Knowledge Bomb 💣

A quiz game, a brain imagery generator, an ice breaker to galvanize undercurrent ideas, an alternative to Wikipedia "random article", available in 300+ languages 🌍🌏🌎 https://www.knb.wiki

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The game content is data mined and analysed from Wikipedia articles and Wikidata items by multiple dimensions: languages clustering, hypernyms categorization, complexity scoring, etc.

Hypernym Hierarchy

All qualified Wikidata items / Wikipedia articles are categorized in 64 buckets.

The 64 top-level hypernyms of controlled vocabulary are presented for players to switch on/off to control the scopes of the next randomized quiz during game session by detonating 💣

Different views of the hypernym hierarchy:

• Tree path
• Tree path (monospaced node)
• Hilbert curve
• Hilbert curve (monospaced node)
  • Compact tiles

The design principle is: gameplay first, performance second, academic last; coarsely classifying as a decision tree, instead of networking as an ontologically precise semantic graph. The tree branches are grown and pruned to achieve balanced number of items/articles per node.

Knowledge Profile

For each game, a knowledge profile, or a brain imagery, will be generated to represent the player's mind for that quiz session. The more correct answer of the hypernym category, the more saturation of the cell color; the more wrong answer of the hypernym caterogy, the less saturation of the cell color. Example of knowledge profile :

2D language picker

The 🗺 2D language picker keeps the constant position of each language no matter choosing which language to display. It solves the problem of one dimentional language pickers (re)ordering certain languages alphabetically at various positions that hard to find, e.g. Deutsch - Aleman, Inaleman, Lialémani, Béésh bichʼahii, Ceruman, Duits, Eleman, Frangikos, German, Jerman, Krzyżacki, Mjymjecko, Немецкий, Német, Olmoni, Preisen, Rajchski, Saksa, 𐌸𐌹𐌿𐌳𐌹𐍃𐌺𐌰𐍂𐌰𐌶𐌳𐌰, Þýska, Tysk, Ubudage, Vācu, Weimarische, Yoeraman, Zėm, etc.

🗺 is also availble in game as a cheatsheet (hit top menubar ⌨ for hotkeys).

Design considerations

A lite version of the lexical distance map can be used as an convenient language picker for Google Translate:

List of Wikipedias - Languages ranking table

English is the first language in terms of information coverage and completeness. But which language is the second, and so on? Can we order languages in a quantitative manner?

The ranking is based on numbers of qualified Wikidata items per language. (wonder where's the swedish-cebuano?)

Ranking methodology

Rank	Language(Local)	Language	Wiki	Coverage%	Q60 🥇	Q50 🥈	Q40 🥉	Q30 🏅	Q20 🎖️	WSM	Articles	Solidness%	Speakers
1	English	English	en	100.00%	5265	7845	12532	20969	38482	9080.18	6424700	4.07%	1348000
2	Español	Spanish	es	98.17%	5254	7798	12383	20511	36588	8996.87	1738225	14.77%	586000
3	Français	French	fr	97.96%	5242	7787	12348	20427	36468	8987.09	2381920	10.76%	274000
4	Русский	Russian	ru	97.59%	5251	7805	12401	20385	35823	8970.06	1779001	14.35%	258000
5	Deutsch	German	de	95.89%	5150	7623	12080	19913	35258	8891.75	2643273	9.49%	135000
6	Українська	Ukrainian	uk	95.52%	5259	7827	12397	20009	33019	8874.50	1128056	22.15%	40000
7	Português	Portuguese	pt	94.92%	5174	7673	12136	19753	33533	8846.33	1078928	23.01%	274000
8	Italiano	Italian	it	94.84%	5155	7638	12053	19634	34073	8842.92	1730998	14.33%	85000
9	中文	Chinese	zh	94.72%	5250	7792	12276	19708	32463	8837.26	1245720	19.89%	1120000
10	日本語	Japanese	ja	94.47%	5230	7744	12165	19558	32699	8825.35	1305277	18.93%	128000
11	Nederlands	Dutch	nl	93.65%	5257	7742	12093	19339	31590	8787.27	2074259	11.81%	30000
12	العربية	Arabic	ar	93.16%	5262	7806	12150	19203	30778	8764.37	1149073	21.20%	274000
13	Polski	Polish	pl	93.05%	5141	7575	11937	19269	32035	8758.93	1500377	16.22%	45000
14	Català	Catalan	ca	92.74%	5214	7705	12050	19125	30748	8744.42	692111	35.05%	10000
15	فارسی	Persian	fa	91.96%	5263	7835	12141	18866	29321	8707.67	849228	28.32%	74000
16	한국어	Korean	ko	90.98%	5237	7721	11941	18585	28811	8661.02	569320	41.79%	82000
17	Svenska	Swedish	sv	90.92%	5132	7565	11820	18696	29433	8658.00	2820221	8.43%	13000
18	Čeština	Czech	cs	88.35%	5097	7488	11591	17926	27213	8534.71	493744	46.80%	14000
19	Suomi	Finnish	fi	87.43%	5071	7451	11493	17617	26438	8490.23	521190	43.87%	6000
20	Bahasa Indonesia	Indonesian	id	86.51%	5140	7513	11406	17079	25379	8445.50	608149	37.20%	199000
21	Türkçe	Turkish	tr	85.73%	5084	7428	11283	16989	24877	8407.55	461700	48.56%	88000
22	Norsk (Bokmål)	Norwegian (Bokmål)	no	84.24%	5022	7294	11088	16654	24082	8334.09	572167	38.51%	5000
23	עברית	Hebrew	he	83.53%	4965	7206	10871	16286	23866	8298.69	307833	70.97%	9000
24	Српски / Srpski	Serbian	sr	81.16%	5145	7432	10886	15200	20230	8180.39	652901	32.51%	12000
25	Esperanto	Esperanto	eo	80.46%	5074	7278	10668	14974	20333	8145.04	308208	68.28%	180
320	see 295 more...

Qualified Wikidata items

A qualified article (examples) should be a general, universal, common concept or knowledge, which satisfies these criteria:

• With more than 20 languages versions.
• Not an onomatology (anthroponymy, toponymy, hydronym, taxon etc.), which is not a particular person, place, biota, chemical, astronomical object, company, product, publication, song, team, sport match, event, year, decade, day, unicode, etc.
• Not a disambiguation page item.
• Can be hypernyms of items above (any level of parents of instance_of?/subclass_of*). Hypernyms are legitimate to have less than 20 languages versions, being small quantity of abstract philosophical terms backboning the semantic network.
• Qualified item/article should be verified through with cross check and proofreading for quality assurance.

Use ChatGPT to decide whether a term is an onomatology

1. Name-related: Assess whether the term is directly related to the study of names. Onomastics focuses on names, including personal names, place names, and other types of names. If the term specifically pertains to names or their characteristics (e.g., etymology, symbolism, usage patterns), it is more likely to be associated with onomastics.

2. Research and Study: Determine if the term is commonly used in onomastic research and academic discourse. Look for its presence in scholarly publications, such as journals, books, or conference proceedings, that discuss the study of names. If the term is frequently employed in onomastic literature, it indicates its relevance to the field.

3. Etymology and Linguistic Analysis: Consider if the term involves the analysis of name origins, linguistic aspects, or phonetic and semantic variations. Onomastics often examines the etymology, phonetics, and semantics of names to understand their historical development and cultural significance. If the term involves such analyses, it aligns with onomastic interests.

4. Cultural and Historical Context: Evaluate whether the term relates to the cultural and historical context of names. Onomastics explores how names are influenced by cultural, social, and historical factors. If the term is associated with the cultural, social, or historical aspects of names (e.g., naming practices, name changes over time), it is more likely to be relevant to onomastics.

5. Interdisciplinary Connections: Consider if the term intersects with other disciplines related to names, such as linguistics, anthropology, history, sociology, or cultural studies. Onomastics often collaborates with these fields to gain insights into the study of names. If the term has connections to multiple disciplines, it strengthens its association with onomastics.

6. Consensus within the Field: Take into account the consensus among experts and scholars in onomastics. If the term is widely accepted and recognized by the onomastic community as relevant to the field, it can be considered part of onomatology.

ChatGPT's knowledge is based on pre-existing data and may not always reflect the latest developments in onomastics.