The value of the semantic web. RDF$? November 6, 2007Posted by shahan in information retrieval, internet artchitecture, online social networks, openid, semantic web, standards, Uncategorized.
The question that this entry seeks to answer is, “Using the semantic web, what resources are available that have meaningful marketable value?”.
While the value of the semantic web has been touted, marketable value is not as widely discussed. However; in order to encourage Google to develop an OpenRDF API, they need to see what it can do for them. In my previous post about Search Standards, I mentioned measurement of a person’s search preferences, such as type of content to search and metric ranges, is key to improving results. Combining Greg Wilson’s post about Measurement with the value-of-data issues mentioned in Bob Warfield’s User-Contributed Data Auditing we now want to understand how to retrieve semantically marked-up content which has the ability to generate revenue.
User-generated semantic metrics are easily achieved with the semantic web. Further, semantic metrics can be tied together using various means, one of which is mentioned in Dan Connolly’s blog entry Units of measure and property chaining. It should be noted that, due to the extensibility of semantic data, the value or metrics are independent of any specifics, thus allowing it to be used for trust metrics as well.
There is a general use case which describes what I mean:
- Content is made available. The quality is not called into question, yet.
- The content is semantically marked up so that it has properties that mean something.
- Other users markup the content even further but with personally-relevant properties that can be created by themselves or using an existing schema (e.g. available from their employer) which can be associated through their online identity OpenID and can be extended with their social network through Google’s OpenSocial API.
The data has now been extended from being searchable for relevant content using existing methods to becoming searchable using user-generated value metrics. These can then be leveraged, similar to Google Coop, and with further benefit if search standards were available.
If a group was selected based on their ability to identify and rank relevant content based on not by the content contained, but by the value associated with the properties of that content, the idea of relevant content no longer becomes whether the content itself is relevant to the person evaluating it, but whether the properties would be relevant to someone searching for those properties. This potentially has the ability to remove bias from relevance evaluation. No longer is content being evaluated for what it is but what it is perceived as, and the metrics from paid users as well as the users who view the content for their own or standard metrics is easily expandable and searchable by others, an architecture permitting growth beyond limited views.