres de livres sur le sujet "Knowledge graph profiling"

Lully, Vincent, Philippe Laublet, Milan Stankovic, and Filip Radulovic. "Image User Profiling with Knowledge Graph and Computer Vision." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98192-5_19.

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Actes de conférences sur le sujet "Knowledge graph profiling"

Li, Jiao, Tan Sun, Guojian Xian, Yongwen Huang, and Ruixue Zhao. "Scientific Knowledge Graph-driven Research Profiling." In CSAE 2022: The 6th International Conference on Computer Science and Application Engineering. ACM, 2022. http://dx.doi.org/10.1145/3565387.3565423.

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Yan, Qilong, Yufeng Zhang, Qiang Liu, Shu Wu, and Liang Wang. "Relation-aware Heterogeneous Graph for User Profiling." In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. ACM, 2021. http://dx.doi.org/10.1145/3459637.3482170.

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Luo, Yan, Fu-lai Chung, and Kai Chen. "Urban Region Profiling via Multi-Graph Representation Learning." In CIKM '22: The 31st ACM International Conference on Information and Knowledge Management. ACM, 2022. http://dx.doi.org/10.1145/3511808.3557720.

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Wang, Pengyang, Kunpeng Liu, Lu Jiang, Xiaolin Li, and Yanjie Fu. "Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2020. http://dx.doi.org/10.1145/3394486.3403128.

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Purificato, Erasmo, Ludovico Boratto, and Ernesto William De Luca. "Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling." In CIKM '22: The 31st ACM International Conference on Information and Knowledge Management. ACM, 2022. http://dx.doi.org/10.1145/3511808.3557584.

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Wang, Heyuan, Tengjiao Wang, Shun Li, Shijie Guan, Jiayi Zheng, and Wei Chen. "Heterogeneous Interactive Snapshot Network for Review-Enhanced Stock Profiling and Recommendation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/550.

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Stock recommendation plays a critical role in modern quantitative trading. The large volumes of social media information such as investment reviews that delegate emotion-driven factors, together with price technical indicators formulate a “snapshot” of the evolving stock market profile. However, previous studies usually model the temporal trajectories of price and media modalities separately while losing their interrelated influences. Moreover, they mainly extract review semantics via sequential or attentive models, whereas the rich text associated knowledge is largely neglected. In this paper, we propose a novel heterogeneous interactive snapshot network for stock profiling and recommendation. We model investment reviews in each snapshot as a heterogeneous document graph, and develop a flexible hierarchical attentive propagation framework to capture fine-grained proximity features. Further, to learn stock embedding for ranking, we introduce a novel twins-GRU method, which tightly couples the media and price parallel sequences in a cross-interactive fashion to catch dynamic dependencies between successive snapshots. Our approach excels state-of-the-arts over 7.6% in terms of cumulative and risk-adjusted returns in trading simulations on both English and Chinese benchmarks.

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Denzler, Alexander, and Michael Kaufmann. "Toward granular knowledge analytics for data intelligence: Extracting granular entity-relationship graphs for knowledge profiling." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258010.

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