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Journal articles on the topic 'An entity'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Зыкова, О. А. "OFFSHORE ENTITY – A NEW BUSINESS ENTITY." Правовая мысль, no. 2(3) (January 1, 2022): 90–95. http://dx.doi.org/10.55000/mcu.legth.2022.3.2.017.

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На основании изучения доктринальных источников и законодательства, устанавливающих дефи-ницию термина «офшорная компания», автором выделены ее отличительные черты и в после-дующем дана краткая характеристика в части обоснованности, разумности их выделения и обо-значения. Так, было предложено расширение состава субъектов, которые могут входить в анали-зируемое понятие. Это было обусловлено анализом иностранных источников права (офшорных зон), который показал допустимость осуществления российскими резидентами регистрации своего предпринимательства в форме не только юридического лица, но и единол

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2

KIM, Eun-kyung, and Key-Sun CHOI. "Entity Summarization Based on Entity Grouping in Multilingual Projected Entity Space." IEICE Transactions on Information and Systems E100.D, no. 9 (2017): 2138–46. http://dx.doi.org/10.1587/transinf.2016edp7235.

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3

Qin, Yongbin, Weizhe Yang, Kai Wang, et al. "Entity Relation Extraction Based on Entity Indicators." Symmetry 13, no. 4 (2021): 539. http://dx.doi.org/10.3390/sym13040539.

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Relation extraction aims to extract semantic relationships between two specified named entities in a sentence. Because a sentence often contains several named entity pairs, a neural network is easily bewildered when learning a relation representation without position and semantic information about the considered entity pair. In this paper, instead of learning an abstract representation from raw inputs, task-related entity indicators are designed to enable a deep neural network to concentrate on the task-relevant information. By implanting entity indicators into a relation instance, the neural

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4

Possemato, Tiziana. "Entity modeling." JLIS.it 13, no. 3 (2022): 12–28. http://dx.doi.org/10.36253/jlis.it-481.

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In questo lavoro tratteremo il tema delle entità intese come oggetti del mondo reale (real-world object) e di come questo concetto sia utilizzato nell’ambito dell’entity modeling, quel processo di identificazione e modellamento delle entità che tanta parte occupa nei progetti di conversione dei cataloghi in linked open data. L’aiuto alla comprensione di cosa questo concetto esprima nell’ambito dell’universo bibliografico ci viene dalla programmazione orientata agli oggetti, che introduce il concetto di modellamento e gestione di un “oggetto” definendone uno stato e un comportamento. Ma per mod

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5

Wang, Jiannan, Guoliang Li, Jeffrey Xu Yu, and Jianhua Feng. "Entity matching." Proceedings of the VLDB Endowment 4, no. 10 (2011): 622–33. http://dx.doi.org/10.14778/2021017.2021020.

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6

Getoor, Lise, and Ashwin Machanavajjhala. "Entity resolution." Proceedings of the VLDB Endowment 5, no. 12 (2012): 2018–19. http://dx.doi.org/10.14778/2367502.2367564.

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7

Liu, Chen, Feng Li, Xian Sun, and Hongzhe Han. "Attention-Based Joint Entity Linking with Entity Embedding." Information 10, no. 2 (2019): 46. http://dx.doi.org/10.3390/info10020046.

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Entity linking (also called entity disambiguation) aims to map the mentions in a given document to their corresponding entities in a target knowledge base. In order to build a high-quality entity linking system, efforts are made in three parts: Encoding of the entity, encoding of the mention context, and modeling the coherence among mentions. For the encoding of entity, we use long short term memory (LSTM) and a convolutional neural network (CNN) to encode the entity context and entity description, respectively. Then, we design a function to combine all the different entity information aspects

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8

Xu, Zhenran, Yulin Chen, and Baotian Hu. "Improving Biomedical Entity Linking with Cross-Entity Interaction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13869–77. http://dx.doi.org/10.1609/aaai.v37i11.26624.

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Biomedical entity linking (EL) is the task of linking mentions in a biomedical document to corresponding entities in a knowledge base (KB). The challenge in biomedical EL lies in leveraging mention context to select the most appropriate entity among possible candidates. Although some EL models achieve competitive results by retrieving candidate entities and then exploiting context to re-rank them, these re-ranking models concatenate mention context with one candidate at a time. They lack fine-grained interaction among candidates, and potentially cannot handle ambiguous mentions when facing can

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9

Lokhande, Rohit, Namita Goyal, Sunita Bhargava, and Abha Patni. "Retroperitoneal Angiomyolipoma: Rare Entity." Asian Pacific Journal of Health Sciences 4, no. 1 (2017): 15–18. http://dx.doi.org/10.21276/apjhs.2017.4.1.5.

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10

S, Sujana Donandi. "TINJAUAN HUKUM MENGENAI BENTUK BADAN HUKUM LEMBAGA MANAJEMEN KOLEKTIF DI INDONESIA." Problematika Hukum 2, no. 2 (2019): 94. http://dx.doi.org/10.33021/ph.v2i2.567.

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The existing legislation does not yet give clear boundaries regarding the form of legal entity of Lembaga Manajemen Kolektif (LMK) in Indonesia. The legal certainty about the form of legal entitiy of LMK is important to be examined as the basis for government and society in running and supervising LMK. This research is conducted through a normative legal research method using statute approachment dan utilizing primary and secoundary resources. The discussion shows that LMK is divided into 2, The LMK and LMK Nasional. The form of legal entity that lawfully allowed to establish LMK is Associatio

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11

Foppiano, Luca, and Laurent Romary. "Entity-fishing: A DARIAH Entity Recognition and Disambiguation Service." Journal of the Japanese Association for Digital Humanities 5, no. 1 (2020): 22–60. http://dx.doi.org/10.17928/jjadh.5.1_22.

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12

Chen, Shuang, Jinpeng Wang, Feng Jiang, and Chin-Yew Lin. "Improving Entity Linking by Modeling Latent Entity Type Information." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7529–37. http://dx.doi.org/10.1609/aaai.v34i05.6251.

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Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility. However, the latent entity type information in the immediate context of the mention is neglected, which causes the models often link mentions to incorrect entities with incorrect type. To tackle this problem, we propose to inject latent entity type information into the entity embeddings based on pre-trained BERT. In addition, we integrate a BERT-based entity similarity score into

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13

Onoe, Yasumasa, and Greg Durrett. "Fine-Grained Entity Typing for Domain Independent Entity Linking." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8576–83. http://dx.doi.org/10.1609/aaai.v34i05.6380.

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Neural entity linking models are very powerful, but run the risk of overfitting to the domain they are trained in. For this problem, a “domain” is characterized not just by genre of text but even by factors as specific as the particular distribution of entities, as neural models tend to overfit by memorizing properties of frequent entities in a dataset. We tackle the problem of building robust entity linking models that generalize effectively and do not rely on labeled entity linking data with a specific entity distribution. Rather than predicting entities directly, our approach models fine-gr

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14

Zeng, Weixin, Jiuyang Tang, and Xiang Zhao. "Measuring Entity Relatedness via Entity and Text Joint Embedding." Neural Processing Letters 50, no. 2 (2018): 1861–75. http://dx.doi.org/10.1007/s11063-018-9966-6.

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15

Unnikrishnan, Vishnu, Christian Beyer, Pawel Matuszyk, et al. "Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity." International Journal of Data Science and Analytics 9, no. 1 (2019): 1–15. http://dx.doi.org/10.1007/s41060-019-00177-1.

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16

K, Kritika, Gagan Upadhyay, Tushar T, and Shubham S. "Intelligent Entity Coordinator." International Journal of Computer Applications 181, no. 50 (2019): 50–53. http://dx.doi.org/10.5120/ijca2019918689.

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17

Ford, Pauline. "RCN entity review." Nursing Standard 4, no. 7 (1989): 43. http://dx.doi.org/10.7748/ns.4.7.43.s58.

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18

Tichy, Milik. "Entity Risk Mechanics." Journal of Risk Analysis and Crisis Response 2, no. 2 (2012): 107. http://dx.doi.org/10.2991/jracr.2012.2.2.3.

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19

Li, Yuliang, Jinfeng Li, Yoshihiko Suhara, Jin Wang, Wataru Hirota, and Wang-Chiew Tan. "Deep Entity Matching." Journal of Data and Information Quality 13, no. 1 (2021): 1–17. http://dx.doi.org/10.1145/3431816.

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20

Pinchuk, Steve. "Nano entity economics." Journal of Revenue and Pricing Management 9, no. 5 (2010): 476–78. http://dx.doi.org/10.1057/rpm.2010.29.

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21

Il-Yeol Song and K. Froehlich. "Entity-relationship modeling." IEEE Potentials 13, no. 5 (1995): 29–34. http://dx.doi.org/10.1109/45.464652.

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22

Gal, Avigdor. "Uncertain entity resolution." Proceedings of the VLDB Endowment 7, no. 13 (2014): 1711–12. http://dx.doi.org/10.14778/2733004.2733068.

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23

Devezas, José, and Sérgio Nunes. "Hypergraph-of-entity." Open Computer Science 9, no. 1 (2019): 103–27. http://dx.doi.org/10.1515/comp-2019-0006.

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AbstractModern search is heavily powered by knowledge bases, but users still query using keywords or natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We have previously proposed the graph-of-entity as a purely graph-based representation and retrieval model, however this model would scale poorly. We tackle the scalability issue by adapting the model so that it can be represented as a hypergraph. This enables a significant re

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24

Husein El Ahmed, Husein, and Carmen Dulanto-Campos. "A distinct entity." American Journal of Obstetrics and Gynecology 208, no. 1 (2013): 89.e1–89.e2. http://dx.doi.org/10.1016/j.ajog.2012.10.864.

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25

Wei, Fu-Chan, and Naci Celik. "Perforator flap entity." Clinics in Plastic Surgery 30, no. 3 (2003): 325–29. http://dx.doi.org/10.1016/s0094-1298(03)00033-6.

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26

Yakkioui, Youssef, Jacobus J. van Overbeeke, Remco Santegoeds, Manon van Engeland, and Yasin Temel. "Chordoma: the entity." Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1846, no. 2 (2014): 655–69. http://dx.doi.org/10.1016/j.bbcan.2014.07.012.

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27

Pal, K., F. Reinartz, and S. Michel. "Mining Entity Rankings." Datenbank-Spektrum 16, no. 1 (2016): 27–38. http://dx.doi.org/10.1007/s13222-015-0205-2.

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28

Beck, Julia, Michael Büchner, Stephan Bartholmei, and Marko Knepper. "Performing Entity Facts." Datenbank-Spektrum 17, no. 1 (2017): 47–52. http://dx.doi.org/10.1007/s13222-016-0241-6.

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29

Shah, S. "A distinctive entity." British Dental Journal 203, no. 10 (2007): 560. http://dx.doi.org/10.1038/bdj.2007.1060.

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30

Campbell, David. "Entity-relationship modeling." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 23, no. 3 (1992): 12–18. http://dx.doi.org/10.1145/146548.146550.

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31

Morgan, Alastair. "The ‘living entity’." European Journal of Social Theory 17, no. 4 (2014): 377–88. http://dx.doi.org/10.1177/1368431014536092.

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32

EKBAL, ASIF, SUDIP KUMAR NASKAR, and SIVAJI BANDYOPADHYAY. "Named Entity Transliteration." International Journal of Computer Processing of Languages 20, no. 04 (2007): 289–310. http://dx.doi.org/10.1142/s021942790700169x.

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33

D´Cruz, Len. "Entity the dragon." BDJ In Practice 35, no. 12 (2022): 30. http://dx.doi.org/10.1038/s41404-022-1831-7.

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34

Zhao, Ziwen, Nikolaos Kostopoulos, Sagar Ganguli, and Alina Sekretareva. "Single-Entity Bioelectrochemistry." ECS Meeting Abstracts MA2023-02, no. 52 (2023): 2531. http://dx.doi.org/10.1149/ma2023-02522531mtgabs.

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Interfacing biological catalytic entities, such as whole cells or enzymes, with electrodes, has a wide range of applications, ranging from fundamental studies of the entities themselves to power generation, bioremediation, chemical synthesis, and biosensing. In all cases, the operation of these bioelectrocatalytic systems is based on electron transfer phenomena within protein molecules, whether isolated or residing inside cells or parts of cells, as well as between proteins and electronics. Typically, the electron transfer and catalytic properties of proteins are studied using ensemble averagi

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35

Jung, Jinuk, Hyunwhan Joe, Kyungsik Ha, Jin-muk Lim, and Hong-gee Kim. "Biomedical Entity Explorer: A Web Server for Biomedical Entity Exploration." Journal of Computational Biology 28, no. 6 (2021): 619–28. http://dx.doi.org/10.1089/cmb.2020.0364.

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36

Zhang, Junsan, Youli Qu, and Shengfeng Tian. "A Novel Entity Type Filtering Model for Related Entity Finding." International Journal of Computers Communications & Control 9, no. 1 (2014): 113. http://dx.doi.org/10.15837/ijccc.2014.1.63.

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37

Rui, Qin. "The Game Between Accounting Oversight Entity and Enterprise Accounting Entity." Journal of Finance and Accounting 3, no. 4 (2015): 97. http://dx.doi.org/10.11648/j.jfa.20150304.15.

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38

Zhao, Yu, Ying Zhang, Xuhui Sui, and Xiangrui Cai. "ME3A: A Multimodal Entity Entailment framework for multimodal Entity Alignment." Information Processing & Management 62, no. 1 (2025): 103951. http://dx.doi.org/10.1016/j.ipm.2024.103951.

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39

Cai, Desheng, and Gongqing Wu. "Content-aware attributed entity embedding for synonymous named entity discovery." Neurocomputing 329 (February 2019): 237–47. http://dx.doi.org/10.1016/j.neucom.2018.10.055.

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40

Gusev, Daniil Ivanovic, and Zinaida Vladimirovna Apanovich. "How Entity Name Embedings Affect the Quality of Entity Alignment." Russian Digital Libraries Journal 26, no. 1 (2023): 52–79. https://doi.org/10.26907/1562-5419-2023-26-1-52-79.

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Cross-lingual entity alignment algorithms are designed to look for identical real-world objects in multilingual knowledge graphs. This problem occurs, for example, when searching for drugs manufactured in different countries under different names, or when searching for imported equipment. At the moment, there are several open-source libraries that collect implementations of entity alignment algorithms as well as test data sets for various languages. This paper describes experiments with several popular entity alignment algorithms applied to a Russian-English dataset. In addition to translating

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41

B., Punyashetty Kajal, Patil Tejeshwini, and Singh Chitra. "Granulomatous Mastitis: A Masquerading Entity." Indian Journal of Pathology: Research and Practice 6, no. 2 (Part-2) (2017): 398–403. http://dx.doi.org/10.21088/ijprp.2278.148x.62(pt-ii)17.10.

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42

Chopra, Himanshu. "Goldenhar Syndrome - A Rare Entity." International Journal of Science and Research (IJSR) 11, no. 12 (2022): 348–49. http://dx.doi.org/10.21275/mr221205233832.

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43

Akther, Dr Mantasha. "EVAN’S SYNDROME – A RARE ENTITY." Era's Journal of Medical Research 11, no. 1 (2024): 139–42. http://dx.doi.org/10.24041/ejmr2024.24.

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The Evans syndrome was first identified by Robert Evans in 1951. Primary thrombocytopenic purpura and acquired hemolytic anemia are r e l a t ed by a r a r e autoimmune dis e a s e . Immunologi c a l thrombocytopenia (ITP) and autoimmune hemolytic anemia (AIHA) may develop in this illness simultaneously or consecutively, and immunological neutropenia may also follow ITP. About 2–5% of ITP patients and 5–10% of warm autoimmune hemolytic anemia cases have Evans syndrome, which is often identified by exclusion. Our case report focuses on a female patient, age 15, who complained of anemia, jaundic

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44

R, Satarkar. "Chylous Ascites: A Rare Entity." Gastroenterology & Hepatology International Journal 4, no. 1 (2022): 1–3. http://dx.doi.org/10.23880/ghij-16000154.

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Chylous ascites is a distinct and rare entity. Diagnosis of chylous ascites is by milky white appearance of the ascitic fluid with triglyceride content more than 200 mg/dl. We present a case of chylous ascites due to Non Hodgkin`s Lymphoma where the diagnosis was missed in another tertiary hospital.

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45

Das, Subhashish, Harendra Kumar ML, and Anand R. "Bombay Blood A Rare Entity." JOURNAL OF CLINICAL AND BIOMEDICAL SCIENCES 01, no. 3 (2011): 122–25. http://dx.doi.org/10.58739/jcbs/v01i3.1.

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46

Raina, Aditi. "Scrotal Calcinosis – A Rare Entity." Recent Advances in Pathology & Laboratory Medicine 09, no. 3&4 (2024): 05–06. http://dx.doi.org/10.24321/2454.8642.202303.

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47

LI, Ya-Kun, Hong-Zhi WANG, Hong GAO, and Jian-Zhong LI. "Efficient Entity Resolution on XML Data Based on Entity-Describe-Attribute." Chinese Journal of Computers 34, no. 11 (2011): 2131–41. http://dx.doi.org/10.3724/sp.j.1016.2011.02131.

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48

Gabov, A. V. "Reorganization of a Peasant (Farm) Entity Functioning as a Legal Entity." Actual Problems of Russian Law 15, no. 5 (2020): 79–95. http://dx.doi.org/10.17803/1994-1471.2020.114.5.079-095.

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49

Effland, Thomas, and Michael Collins. "Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss." Transactions of the Association for Computational Linguistics 9 (2021): 1320–35. http://dx.doi.org/10.1162/tacl_a_00429.

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Abstract We study learning named entity recognizers in the presence of missing entity annotations. We approach this setting as tagging with latent variables and propose a novel loss, the Expected Entity Ratio, to learn models in the presence of systematically missing tags. We show that our approach is both theoretically sound and empirically useful. Experimentally, we find that it meets or exceeds performance of strong and state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. In particular, we find that it significantly outperforms the pre

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50

Kim, SeoHyun, YoungDuk Seo, and Doo-Kwon Baik. "Tweet Entity Linking Method based on User Similarity for Entity Disambiguation." Journal of KIISE 43, no. 9 (2016): 1043–51. http://dx.doi.org/10.5626/jok.2016.43.9.1043.

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