Re: There's No Money in Linked Data

I beg to differ. I have been looking at the use of linked data for both commercial and non-profit purposes and it is only with commercial used that these intellectual property rights issues pose a real obstacle.

We are still thinking in business models of the old paradigm for online business. 

Think FaceBook. If enough users use linked data in predominantly streams of raw data but there are some collective cloud uses of this huge amount of data, the users of raw data become the monetizable commodity, not the linked data directly. But since the raw data defines the data user collective, the linked data acquires added value through its users through the cloud services employed.

There you have it, Pascal you have forced me to give away part of the new categories of business models I mentioned in an earlier email,

Indirect acquired added value through users.


 
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________________________________
 From: Pascal Hitzler <pascal.hitzler@wright.edu>
To: semantic-web@w3.org 
Sent: Friday, May 17, 2013 10:13 PM
Subject: There's No Money in Linked Data
 

We just finished a piece indicating serious legal issues regarding the commercialization of Linked Data - this may be of general interest, hence the post. We hope to stimulate discussions on this issue (hence the provokative title).

Available from
http://knoesis.wright.edu/faculty/pascal/pub/nomoneylod.pdf

Abstract.
Linked Data (LD) has been an active research area for more than 6 years and many aspects about publishing, retrieving, linking, and cleaning Linked Data have been investigated. There seems to be a broad and general agreement that in principle LD datasets can be very useful for solving a wide variety of problems ranging from practical industrial analytics to highly specific research problems. Having these notions in mind, we started exploring the use of notable LD datasets such as DBpedia, Freebase, Geonames and others for a commercial application. However, it turns out that using these datasets in realistic settings is not always easy. Surprisingly, in many cases the underlying issues are not technical but legal barriers erected by the LD data publishers. In this paper we argue that these barriers are often not justified, detrimental to both data publishers and users, and are often built without much consideration of their consequences.

Authors:
Prateek Jain, Pascal Hitzler, Krzysztof Janowicz, Chitra Venkatramani

-- Prof. Dr. Pascal Hitzler
Kno.e.sis Center, Wright State University, Dayton, OH
pascal@pascal-hitzler.de   http://www.knoesis.org/pascal/
Semantic Web Textbook: http://www.semantic-web-book.org
Semantic Web Journal: http://www.semantic-web-journal.net