Annotatsiya

Vikipediya, ochiq ensiklopediya

Izoh-bu hujjat yoki boshqa ma'lumotlarning ma'lum bir nuqtasi bilan bog'liq qo'shimcha ma'lumotlar hisoblanadi. Bu sharh yoki tushuntirishni o'z ichiga olgan eslatma ko'rinishida bo'lishi mumkin[1]. Izohlar ba'zan kitob sahifalari chegarasida taqdim etiladi. Turli xil raqamli ommaviy axborot vositalarining izohlari uchun veb-izoh va matn izohi mavjud bo'ladi.

Adabiyot va ta'lim[tahrir | manbasini tahrirlash]

Matnli stipendiya[tahrir | manbasini tahrirlash]

Matnli stipendiya-bu tushunishni osonlashtirish maqsadida matnlar va jismoniy hujjatlarga qo'shimcha tarixiy kontentni tavsiflash yoki qo'shish uchun ko'pincha izohlash texnikasidan foydalanadigan soha[2].

Talaba foydalanadi[tahrir | manbasini tahrirlash]

Talabalar ko'pincha asosiy iboralarga osongina murojaat qilish yoki o'qishga yordam berish uchun kitoblardagi parchalarni ajratib ko'rsatishdan foydalanishadi.

Izohli bibliografiyalar shunchaki manbani aniqlaydigan odatiy bibliografik ma'lumotlardan tashqari, har bir manbaning dolzarbligi,munozarasi va sifatiga sharh qo'shadi.

Matematik ifoda izohi[tahrir | manbasini tahrirlash]

Matematik ifodalarni (belgilar va formulalar) tabiiy til ma'nolari bilan izohlash mumkin deb qaraladi. Bu ajratish uchun juda muhimdir, chunki belgilar turli xil qiymatlarga ega bo'lishi mumkin (masalan, "E" "energiya" yoki "kutish qiymati" bo'lishi mumkin va hokazo)[3][4]. Annotatsiya jarayoni tavsiyalar orqali osonlashtirilishi va tezlashtirilishi mumkin, masalan, Vikimedia tomonidan joylashtirilgan "AnnoMathTeX" tizimidan foydalanish mumkin[5][6][7].

O'rganish va o'qitish[tahrir | manbasini tahrirlash]

Kognitiv nuqtayi nazardan, izohlash orqali o'rganish va o'qitishda muhim rol o'ynaydi. Yo'naltirilgan farqning bir qismi sifatida o'quvchilar e'tiborini o'ziga xos vizual jihatlarga qaratishga yordam berish uchun tasvirlarning jihatlarini ta'kidlash, nomlash yoki belgilar bilan sharhlashni o'z ichiga oladii.Boshqacha qilib aytganda, bu tipologik vakolatxonalarni (madaniy jihatdan mazmunli toifalar), topologik vakolatxonalarga (masalan, tasvirlar) tayinlashni anglatadi[8]. Bu, ayniqsa, tibbiyot shifokorlari kabi mutaxassislar vizualizatsiyani batafsil talqin qilgan paytda va boshqalarga yetkazishda, masalan, raqamli texnologiyalar yordamida tushuntirganda juda muhimdir omil hisoblanadi[9]. Bu yerda izoh turli darajadagi bilimlarga ega bo'lgan interaktantlar o'rtasida umumiy til o'rnatishning bir usuli bo'lishi mumkin[10][11].

YouTube Haqida[tahrir | manbasini tahrirlash]

Izohlar 2019-yil 15-yanvarda YouTube-dan o'n yillik xizmatdan so'ng olib tashlangan. Ular foydalanuvchilarga video paytida paydo bo'lgan ma'lumotlarni taqdim etishga ruxsat berishgan, ammo YouTube kichik mobil ekranlarda yaxshi ishlamayotganligini va suiiste'mol qilinayotganlarini ko'rsatgan.

Dasturiy ta'minot va muhandislik[tahrir | manbasini tahrirlash]

Matn hujjatlari[tahrir | manbasini tahrirlash]

XML va HTML kabi belgilash tillari matnni ushbu matndan sintaktik ravishda ajratib turadigan tarzda izohlovchi hisoblanadi.Ular kerakli vizual taqdimot yoki mashinada o'qiladigan sxemantik ma'lumotlar haqida axborot qo'shish uchun ishlatilishi mumkin[1].

Jadval ma'lumotlari[tahrir | manbasini tahrirlash]

Bunga CSV va XLS kiradi. Jadval ma'lumotlariga semantik izohlarni berish jarayoni semantik yorliq deb ataladi. Semantik yorliq-bu ontologiyalardan jadval ma'lumotlariga izohlarni berish jarayoni..[12][13][14][15] Ushbu jarayon semantik izoh deb ham ataladi.[16][15] Semantik yorliq ko'pincha (yarim)avtomatik tarzda amalga oshiriladi. Semantik yorliqlash texnikasi shaxs ustunlarida ishlaydi, [4] raqamli ustunlar,[1][3][6][7] koordinatalari,[8] va yana.[15][7]

Semantik Yorliqlash Texnikasi[tahrir | manbasini tahrirlash]

There are several semantic labelling types which utilises machine learning techniques. These techniques can be categorised following the work of Flach[17] as follows: geometric (using lines and planes, such as Support-vector machine, Linear regression), probabilistic (e.g., Conditional random field), logical (e.g., Decision tree learning), and Non-ML techniques (e.g., balancing coverage and specificity[18]). Note that the geometric, probabilistic, and logical machine learning models are not mutually exclusive.[17]

T2D[19] semantik yorliqlash uchun eng keng tarqalgan oltin standartdir. T2D ning ikkita versiyasi mavjud: T2Dv1 (ba'zan T2D deb ham ataladi) va T2Dv2.[19] Boshqa ma'lum ko'rsatkichlar SemTab Challenge bilan nashr etilgan.[20]

  1. „Definition of ANNOTATION“. www.merriam-webster.com.
  2. Greetham, David C.. Textual Scholarship: An Introduction, Garland Reference Library of the Humanities. Routledge [1992], 28 October 2015. ISBN 978-1-136-75579-8. 
  3. Moritz Schubotz; Philipp Scharpf (12 September 2018). "Introducing MathQA: a Math-Aware question answering system". Information Discovery and Delivery (Emerald Publishing Limited) 46 (4): 214–224. doi:10.1108/IDD-06-2018-0022. 
  4. Scharpf, P.Expression error: Unrecognized word "et". (2018). "Representing Mathematical Formulae in Content MathML using Wikidata". ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018). https://openreview.net/pdf?id=BkZ1IIZu-H. 
  5. „AnnoMathTeX Formula/Identifier Annotation Recommender System“.
  6. Philipp ScharpfExpression error: Unrecognized word "et". (17 September 2019). "AnnoMathTeX : a formula identifier annotation recommender system for STEM documents". Proceedings of the 13th ACM Conference on Recommender Systems (RecSys 2019): 532–3. doi:10.1145/3298689.3347042. ISBN 9781450362436. https://www.gipp.com/wp-content/papercite-data/pdf/scharpf2019b.pdf. 
  7. Philipp Scharpf; Moritz Schubotz; Bela Gipp (14 April 2021). "Fast Linking of Mathematical Wikidata Entities in Wikipedia Articles Using Annotation Recommendation". Companion Proceedings of the Web Conference 2021 (WWW '21 Companion): 602–9. doi:10.1145/3442442.3452348. ISBN 9781450383134. https://www.gipp.com/wp-content/papercite-data/pdf/scharpf2021.pdf. 
  8. Pea, R.D. „Video-as-Data and Digital Video Manipulation Techniques for Transforming Learning Sciences Research, Education, and Other Cultural Practices“,. The International Handbook of Virtual Learning Environments. Springer, 2006 — 1321–93 bet. DOI:10.1007/978-1-4020-3803-7_55. ISBN 978-1-4020-3803-7. 
  9. Coiera, E. (2014). "Communication spaces". J Am Med Inform Assoc 21 (3): 414–422. doi:10.1136/amiajnl-2012-001520. PMID 24005797. PMC 3994845. //www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3994845. 
  10. Clark, Herbert H.. Using Language. Cambridge University Press, 1996. ISBN 978-0-521-56745-9. 
  11. Pimmer, C.; Mateescu, M.; Zahn, C.; Genewein, U. (2013). "Smartphones as multimodal communication devices to facilitate clinical knowledge processes — a randomized controlled trial". Journal of Medical Internet Research 15 (11): e263. doi:10.2196/jmir.2758. PMID 24284080. PMC 3868983. //www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3868983. 
  12. Alobaid, Ahmad; Kacprzak, Emilia; Corcho, Oscar (January 1, 2021). "Typology-based semantic labeling of numeric tabular data". Semantic Web 12 (1): 5–20. doi:10.3233/SW-200397. https://content.iospress.com/articles/semantic-web/sw200397. 
  13. Taheriyan, Mohsen; Knoblock, Craig A.; Szekely, Pedro; Ambite, José Luis (March 1, 2016). "Learning the semantics of structured data sources". Web Semantics: Science, Services and Agents on the World Wide Web 37 (C): 152–169. doi:10.1016/j.websem.2015.12.003. https://doi.org/10.1016/j.websem.2015.12.003. 
  14. Alobaid, Ahmad; Corcho, Oscar (2018). Faron Zucker, Catherine; Ghidini, Chiara; Napoli, Amedeo et al.. eds. "Fuzzy Semantic Labeling of Semi-structured Numerical Datasets" (en). Knowledge Engineering and Knowledge Management. Lecture Notes in Computer Science (Cham: Springer International Publishing) 11313: 19–33. doi:10.1007/978-3-030-03667-6_2. ISBN 978-3-030-03667-6. https://link.springer.com/chapter/10.1007/978-3-030-03667-6_2. 
  15. 15,0 15,1 15,2 Alobaid, Ahmad; Corcho, Oscar (2022-03-15). "Balancing coverage and specificity for semantic labelling of subject columns" (en). Knowledge-Based Systems 240: 108092. doi:10.1016/j.knosys.2021.108092. ISSN 0950-7051. https://www.sciencedirect.com/science/article/pii/S095070512101159X. 
  16. Hassanzadeh. „Understanding a large corpus of web tables through matching with knowledge bases: an empirical study“ (2015-yil 17-dekabr).
  17. 17,0 17,1 Flach, Peter. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge: Cambridge University Press, 2012. DOI:10.1017/cbo9780511973000. ISBN 978-1-107-09639-4. 
  18. Alobaid, Ahmad; Corcho, Oscar (2022-03-15). "Balancing coverage and specificity for semantic labelling of subject columns" (en). Knowledge-Based Systems 240: 108092. doi:10.1016/j.knosys.2021.108092. ISSN 0950-7051. https://www.sciencedirect.com/science/article/pii/S095070512101159X. 
  19. 19,0 19,1 Bizer. „Web Data Commons - T2Dv2“. webdatacommons.org. Qaraldi: 2022-yil 18-iyul.
  20. „Semantic Web Challenge on Tabular Data to Knowledge Graph Matching“. www.cs.ox.ac.uk. Qaraldi: 2022-yil 30-sentyabr.