معناشناسی در سامانه‌های برچسب‌گذاری اجتماعی: یک مرور نظام‌مند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترای علم اطلاعات و دانش‌شناسی، دانشگاه شیراز، شیراز. ایران

2 دانشیار گروه علم اطلاعات و دانش‌شناسی، دانشگاه شیراز، شیراز، ایران

10.30484/nastinfo.2020.2357.1906

چکیده

هدف: مرور نظام­مند پژوهش­های حوزه معناشناسی در سامانه­های برچسب­گذاری اجتماعی، به منظور شناسایی شاخه‌­های موضوعی قابل توجه پژوهشگران، مرور راهکارهای رفع یا کاهش اثرات مسائل معناشناسی بر بازیابی اطلاعات در این سامانه‌­ها و شناسایی شکاف‌­های پژوهشی این حوزه است.
روش پژوهش : پژوهش به روش مرور نظام‌­مند انجام گرفته است. به این منظور، با جستجو در پایگاه‌های اطلاعاتی، 101 مقاله پژوهشی به زبان انگلیسی در بازه زمانی 2003-2019 انتخاب و پس از پالایش، 44 پژوهش تحلیل شد.
یافته­ها: محورهای موضوعی مهم شامل طراحی یک سامانه برچسب­‌گذاری معنایی، استفاده از وردنت برای تعیین رابطه معنایی میان برچسب‌ها، بهره­گیری از بافت برای ابهام‌زدایی از معنای برچسب، تولید الگوریتم برچسب­‌گذاری معنایی خودکار بودند. خلأهای پژوهشی برای انجام پژوهش‌های آتی عبارتند از: ابداع روشی برای شناسایی منابع حاوی معنایی خاص از یک برچسب بدون نیاز به بررسی همه منابع، بررسی امکان استفاده از روش‌های خوشه‌بندی برچسب‌ها برای خوشه‌بندی منابع.
نتیجه‌­گیری: با توجه به شکاف‌­های پژوهشی همچنان مسئله معنا در سامانه­های برچسب‌­گذاری می‌تواند از حوزه‌های مهم مطالعات پژوهشی سازماندهی اطلاعات باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Semantics in Social Tagging Systems: A Systematic Review

نویسندگان [English]

  • Z. Honarjooyan 1
  • mahdieh mirzabeigi 2
1 PhD Candidate, Department of Knowledge and Information Science, Faculty of Education and Psychology, Shiraz University, Shiraz, Iran
2 Associate Professor, Department of Knowledge and Information Science, Faculty of Education and Psychology, Shiraz University, Shiraz, Iran
چکیده [English]

Purpose: The objective of the present study has been to systematically review semantic research studies on social tagging systems in order to identify the researchers’ areas of interest, to investigate the impact of semantic issues on information retrieval in ​such systems, and to identify research gaps in this area​​.
Methodology: Ninety-eight studies were found by searching ​ relevant ​databases. After initial investigation and consultation with two specialists in the field, 41 studies published in 2003-2018 were reviewed.
Findings: Important topics of semantic research on social tagging systems include producing an automatic semantic tagging algorithm, designing a semantic tagging system, producing an algorithm, extracting hierarchical relationship from a set of tags, and using WordNet to determine semantic relationships among tags. In addition, research gaps identified include devising a method for identifying sources containing a specific meaning of a tag without having to review all sources, exploring the possibility of using clustering methods to cluster sources or users of folksonomies, and designing a semantic tagging system which is user-friendly. All of these issues should be taken into account in future research.
Conclusion: Given the gaps identified, the subject of semantics in tagging systems needs further investigation, as it has a direct impact on search and retrieval by these systems.

کلیدواژه‌ها [English]

  • Folksonomies
  • Social Tagging Systems
  • Semantic Relations
  • Information retrieval
  • Systematic review
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