Relevant bibliographies by topics / Knowledge graph profiling

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Knowledge graph profiling'

Author: Grafiati
Published: 9 March 2023
Last updated: 19 July 2025

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Journal articles on the topic "Knowledge graph profiling"

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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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&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; <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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Dissertations / Theses on the topic "Knowledge graph profiling"

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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 specialize
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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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Book chapters on the topic "Knowledge graph profiling"

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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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Li, Zhinan, Guodong Sa, Zhenyu Liu, Chan Qiu, and Jianrong Tan. "Design Knowledge Graph and User Profiling-Driven Product Innovation Design Problem Solving." In Advances in Mechanical Design. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0922-9_8.

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Velampalli, Sirisha, and Chandrashekar Muniyappa. "GraphRank Pro+: Advancing Talent Analytics Through Knowledge Graphs and Sentiment-Enhanced Skill Profiling." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62269-4_21.

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Spahiu Blerina, Maurino Andrea, and Palmonari Matteo. "Towards Improving the Quality of Knowledge Graphs with Data-driven Ontology Patterns and SHACL." In Studies on the Semantic Web. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-894-5-103.

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As Linked Data available on the Web continue to grow, understanding their structure and assessing their quality remains a challenging task making such the bottleneck for their reuse. ABSTAT is an online semantic profiling tool which helps data consumers in better understanding of the data by extracting data-driven ontology patterns and statistics about the data. The SHACL Shapes Constraint Language helps users capturing quality issues in the data by means of constraints. In this paper we propose a methodology to improve the quality of different versions of the data by means of SHACL constraints learned from the semantic profiles produced by ABSTAT.
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Conference papers on the topic "Knowledge graph profiling"

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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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Shimomura, Larissa C., Nikolay Yakovets, and George Fletcher. "Discovering Graph Generating Dependencies for Property Graph Profiling." In CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management. ACM, 2024. http://dx.doi.org/10.1145/3627673.3679764.

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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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Jinhua, Du, and Yin Hao. "KLDP:A Data Profiling Technique Based on Knowledge Graph and Large Language Modeling." In 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2023. http://dx.doi.org/10.1109/trustcom60117.2023.00329.

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Purificato, Erasmo, Ludovico Boratto, and Ernesto William De Luca. "Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges." In CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management. ACM, 2023. http://dx.doi.org/10.1145/3583780.3615292.

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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
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Luo, Yuankai, Lei Shi, Mufan Xu, et al. "Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2023. http://dx.doi.org/10.1145/3580305.3599845.

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