Knowledge Graph Browser in a single executable

Hacker News

A Semantic Web knowledge graph consists of RDF resources identified by URLs which you ideally host somewhere so users can enter the URL of an entity, like https://dbpedia.org/resource/Leipzig , in a browser and get a human-readable representation of the resource. Normally you need a separate SPARQL endpoint (kind of a graph database) and then connect your RDF browser to that, but that is wasteful when you have a small knowledge base, and when the SPARQL endpoint is down due to overload, then you can't access your resources anymore. To solve this I made RickView (for "RDF/Rust quick viewer"), a single binary that holds a knowledge graph in memory and acts as a web server for RDF resources. Here are some RDF knowledge graphs you can browse that are served by RickView: - < https://www.snik.eu/ontology/bb > - < https://hitontology.eu/ontology/ > - < https://annosaxfdm.de/ontology/ > - < https://linkedspending.aksw.org/ >

Visit product View on Hacker NewsMay 31, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, single · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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