{"slug": "wikidata", "name": "Wikidata", "category": "notes", "category_name": "Notes and knowledge", "description": "", "recommenders": 5, "commenters": 8, "warners": 1, "score": 4.109, "rising": -1.64, "first": "2025-12-05", "latest": "2026-07-15", "why": "I like WikiData for tasks like these that require consolidating multiple different identifiers for the same work", "why_is_quote": true, "has_verdict": false, "sentiment": {"recommend": 6, "neutral": 2, "compare": 0, "warn": 1}, "spark": [0, 0, 1, 1, 1, 0, 1, 2, 0, 0, 0, 0], "listed": true, "loved": [], "caveat": "", "short_quote": "I like WikiData for tasks like these that require consolidating multiple different identifiers for the same work", "kind": "", "icon": true, "domain": "wikidata.org", "github_repo": null, "official_url": "https://www.wikidata.org", "pricing": "", "aliases": ["Wikidata"], "verdict": null, "praise": [{"label": "", "summary": "", "quotes": [{"id": 777163168, "item_id": 48572698, "url": "https://news.ycombinator.com/item?id=48572698", "by": "scurnus", "date": "2026-06-17", "version": null, "stance": "recommend", "quote": "I used Wikidata as source, ArangoDB as database, Redis for caching, Vue+ Cytoscape.js for frontend.", "excerpt": "I added the Feed feature to the site to make it more usable from mobile. You can scroll and discover important connections. I used Wikidata as source, ArangoDB as database, Redis for caching, Vue+ Cytoscape.js for frontend. If you are from desktop try Explore mode.", "context": "built HumansMap", "use_case": "data source", "switched_from": "", "story": "HumansMap, a feed of family dynasties and links between orgs. No ads"}, {"id": 768661697, "item_id": 48041356, "url": "https://news.ycombinator.com/item?id=48041356", "by": "jeromechoo", "date": "2026-05-06", "version": null, "stance": "recommend", "quote": "you can use Sparql to perform structured queries on Wikidata (a structured database version of Wikipedia)", "excerpt": "This is a fantastic resource. The author only briefly covers library databases, but there's so much more in structured querying that could be worth covering. For example, you can use Sparql to perform structured queries on Wikidata (a structured database version of Wikipedia) to get well beyond unstructured documents. Here's every person in Wikibase that was born in NYC: https://query.wikidata.org/#%23Humans%20born%2", "context": "", "use_case": "", "switched_from": "", "story": "Google has a secret reference desk"}, {"id": 761512000, "item_id": 47594500, "url": "https://news.ycombinator.com/item?id=47594500", "by": "FelipeCortez", "date": "2026-03-31", "version": null, "stance": "recommend", "quote": "The Wikidata documentation is good", "excerpt": "The Wikidata documentation is good: https://www.wikidata.org/wiki/Wikidata:Introduction And you can find lots of SPARQL examples here: https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/queries/examples", "context": "", "use_case": "Structured knowledge base querying", "switched_from": "", "story": "What major works of literature were written after age of 85? 75? 65?"}, {"id": 761402592, "item_id": 47587662, "url": "https://news.ycombinator.com/item?id=47587662", "by": "FelipeCortez", "date": "2026-03-31", "version": null, "stance": "recommend", "quote": "This is actually a good fit for a Wikidata SPARQL query you can run here https://query.wikidata.org/", "excerpt": "This is actually a good fit for a Wikidata SPARQL query you can run here https://query.wikidata.org/: SELECT ?work ?workLabel ?author ?authorLabel ?publicationDate ?ageAtPublication WHERE { ?author wdt:P569 ?birth . ?author wdt:P570 ?death . ?author wdt:P800 ?work . ?work wdt:P50 ?author ; wdt:P31 wd:Q47461344 ; wdt:P577 ?publicationDate . FILTER(?publicationDate <= ?death) BIND(YEAR(?publicationDate) - YEAR(?birth) ", "context": "", "use_case": "structured data queries", "switched_from": "", "story": "What major works of literature were written after age of 85? 75? 65?"}, {"id": 754476352, "item_id": 47154772, "url": "https://news.ycombinator.com/item?id=47154772", "by": "culi", "date": "2026-02-25", "version": null, "stance": "recommend", "quote": "I like WikiData for tasks like these that require consolidating multiple different identifiers for the same work", "excerpt": "I like WikiData for tasks like these that require consolidating multiple different identifiers for the same work. E.g. https://www.wikidata.org/wiki/Q106545884 Gives us the ISBN, Goodreads work id, LibraryThing id, the OpenLibrary id, and the Google Knowledge Graph id all in one query.", "context": "", "use_case": "Consolidating identifiers for works", "switched_from": "", "story": "The Perils of ISBN"}]}], "caveats": [{"label": "", "summary": "", "quotes": [{"id": 759664001, "item_id": 47479000, "url": "https://news.ycombinator.com/item?id=47479000", "by": "adsharma", "date": "2026-03-22", "version": null, "stance": "warn", "quote": "which uses RDF dumps (and probably 8x less compressed than it should be)", "excerpt": "So this thing is based on Kiwix, which is based on the ZIM file format. In the meanwhile, wikipedia ships wikidata, which uses RDF dumps (and probably 8x less compressed than it should be). https://www.wikidata.org/wiki/Wikidata:Database_download There is room for a third option leveraging commercial columnar database research. https://adsharma.github.io/duckdb-wikidata-compression/", "context": "", "use_case": "", "switched_from": "", "story": "Project Nomad \u2013 Knowledge That Never Goes Offline"}]}], "who": [{"by": "scurnus", "context": "built HumansMap", "url": "https://news.ycombinator.com/item?id=48572698", "date": "2026-06-17"}], "tags": [], "use_cases": ["data source", "structured knowledge base querying", "structured data queries", "consolidating identifiers for works", "aggregating external identifiers", "editing structured data"], "compared": [], "makers": [], "threads": [{"url": "https://news.ycombinator.com/item?id=47556076", "title": "What major works of literature were written after age of 85? 75? 65?", "mentions": 2}, {"url": "https://news.ycombinator.com/item?id=48908271", "title": "Show HN: Opening lines of famous literary works", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=48572637", "title": "HumansMap, a feed of family dynasties and links between orgs. No ads", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=48493218", "title": "WikiLambda the Ultimate", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=48032240", "title": "Google has a secret reference desk", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=47476821", "title": "Project Nomad \u2013 Knowledge That Never Goes Offline", "mentions": 1}], "sentiment_by_month": {"2025-11": {}, "2025-12": {"neutral": 1}, "2026-01": {"recommend": 1}, "2026-02": {"recommend": 1}, "2026-03": {"warn": 1, "recommend": 1}, "2026-04": {}, "2026-05": {"recommend": 1}, "2026-06": {"recommend": 2}, "2026-07": {"neutral": 1}, "2026-08": {}, "2026-09": {}, "2026-10": {}}, "versions": [], "version_unnamed": 0, "family_ref": null, "runs_on": null, "lines": [], "quotes": [{"id": 782767248, "item_id": 48922953, "url": "https://news.ycombinator.com/item?id=48922953", "by": "YoshiRulz", "date": "2026-07-15", "version": null, "stance": "neutral", "quote": "This same idea as a Wikidata query", "excerpt": "This same idea as a Wikidata query: https://w.wiki/SV9k (something something everything is a nail)", "context": "", "use_case": "", "switched_from": "", "story": "Show HN: Opening lines of famous literary works"}, {"id": 777163168, "item_id": 48572698, "url": "https://news.ycombinator.com/item?id=48572698", "by": "scurnus", "date": "2026-06-17", "version": null, "stance": "recommend", "quote": "I used Wikidata as source, ArangoDB as database, Redis for caching, Vue+ Cytoscape.js for frontend.", "excerpt": "I added the Feed feature to the site to make it more usable from mobile. You can scroll and discover important connections. I used Wikidata as source, ArangoDB as database, Redis for caching, Vue+ Cytoscape.js for frontend. If you are from desktop try Explore mode.", "context": "built HumansMap", "use_case": "data source", "switched_from": "", "story": "HumansMap, a feed of family dynasties and links between orgs. No ads"}, {"id": 776027840, "item_id": 48501740, "url": "https://news.ycombinator.com/item?id=48501740", "by": "meh2frdf", "date": "2026-06-12", "version": null, "stance": "recommend", "quote": "", "excerpt": "This might interest you then https://ont.fyi (shameless plug)!", "context": "", "use_case": "", "switched_from": "", "story": "WikiLambda the Ultimate"}, {"id": 768661697, "item_id": 48041356, "url": "https://news.ycombinator.com/item?id=48041356", "by": "jeromechoo", "date": "2026-05-06", "version": null, "stance": "recommend", "quote": "you can use Sparql to perform structured queries on Wikidata (a structured database version of Wikipedia)", "excerpt": "This is a fantastic resource. The author only briefly covers library databases, but there's so much more in structured querying that could be worth covering. For example, you can use Sparql to perform structured queries on Wikidata (a structured database version of Wikipedia) to get well beyond unstructured documents. Here's every person in Wikibase that was born in NYC: https://query.wikidata.org/#%23Humans%20born%2", "context": "", "use_case": "", "switched_from": "", "story": "Google has a secret reference desk"}, {"id": 761512000, "item_id": 47594500, "url": "https://news.ycombinator.com/item?id=47594500", "by": "FelipeCortez", "date": "2026-03-31", "version": null, "stance": "recommend", "quote": "The Wikidata documentation is good", "excerpt": "The Wikidata documentation is good: https://www.wikidata.org/wiki/Wikidata:Introduction And you can find lots of SPARQL examples here: https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/queries/examples", "context": "", "use_case": "Structured knowledge base querying", "switched_from": "", "story": "What major works of literature were written after age of 85? 75? 65?"}, {"id": 761402592, "item_id": 47587662, "url": "https://news.ycombinator.com/item?id=47587662", "by": "FelipeCortez", "date": "2026-03-31", "version": null, "stance": "recommend", "quote": "This is actually a good fit for a Wikidata SPARQL query you can run here https://query.wikidata.org/", "excerpt": "This is actually a good fit for a Wikidata SPARQL query you can run here https://query.wikidata.org/: SELECT ?work ?workLabel ?author ?authorLabel ?publicationDate ?ageAtPublication WHERE { ?author wdt:P569 ?birth . ?author wdt:P570 ?death . ?author wdt:P800 ?work . ?work wdt:P50 ?author ; wdt:P31 wd:Q47461344 ; wdt:P577 ?publicationDate . FILTER(?publicationDate <= ?death) BIND(YEAR(?publicationDate) - YEAR(?birth) ", "context": "", "use_case": "structured data queries", "switched_from": "", "story": "What major works of literature were written after age of 85? 75? 65?"}, {"id": 759664001, "item_id": 47479000, "url": "https://news.ycombinator.com/item?id=47479000", "by": "adsharma", "date": "2026-03-22", "version": null, "stance": "warn", "quote": "which uses RDF dumps (and probably 8x less compressed than it should be)", "excerpt": "So this thing is based on Kiwix, which is based on the ZIM file format. In the meanwhile, wikipedia ships wikidata, which uses RDF dumps (and probably 8x less compressed than it should be). https://www.wikidata.org/wiki/Wikidata:Database_download There is room for a third option leveraging commercial columnar database research. https://adsharma.github.io/duckdb-wikidata-compression/", "context": "", "use_case": "", "switched_from": "", "story": "Project Nomad \u2013 Knowledge That Never Goes Offline"}, {"id": 754476352, "item_id": 47154772, "url": "https://news.ycombinator.com/item?id=47154772", "by": "culi", "date": "2026-02-25", "version": null, "stance": "recommend", "quote": "I like WikiData for tasks like these that require consolidating multiple different identifiers for the same work", "excerpt": "I like WikiData for tasks like these that require consolidating multiple different identifiers for the same work. E.g. https://www.wikidata.org/wiki/Q106545884 Gives us the ISBN, Goodreads work id, LibraryThing id, the OpenLibrary id, and the Google Knowledge Graph id all in one query.", "context": "", "use_case": "Consolidating identifiers for works", "switched_from": "", "story": "The Perils of ISBN"}, {"id": 745158288, "item_id": 46572393, "url": "https://news.ycombinator.com/item?id=46572393", "by": "culi", "date": "2026-01-10", "version": null, "stance": "recommend", "quote": "I find WikiData to be perfect for aggregating identifiers.", "excerpt": "I find WikiData to be perfect for aggregating identifiers. I mostly work with species names and it's perfect for getting the iNaturalist, GBIF, Open Tree of Life, Catalogue of Life, etc identities all in one query I haven't tried it for books. I imagine it's not sufficiently complete to serve as a backbone but a quick look at an example book gives me the ids for OpenLibrary, Librarything, Goodreads, Bing, and even ni", "context": "", "use_case": "Aggregating external identifiers", "switched_from": "", "story": "Show HN: Librario, a book metadata API that aggregates G Books, ISBNDB, and more"}, {"id": 738675089, "item_id": 46167193, "url": "https://news.ycombinator.com/item?id=46167193", "by": "starkparker", "date": "2025-12-05", "version": null, "stance": "neutral", "quote": "Took about 2 minutes with no prior experience to remove the dead IA link", "excerpt": "> The WikiFlix tool is hosted on Toolforge in the US. Toolforge is operated by the Wikimedia Foundation. All WikiFlix content is generated from Wikidata and maintained by that community. https://www.wikidata.org/wiki/Help:WikiFlix Network's WikiData entry is https://www.wikidata.org/wiki/Q572165, which includes the Archive link and lacks a copyright status. This is also linked to from the barcode icon at the top of t", "context": "", "use_case": "Editing structured data", "switched_from": "", "story": "WikiFlix: Full Movies Hosted on Wikimedia Commons"}]}