Saturday, November 22, 2025

The Digital Journey of Libraries: Tracing Service Evolution Across Web Generations (1.0 to 5.0)

LibCognizance digital libraries , Library 2.0

Explore technological shifts from Web 1.0 to Web 5.0 and their impact on library services, including Library 2.0, Semantic Web, AI, IoT, linked data, and decentralized user identity.

The Digital Journey of Libraries: Tracing Service Evolution Across Web Generations (1.0 to 5.0)

The evolution of the Web from its early static pages to today’s intelligent, predictive ecosystems has profoundly shaped how libraries design, deliver, and enhance their services. Each new Web generation has redefined the way users search, interact with, and retrieve information in libraries. Libraries, as knowledge hubs, have continuously adapted by integrating new technologies such as social tools, semantic metadata, artificial intelligence, IoT, etc. Here is a a clear timeline depicting how library services have transformed across Web 1.0 to Web 5.0, from simple information providers to dynamic, user-centric, and intelligent learning environments.

Evolution of Library Services Across Web Generations

Web 1.0 Era (1990s): The Static Library Web Presence

Characteristics: Static, read-only HTML pages.
Library Services: Basic informational websites, rules, timings, downloadable PDFs.
OPACs: Mostly offline; early static online catalogs.
User Experience: Minimal interactivity, one-way information flow.

Web 2.0 Era (2000s): Library 2.0 — Social, Participatory, Collaborative

Interactive library portals, user-generated content, tagging, book reviews.
Online OPACs, Blogs, podcasts, YouTube tutorials.
Social media integration (Facebook, Twitter).
Virtual Reference Services (Ask-a-Librarian, chat-based help).
User Experience: Users become contributors and active participants.

Web 3.0 Era (2010s–Early 2020s): Smart, Semantic, AI-Enhanced Libraries

Semantic metadata, linked data, structured data.
Intelligent discovery systems & AI-powered search.
Recommendation engines in OPACs.
BIBFRAME replacing MARC; linked-data repositories.
Researcher identity systems: IRINS, VIDWAN.
User Experience: Personalized, context-aware services.

Web 4.0 Era (2020s–Emerging): Intelligent, Ambient, Symbiotic Libraries

AI-driven library assistants and chatbots.
IoT-based smart libraries (RFID, sensors, smart shelves).
Voice/gesture-controlled interfaces.
AR/VR-based learning experiences.
User Experience: Predictive and proactive services.

Web 5.0 (Proposed): Decentralized, User-Owned Library Ecosystems

Self-sovereign digital identities (DIDs).
User-controlled borrowing history & privacy.
Decentralized scholarly publishing (verified authorship & citations).
Cross-library identity portability.
User Experience: Maximum autonomy & data control.

Final Timeline of Web Evolution & Library Services

Web Stage	Years	Core Features	Library Service Evolution
Web 1.0	1990s	Static, read-only	Basic sites, static OPAC
Web 2.0	2000s	Social, collaborative	Interactice library portals, virtual reference
Web 3.0	2010s–2020s	Semantic, AI, linked data	Smart OPAC, AI search, BIBFRAME
Web 4.0	2020s (Emerging)	IoT, AI agents, symbiotic	Chatbots, IoT, AR/VR, predictive services
Web 5.0	Proposed	Decentralized, user-owned	Self-sovereign digital identities, decentralized publishing

In Summary

The journey from Web 1.0 to the emerging ideas of Web 5.0 reflects more than just technological change. It represents a fundamental shift in how libraries interact with their users. From static OPACs to AI-powered assistants, libraries have consistently embraced innovation to stay relevant, inclusive, and user-driven. As we look ahead, the next generation of library services will likely be more intelligent, autonomous, and privacy-preserving, giving users greater control while enabling libraries to provide deeply personalized and seamless experiences. With this continuous evolution in web technoogies, libraries remain at the forefront to implement them and bring changes that shape the future of learning.

References

Understanding Wikimedia Projects: Beyond Wikipedia

LibCognizance wiki projects , wikidata

The Wikimedia Foundation is a nonprofit organization that hosts and manages some of the world’s largest free knowledge projects. Its mission is to empower people around the globe to share and access knowledge freely. While Wikipedia is its most famous project, the Wikimedia family includes several other initiatives designed to preserve, organize, and disseminate information in various forms.

Understanding Wikimedia Projects: Beyond Wikipedia

Understanding Wikimedia Projects

Beyond Wikipedia: The Ecosystem of Free Knowledge

🌍 What Is Wikimedia?

The Wikimedia Foundation is a nonprofit organization that hosts and manages some of the world’s largest free knowledge projects. Its mission is to empower people around the globe to share and access knowledge freely. While Wikipedia is its most famous project, the Wikimedia family includes several other initiatives designed to preserve, organize, and disseminate information in various forms.

📚 Major Wikimedia Projects

Wikipedia

The world’s largest free encyclopedia, collaboratively written and constantly updated by volunteers worldwide. Articles span history, science, culture, and more.

Wikidata

A structured, machine-readable database that stores factual data used across Wikipedia and other Wikimedia projects. It ensures data consistency in multiple languages.

Wikimedia Commons

A vast repository of freely licensed images, videos, and audio files available for use in educational and creative works worldwide.

Wikisource

A digital library that hosts original texts, including laws, historical documents, and classical works in the public domain.

Wikibooks

A collaborative platform offering free, open-content textbooks and manuals covering a wide range of academic and technical subjects.

Wiktionary

A multilingual dictionary and thesaurus that includes definitions, etymologies, translations, and pronunciations across languages.

Wikiquote

A collection of verified quotations from notable figures, literary works, and cultural sources, promoting inspiration and thought.

Wikiversity

An open learning community that provides free educational resources, tutorials, and research materials for lifelong learners.

Wikivoyage

A free, collaborative travel guide that offers practical information, cultural insights, and destination advice from contributors worldwide.

Wikispecies

A directory of all forms of life, documenting species classification and biological taxonomy for use by researchers and enthusiasts.

💡 Why These Projects Matter

Together, Wikimedia projects form an interconnected ecosystem that preserves and spreads knowledge freely, without barriers of cost or access. They support education, cultural preservation, and scientific research on a global scale — empowering communities and promoting digital equity.

🔖 References

Buzzwords Unplugged: The Vocabulary Powering the Knowledge Age

LibCognizance

The world of information and knowledge is evolving faster than ever, driven by digital transformation, data-driven decision-making, and emerging technologies. Along with this growth comes a wave of terms that capture new trends, methods, and innovations. While some may sound overused, many of these words reflect real shifts in how we create, share, and apply knowledge.

Here are some impactful buzzwords in the information and knowledge domain. Whether you’re a librarian, researcher, or knowledge worker, understanding these terms will not only keep you updated but also help you navigate the changing landscape of information science.

Buzzwords: Information and knowledge domain

Misinformation

False or inaccurate information—getting the facts wrong.

Disinformation

False information which is deliberately intended to mislead—intentionally misstating the facts.

InfoSphere

A holistic ecosystem of interconnected information — the digital universe of data, platforms, and communication channels.

Datafication

Turning everyday activities into quantified data to enable analysis, automation, and decision-making.

Infodemic

An overwhelming flood of both accurate and misleading information, especially during a crisis, that makes finding reliable facts difficult.

Infonomics

Treating information as an economic asset — valuing, measuring, and managing data for business advantage.

InfoResilience

The ability to manage, adapt, and recover from information overload, disruptions, or misinformation.

Cognitive Computing

AI systems that mimic human thinking to process unstructured information and assist complex decision-making.

Knowledge Graphs

Structured networks that map relationships between people, concepts, and things to provide context-aware information.

Dark Data

Collected but unused or unanalyzed data that often remains hidden in systems, representing untapped value.

Zero-Knowledge Proofs (ZKP)

A cryptographic method that lets one party prove knowledge of information without revealing the information itself.

Algorithmic Transparency

Clarity about how algorithms process data and make decisions, for accountability and fairness.

Filter Bubble

A personalized content ecosystem where algorithms limit exposure to diverse viewpoints, creating informational isolation.

Digital Twin

A virtual replica of a physical system or object that is updated with real-world data to simulate and optimize performance.

Explainable AI (XAI)

Techniques that make machine decisions and information processes understandable to humans, improving trust and oversight.

Information Sovereignty

The right or policy to control where information is stored, who can access it, and under which legal jurisdiction.

Infodiversity

Ensuring a variety of perspectives and sources in information ecosystems to prevent dominance by a single viewpoint.

Data Feminism

An approach that centers equity and ethics in data practice, exposing bias and promoting inclusion in information systems.

Edge Intelligence

Processing and acting on information directly at devices (edge) rather than sending everything to centralized servers.

Semantic Interoperability

Ensuring diverse information systems share common meaning so that data exchanged is understood consistently.

Data Democratization

Making data and insights accessible to non-experts so more people can use information in decisions.

Information Hygiene

Practices such as fact-checking, source evaluation, and careful sharing to maintain quality and credibility of information.

Delhi High court banned Sci-Hub and Libgen in India

LibCognizance Access restricted , Libgen

The Delhi High Court has ordered the immediate blocking of shadow libraries like Sci-Hub and Libgen in India, directing MeitY and DoT to act within 72 hours. The move comes after publishers Elsevier, Wiley, and the American Chemical Society accused these sites of copyright violations and piracy. Many researchers and academic groups have defended Sci-Hub and Libgen, citing their importance for unfunded scholars and the Copyright Act’s provisions for fair use in research.

“Misinformation by Machine: The Rise of LLM Grooming in AI Systems”

LibCognizance Disinformation , LLM Grooming

Artificial intelligence has now become a part of the lives of researchers, writers, officials and general people, impacting their decisions and planning. But what if these LLM models, such as ChatGPT, Claude, Gemini, etc., are being massively fed with wrong information and leading the users in a direction not meant for them?

#ResponsibleAI #LLMGrooming #AITrust #misinformation #Disinformation

The term "LLM grooming" was brought to light by The American Sunlight Project (ASP) [1]. In a report, the term was introduced, which describes how disinformation is being propagated by AI. These algorithmic machines are being trained massively behind the curtain to change the perception of their users about some political propaganda. Researchers from the ASP identified and investigated a network of pro-Russia propaganda websites named the Pravda network. A key finding of their research was the belief that the Pravda network may have been custom-built to flood large language models (LLMs) with pro-Russia content.

In a special reality check report released by NewsGuard [2] recently in March 2025, it has been confirmed that a well-funded, Moscow-based "news" network, named Pravda (Russian for "truth"), has been deliberately infecting Western artificial intelligence (AI) tools with Russian propaganda. They are not directly influencing human readers; they are manipulating around 10 leading generative AI models to achieve their disinformation goals by saturating search results and web crawlers with pro-Kremlin falsehoods in a significant percentage of their responses.

The Atlantic Council's DFRLab also confirmed that major AI models have cited Pravda network content.

How will you calculate the impact factor of a journal?

LibCognizance Citation metrics , Clarivate Analytics

Impact factor calculation explained by lib cognizance

Spiral of Scientific Method

LibCognizance Nadir , Spiral of Scientific Method

sp;

How to Cite ChatGPT in APA, MLA, and Chicago Styles: A Complete Guide

LibCognizance #AcademicWriting , #ChatGPT #CitingAI #CitationStyles #APAStyle #MLAStyle #ChicagoStyle

As AI tools like ChatGPT become more prevalent in academic and research writings, proper citation has become essential. This guide covers how to cite ChatGPT and other AI-generated content in APA, MLA, and Chicago styles, with examples and best practices for ethical use in scholarly work. However, the government policies, educational institutes, and style guides across the world are still working on the guidelines. This article presents the updated information to date.

Why Proper Citation of AI Matters

When using ChatGPT or similar AI tools in your research, proper citation serves two crucial purposes:

Academic integrity: Academic integrity, according to the ICAI, involves a commitment to honesty, trust, fairness, respect, responsibility, and courage. These fundamental values can be applied to many circumstances in the learning environment, including how to improve our academic writing skills to avoid plagiarism. Learning how to use AI properly, including how to cite ChatGPT and other tools, is important to pursuing and achieving academic integrity. Academic integrity acknowledges the use of AI-generated content.
Transparency: Allows readers to understand how AI contributed to your work. Citing your sources is an essential part of ethical and professional writing.

Important: Always check your institution's or publisher's guidelines before using AI tools in academic work, as policies vary widely.

General Principles for Citing AI Content

Describe how you used the AI tool in your methodology or introduction
Include the exact prompts you used
Credit the creator of the AI model (e.g., OpenAI for ChatGPT)
Never present AI-generated text as your own original writing
Verify all facts and sources provided by AI tools

How to Cite ChatGPT in Different Citation Styles

APA Style (7th Edition)

The American Psychological Association provides specific guidelines for citing ChatGPT and similar AI tools:

Element	Format	Example
Reference List Entry	Author. (Year). Title (Version) [Descriptor]. URL	OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat
In-text Citation	(Author, Year)	(OpenAI, 2023)

Example in context:
When prompted with "Explain the left brain-right brain divide," ChatGPT responded that "the notion that people can be characterized as 'left-brained' or 'right-brained' is considered to be an oversimplification" (OpenAI, 2023).

MLA Style (9th Edition)

The Modern Language Association recommends this format for citing ChatGPT:

Element	Format	Example
Works Cited Entry	"Prompt text" prompt. ChatGPT, Version, OpenAI, Date, URL	"Examples of cognitive biases" prompt. ChatGPT, 13 Feb. version, OpenAI, 16 Feb. 2023, chat.openai.com
In-text Citation	("Shortened prompt")	("Examples of cognitive")

Chicago Style (17th Edition)

Chicago style treats ChatGPT content as personal communication:

Element	Format	Example
Footnote	1. Text generated by ChatGPT, Date, OpenAI, URL	1. Text generated by ChatGPT, March 31, 2023, OpenAI, https://chat.openai.com
Subsequent Citations	2. ChatGPT	2. ChatGPT

Best Practices for Using ChatGPT in Academic Work

When to Cite ChatGPT

When quoting or paraphrasing AI-generated text
When using AI for editing or translation
When AI contributes significantly to your research process
If you’re using ChatGPT responses as a primary source

When Not to Rely on ChatGPT

As a source for factual information (it may "hallucinate" false details)
For references or citations (it often creates fake sources)
To write complete papers or sections (may be considered plagiarism)

Warning: ChatGPT's knowledge is current only until its training cutoff date (e.g., January 2022 for ChatGPT-3.5). It cannot access or verify real-time information.

Special Cases

Including Long ChatGPT Responses

For extensive AI-generated content, APA recommends:

Include a brief excerpt in your main text with citation
Place the full transcript in an appendix
Reference the appendix in your citation

When asked about neural plasticity, ChatGPT provided a detailed explanation including that "the brain's ability to reorganize itself is most pronounced in childhood but continues throughout life" (OpenAI, 2023; see Appendix A for full transcript).

Citing Other AI Tools

The same principles apply to tools like Google Bard or Bing AI. Adapt the citation format with the appropriate:

Company name (e.g., Google for Bard)
Tool name
Version information
Access URL

There are many questions over the use of ChatGPT in academic writings like, "Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT? What are the ? These questions are still under debate and we haven't reached on the final conclusions. Researchers, editors, instructors, and others are actively debating and creating parameters and guidelines.

References:

1. ChatGPT Citations | Formats & Examples (scribbr.com)

2. How to cite ChatGPT (apa.org)

3. How to Cite ChatGPT Sources | APA, MLA, Chicago, Vancouver - Wordvice

4. When and how to cite ChatGPT and AI in MLA / APA formats (turnitin.com)

5. How to Cite ChatGPT: A Complete Guide (linkedin.com)

6. What is Academic Integrity?

7. Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence

India's initiatives under IndiaAI Mission

LibCognizance AI , AI Compute Portal

The IndiaAI Mission: an MEITY initiative, launched by the Government of India in March 2024. Cabinet has approved over Rs.10,300 Crore for IndiaAI Mission which aims to build a comprehensive ecosystem that fosters AI innovation by democratizing computing access, enhancing data quality, developing indigenous AI capabilities, attracting top AI talent, enabling industry collaboration, providing startup risk capital, ensuring socially impactful AI projects, and promoting ethical AI. This mission drives responsible and inclusive growth of India's AI ecosystem through following seen pillars.

AIKosha: A Big Leap Toward India’s AI Mission

LibCognizance AI Compute Portal , AI Mission in India

What is AIKosha?

AIKosha is a platform that provides repository of datasets, models and use cases to enable AI innovation. It is launched by India's Ministry of Electronics and Information Technology (MeitY) for its ambitious AI Mission on March 6, 2025. The IndiaAI Mission is structured around seven core pillars, focusing on democratizing AI access, enhancing data quality, and ensuring ethical AI practices.

AIKosha is a large corpus of datasets that is built in India for growth of AI in country. Through access of curated datasets, advanced tools, and a collaborative space people can turn AI ideas into impactful solutions. It also features AI sandbox capabilities through an integrated development environment along with tools and tutorials. Speaking at the Raisina Dialogue 2025 in New Delhi, Union Minister for Electronics and IT, Ashwini Vaishnaw informed that the government has also set up a common compute structure called IndiaAI Compute Portal. He said that the MoU between the India AI Mission and the Indian Parliament was signed as Parliament has a vast dataset in multiple languages accumulated over a long period. To take the lead in AI by 2047, minister emphasized that India must have their own LLMs, continuously invest in new technologies and ensure that universities must upgrade their curricula to bring out more experts in the field.

After meeting with the founder of the Bill and Melinda Gates Foundation, Bill Gates in New Delhi. Mr. Vaishnaw also said that the Memorandum of Understanding (MoU) will be signed soon between IndiaAI mission and the Bill and Melinda Gates Foundation emphasizing that the AI solutions for better crops, stronger healthcare, smarter education and climate resilience are need of the present time.

Key takeaways?

Unified AI Resource Platform

Comprehensive Repository: AIKosha offers access to over 300 non-personal datasets and 80+ AI models, serving as a central hub for AI development. These datasets include those from the MeitY’s Digital India Bhashini division, with one dataset containing 1,684 hours of labelled speech data across 12 Indian languages, which could help develop tools for automatic speech recognition. Private companies such as Sarvam AI and Ola Krutrim have also uploaded their models to AIKosha.

Integrated Development Environment: The platform includes an AI sandbox with tools, tutorials, and secure APIs, facilitating seamless AI model training and deployment. According to Union electronics and IT minister Ashwini Vaishnaw Startups, researchers, application developers and others can now access 14,000 GPUs (graphics processing units) on the IndiaAI Compute Portal, with 4,000 more in the pipeline.

A GPU is a specialized chip built to handle massive numbers of operations in parallel. Training AI involves huge amounts of data and computation. GPUs can process many calculations in parallel, which speeds up training massively compared to CPUs.

Ethical and Secure Data Handling

Non-Monetized Data Access: The government has clarified that there are no plans to monetize the non-personal data on AIKosha, ensuring free and open access for users. Users can download the datasets by first signing up for DigiLocker using their mobile number, while organisations must register via the ministry of electronics and information technology’s (MeitY) Entity Locker using Aadhaar. This approach aims to ensure platform security and the appropriateness of the datasets.

Data Privacy Compliance: AIKosha adheres to India's data protection laws, including the Information Technology Act, 2000, and the Digital Personal Data Protection Act, ensuring responsible data usage.

Support for Indigenous AI Development

Foundation for India's AI Models: AIKosha is instrumental in developing India's own foundational AI models, including large language models tailored to the country's diverse needs.

Collaboration with Government Departments: Datasets from various ministries, such as agriculture and weather forecasting, are integrated into the platform, enriching the data pool for AI applications.

Part of a Broader AI Ecosystem

Complementary Initiatives: AIKosha is part of the larger IndiaAI Mission, which includes the AI Compute Portal providing access to over 14,000 GPUs at subsidized rates, and programs like the IndiaAI Startups Global Acceleration Program and AI Competency Framework for public sector officials.

Focus on Skill Development: Initiatives like the India AI Future Skills, the AI Competency Framework for Public Sector Officials, iGOT-AI Mission Karmayogi, an AI-powered personalized learning platform, and the establishment of AI Data Labs in Tier II and III cities aim to build a robust AI talent pool across the country.

AIKosha is a significant step in India's AI journey, providing a secure platform for AI innovation and development. The launch of AIKosha, the AI Compute Portal, and other strategic AI initiatives marks a transformative moment for India’s digital future, reinforcing its status as a pioneer in AI innovation and adoption.

References:

1. AIKosh (indiaai.gov.in)

2. https://pib.gov.in/PressReleasePage.aspx?PRID=2108961

3. https://www.newsonair.gov.in/large-public-datasets-crucial-for-growth-of-ai-in-india-union-minister-ashwini-vaishnaw/

4. https://www.newsonair.gov.in/health-minister-j-p-nadda-bill-gates-discuss-shared-commitment-to-ensure-affordable-health-care-for-all/

5. https://www.youtube.com/watch?v=2IG5VFokosA

6. https://www.facebook.com/meityindia/videos/aikosha-a-platform-that-provides-repository-of-datasets-models-and-use-cases-to-/4053665464955746/

7. https://the-captable.com/2025/02/deepseek-ai-kosh-india-meity-public-dataset/

8. No plans to monetise non-personal data on AIKosha: MeitY informs Rajya Sabha | Latest News India - Hindustan Times

9. https://pib.gov.in/PressReleasePage.aspx?PRID=2108961

10. IndiaAI Mission Turns One; Strengthens AI Ecosystem with AIKosha, Compute Portal and Key Innovations - Elets eGov (eletsonline.com)

11. https://egov.eletsonline.com/2025/03/indiaai-mission-turns-one-strengthens-ai-ecosystem-with-aikosha-compute-portal-and-key-innovations/?utm_source=chatgpt.com

12. https://www.livemint.com/technology/ai-kosh-platorm-data-sets-ai-training-models-gpu-access-live-chatgpt-nvidia-amd-yotta-openai-chatgpt-intel-aws-11741270366709.html

13. https://futuretech.media/india-takes-a-major-step-in-ai-development-with-ai-kosh-and-gpu-initiatives/

14. https://pib.gov.in/PressReleasePage.aspx?PRID=2108961

Preservation Techniques

LibCognizance Audio-visual Preservation , Digital Preservation

Preservation techniques vary based on the type of material and the risks involved (e.g., data corruption, paper deterioration, or film degradation). Digital methods like migration, emulation, and cloud storage are essential for modern archives, while traditional techniques like microfilming, deacidification, and encapsulation continue to protect physical records.