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Data-based startups will be implement internally before it hits popularity. This is supported by semantic-esque elements have shown up in many APIs (such as Ama
by ieatpaste 18y ago
Data-based startups will be implement internally before it hits popularity. This is supported by semantic-esque elements have shown up in many APIs (such as Amazon).
In my startup, we're using ARC (http://arc.semsol.org/ http://arc.semsol.org/) and microformats internally; however, we're not doing it just for the sake of new technology - data is managed better, databases can be designed well, and there is a performance gain in some cases.
- trevelyan 18y agoUpmodded. My firm produces semantic analysis software for Chinese text: http://popupchinese.com/words/downloads http://popupchinese.com/words/downloads The major issue hampering adoption is that unlike services based on pattern-matching, services based on semantic/NLP analysis don't have a simple mechanism to hide errors. There is probably a threshold at which error rates are tolerable for consumer-facing services, but we are not there yet. So the uses are largely on the backend in areas with narrowly-defined problems where semantic tech can help with automation and content processing and where people have an incentive to improve performance over time by customizing the software itself. This is happening, but you don't see glowing write-ups in Wired magazine since the uses are extremely field specific and usually not terribly sexy.