Grants:Programs/Wikimedia Community Fund/Rapid Fund/LexiGuess:Wikidata lexeme guessing game for the Wikimedia movement (ID: 23732328)
Applicant details
[edit]- Main Wikimedia username. (required)
Masssly
- Organization
N/A
- If you are a group or organization leader, board member, president, executive director, or staff member at any Wikimedia group, affiliate, or Wikimedia Foundation, you are required to self-identify and present all roles. (required)
Other
- Describe all relevant roles with the name of the group or organization and description of the role. (required)
I was a co‑founder of the Global Open Initiative (GOI), an independent Wikimedia-allied project that engages in Wikimedia‑related activities. (GOI is not a Wikimedia affiliate, group, or organization). I was a signatory when GOI's bank account was opened, but I have not been involved in GOI’s activities, governance, or finances for the past five years, since relocating from Ghana to Germany. My name remains on the account only for historical reasons.
This individual Rapid Fund application is made solely on my own behalf, with no connection to GOI’s work, finances, or programs, and there is no overlap between my past volunteer role and this grant request.
Main proposal
[edit]- 1. State the title of your proposal. This will also be the Meta-Wiki page title.
LexiGuess: Wikidata lexeme guessing game for the Wikimedia movement
- 2. and 3. Proposed start and end dates for the proposal.
2026-04-03 - 2026-08-03
- 4. What is your tech project about, and how do you plan to build the product?
Include the following points in your answer:
- Project goal and problem you solve
- Product strategy or project roadmap
- Technical approach (infrastructure, tech stack, key tools and services)
- Integrations or dependencies (if any)
LexiGuess is a lightweight web game that turns Wikidata lexemes into an interactive guessing experience for use during Wikimedia online events. Many Wikimedia activities have shifted online since the pandemic, and communities increasingly need simple tools that help people connect, have fun, and stay engaged. LexiGuess provides a reusable activity that supports community interaction while also offering a gentle onboarding path for people who are new to Wikidata, allowing them to explore lexemes in a playful, low‑pressure way.
The game will be built as a standalone web app using HTML, CSS, and JavaScript, with live SPARQL queries to the Wikidata Query Service. It will be deployed on Wikimedia Toolforge, which provides a stable, community‑friendly hosting environment without requiring MediaWiki extension development.
The roadmap includes:
- refactoring the prototype and improving SPARQL error handling
- implementing gameplay features (masking, hints, scoring, session manager)
- redesigning the UI for accessibility and mobile use
- deploying on Toolforge with monitoring, and
- running pilots with multiple Wikimedia communities.
Key dependencies are WDQS for data and Toolforge for hosting. No MediaWiki production integration is required.
- 5. What is the expected impact of your project, and how will you measure success?
Include the following points in your answer:
- Milestones and progress tracking
- Project impact and success metrics
I am aiming to deliver a stable, low‑maintenance game that people can play during online events to increase engagement and to also introduce newcomers to lexemes in a playful way. Because the tool is a standalone web app with no backend server, it will run reliably on its own with very little ongoing maintenance. However, to support long‑term improvement, I will still train a volunteer “standby maintainer” who can respond to community feedback and help implement small enhancements requested on the tool’s talk page
Milestones and progress tracking
[edit]Progress will be tracked through clear development checkpoints:
- refactored codebase and improved SPARQL error‑handling
- completed gameplay features (masking, hints, scoring, session manager)
- Toolforge deployment with monitoring
- successful pilot sessions with 2 to 3 langauge communities, and
- published documentation and maintainer onboarding.
Each milestone will be verified through code commits, demos, and short written updates.
Project impact and success metrics:
[edit]Impact will be measured through both technical reliability and community engagement:
- 30+ participants across pilot sessions
- 15+ editors making follow‑on Wikidata edits after playing
- participant satisfaction of 4/5 or higher
- Toolforge deployment with ≥95% uptime during pilots, and
- one trained volunteer maintainer prepared to handle feedback via phabricator, and possibly do light updates and future feature requests.
- 6. Who is your target audience, and how have you confirmed there is demand for this project? How did you engage with the Wikimedia community?
Include the following points in your answer:
- Project demand and target audience description
- Links to interaction(s) with Wikimedia community
- Evidence from community consultation such as the [Community Wishlist]
The target audience for LexiGuess includes Wikimedia event organizers, Wikidata editors, and newcomers who join online meetups, editathons, and community event. As many communities now meet online, organizers look for simple, engaging activities that help people connect. For example, every year, the Wikidata Birthday celebrations feature a coomunity call that brings peopple together to play games powered by WIkidata, which are often the highlight of the annual birthday celebration and show strong demand for interactive tools. LexiGuess responds to this need by offering a lightweight, low‑barrier game that introduces lexicograohical data (an often misunderstood part of Wikidata even for seasoned editors) in a playful way.
I shared the prototype publicly with the Wikidata community. A Dagbani‑first version was posted on the Dagbani lexeme community talk page, inviting feedback, suggestions, and optional contributions via GitHub or private channels
A matching announcement was posted on the LexiGuess talk page to invite broader community input.
- 7. How will your team predict and manage potential user security and privacy risks, and what risks do you currently see?
Include the following points in your answer:
- The level of in-house or consulted security and privacy expertise you will have available to you during delivery of this project
- How your development, testing, and deployment processes mitigate the introduction of unnecessary security or privacy risks
LexiGuess is intentionally designed to minimize security and privacy risks. It is a static, client‑side web application with no backend server, no user accounts, no data collection, and no storage of personal information. This greatly reduces the attack surface and eliminates most common risks associated with web tools. Security and privacy expertise available.
Security and privacy expertise available
[edit]I will consult Toolforge documentation, best practices, and community guidance when needed. Because the tool has no server‑side components, the security requirements are lightweight and well within the scope of standard Toolforge practices.
Development, testing, and deployment processes
[edit]The project will follow simple but effective safeguards:
* No personal data collected: The game does not log usernames, IP addresses, scores, or gameplay history. All interaction happens in the user’s browser.
- Client‑side only architecture: With no backend, there is no database, no authentication, and no user data to protect.
- SPARQL queries only: The tool reads public Wikidata content and does not write or modify anything.
- Open‑source code review: The code is public on GitHub, allowing community members to spot issues or suggest improvements.
- Toolforge hosting: Deployment uses Toolforge’s standard, well‑maintained environment, which already includes rate‑limiting, HTTPS, and isolation.
- Testing before deployment: All changes will be tested locally and in a staging environment before being deployed to Toolforge to avoid introducing unnecessary risks.
Current risks and how they are mitigated
[edit]The remaining risks are minimal:
- SPARQL query failures or unexpected data: Mitigated through improved error‑handling and safe fallbacks.
- Third‑party contributions: Managed through code review before merging.
- Toolforge downtime: Mitigated by monitoring during pilots and keeping the app stateless so it recovers instantly when Toolforge is back online.
- 8. Who is on your team, and what is your experience?
Include the following points in your answer:
- Your experience as a developer, relevant past projects
- Wikimedia SUL (developer), Gerrit, Github, Gitlab or other relevant public account handles
- Other team members, their roles and expertise
This project is led by me. I have a background in computer science and several years of experience running Bots, building small tools (such as this), userscripts (such as this), and workflows that interact with Wikidata and Wikibase. Although my professional role on the Wikidata development team is in software & community communications rather than software engineering, I work closely with developers, understand the data model well, and have practical experience maintaining lightweight tools in my volunteer capacity. This project and grant are fully in my volunteer capacity.
I have previously built SPARQL‑based tools, contributor guides, and small web utilities for community use, and I am comfortable working with client‑side JavaScript, Toolforge deployment, and open‑source workflows.
Accounts
[edit]- [[1] GitHub]
- Phabricator: (volunteer, professional)
- 9. How will the project be maintained long-term?
Include the long-term maintenance plan with maintainer(s) in your answer. If you expect the long-term maintenance to incur expenses, please list those and the plan for long-term expense coverage.
LexiGuess is a static, client‑side web application with no backend server, no database, and no user accounts. This means there are no ongoing hosting costs, no data to manage, and very few long‑term technical risks. Once deployed on Toolforge, the tool can run reliably with minimal intervention.
For long‑term sustainability, I will train one volunteer “standby maintainer”from the Wikidata:Wiki Mentor Africa community who can help respond to feedback, handle small fixes, and support future feature requests. This ensures that the tool is not dependent on a single person and that community members can participate in its upkeep.
Because the tool is lightweight and open‑source, maintenance tasks are expected to be limited to occasional bug fixes, SPARQL adjustments, or small improvements suggested by users. These can be handled within normal volunteer workflows, and no recurring expenses are anticipated. If future enhancements require additional resources, they can be proposed through community discussions or small follow‑up grants.
- 10. Under what license will your code be released, and how will you ensure the product is well documented?
Include the following points in your answer:
- Code license and compatibility with Wikimedia projects
- Documentation plan
The code will be released under the MIT License, which is fully compatible with Wikimedia projects. Documentation will be kept in the GitHub repository and on the Wikidata:LexiGuess page, including a short technical overview, maintainer notes, and a simple “How to Play” guide.
- 11. Will your project depend on or contribute to third-party tools or services?
No. LexiGuess runs entirely on Toolforge and only uses public Wikidata queries, with no external third‑party services.
- 12. Is there anything else you’d like to share about your project? (optional)
Budget
[edit]- 13. Upload your budget for this proposal or indicate the link to it. (required)
https://docs.google.com/spreadsheets/d/10mgKnT5qE-701pl3-ViVDdmyQYHDRMdBppBzPGuL8Ac/edit?usp=sharing
- 14. and 15. What is the amount you are requesting for this proposal? Please provide the amount in your local currency. (required)
4139 EUR
- 16. Convert the amount requested into USD using the Oanda converter. This is done only to help you assess the USD equivalent of the requested amount. Your request should be between 500 - 5,000 USD.
4904.42 USD
- We/I have read the Application Privacy Statement, WMF Friendly Space Policy and Universal Code of Conduct.
Yes
Endorsements and Feedback
[edit]Please add endorsements and feedback to the grant discussion page only. Endorsements added here will be removed automatically.
Community members are invited to share meaningful feedback on the proposal and include reasons why they endorse the proposal. Consider the following:
- Stating why the proposal is important for the communities involved and why they think the strategies chosen will achieve the results that are expected.
- Highlighting any aspects they think are particularly well developed: for instance, the strategies and activities proposed, the levels of community engagement, outreach to underrepresented groups, addressing knowledge gaps, partnerships, the overall budget and learning and evaluation section of the proposal, etc.
- Highlighting if the proposal focuses on any interesting research, learning or innovation, etc. Also if it builds on learning from past proposals developed by the individual or organization, or other Wikimedia communities.
- Analyzing if the proposal is going to contribute in any way to important developments around specific Wikimedia projects or Movement Strategy.
- Analysing if the proposal is coherent in terms of the objectives, strategies, budget, and expected results (metrics).
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