The idea behind semantic web was to embed intelligence in web content through semantic markup, so that downstream agents could make sense of the content.
However the semantic web didn't have a business model to drive adoption. It assumed web content would be authored with semantic indicators that could later be harvested usefully (including commercially), but failed to consider what incentive or economic paradigm would compel content authors to embed semantic indicators.
Meanwhile, the problem/opportunity was solved in other ways.
Consider this example:
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The promise: machine intelligence
“At the doctor's office, Lucy instructed her Semantic Web agent through her handheld Web browser. The agent promptly retrieved information about Mom's prescribed treatment from the doctor's agent, looked up several lists of providers, and checked for the ones in-plan for Mom's insurance a 20 mile radius of her home and with a rating of excellent or very good on trusted rating services. It then began trying to find a match between available appointment times (supplied by the agents of individual providers through their Web sites) and Pete's and Lucy's busy schedules.”
(The emphasized keywords indicate terms whose semantics, or meaning, were defined for the agent through the Semantic Web.)
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The problem was solved by commercial services e.g. google maps in which the semantic layer is provided, retained and exploited by the service aggregator, google, which monetises the service through advertising.
Ironically, "machine intelligence" has not come to mean semantically enriched source documents, but rather, elaborate computationally intensive processes applied to harvest commercial opportunities from unenriched web content.
I feel like the semantic web was born out of an older way of looking at the web. One that was more about content and distribution of it rather than how it was cataloged. The old “here’s a cool way to share documents” It didn’t need a business model. But the web became something else. Googles dominance in the search business made content mold to their preferred structure, rather than a competitive industry that took content as it was presented.
However the semantic web didn't have a business model to drive adoption. It assumed web content would be authored with semantic indicators that could later be harvested usefully (including commercially), but failed to consider what incentive or economic paradigm would compel content authors to embed semantic indicators.
Meanwhile, the problem/opportunity was solved in other ways.
Consider this example:
- - -
The promise: machine intelligence
“At the doctor's office, Lucy instructed her Semantic Web agent through her handheld Web browser. The agent promptly retrieved information about Mom's prescribed treatment from the doctor's agent, looked up several lists of providers, and checked for the ones in-plan for Mom's insurance a 20 mile radius of her home and with a rating of excellent or very good on trusted rating services. It then began trying to find a match between available appointment times (supplied by the agents of individual providers through their Web sites) and Pete's and Lucy's busy schedules.”
(The emphasized keywords indicate terms whose semantics, or meaning, were defined for the agent through the Semantic Web.)
- - -
The problem was solved by commercial services e.g. google maps in which the semantic layer is provided, retained and exploited by the service aggregator, google, which monetises the service through advertising.
Ironically, "machine intelligence" has not come to mean semantically enriched source documents, but rather, elaborate computationally intensive processes applied to harvest commercial opportunities from unenriched web content.