Scale-free Networks and the Value of Linked Data

Kingsley Idehen of OpenLink Software and others on the Business of Linked Data (BOLD) list have been debating a value proposition for linked data via Twitter (search for #linkeddata) and email. The discussion has included useful iterations on various "elevator pitches" and citations of recent successes, especially the application of GoodRelations e-commerce vocabularies at Best Buy. After some deep thought I decided to take the question of value in a different direction and to consider it from the perspective of the science of networks, especially with reference to the works of Albert-László Barabási, director of the Center for Complex Network Research and author of Linked: The New Science of Networks. I'd like to test the idea here that data sharing between organisations based on linked open data principles is the approach most consistent with the core principles of a networked economy. I believe that the linked data model best exploits "networking thinking" and maximizes the organisation's ability to respond to changes in relationships within the "global graph" of business. Using Barabási as a framework, linked data is the approach that most embodies a networked view of the economy from the macro- to the micro-economic level, and therefore best empowers the enterprise to understand and leverage the consequences of interconnectedness.

As has been noted numerous times elsewhere, the so-called Web of Data is perhaps the web in its purest form. Following Tim Berners-Lee principles or "rules" as stated in his Linked Data Design Issues memo from 2006, we have a very elegant framework for people and especially machines to describe the relationship between entities in a network. If we are smart about how we define those links and the entities we create to aggregate those links --- the linked datasets we create --- we can build dynamic, efficiently adaptive networks embodying the two laws that govern real networks: growth and preferential attachment. Barabási illustrates these two laws with an example "algorithm" for scale-free networks in Chapter 7 of Linked. The critical lessons are (a) networks must have a means to grow --- there must not only be links, but the ability to add links, and (b) networks must provide some mechanism for entities to register their preference for other nodes by creating links to the more heavily-linked nodes. Preferential attachment ensures that the converse is also true: entities will "vote with their feet" and register their displeasure with nodes by eliminating links.

In real networks, the rich get richer. In the Web, the value is inherent in the links. Google's PageRank merely reinforced the "physical" reality that the most valuable properties in the Web of Documents are those resources that are most heavily linked-to. Those properties provide added value if they in turn provide useful links to other resources. The properties that are sensitive to demand and can adapt to the preferences of their consumers, especially to aggregate links to more resources that compound their value and distinguish them from other properties, are especially valuable and are considered hubs.

Openness is important. At this point it is tempting to jump to the conclusion that Tim Berners-Lee's four principles are all we need to create a thriving Web of Data, but this would be premature; Sir Tim's rules are necessary but not sufficient conditions. Within any "space" where Webs of Data are to be created, whether global or constrained within an organisation, the network must embody the open world assumption as it pertains to the web: when datasets or other information models are published, their providers must expect them to be reused and extended. In particular this means that entities within the network, whether powered by humans or machines, must be free to arbitrarily link to (make assertions about) other entities within the network. The "friction" of permission in this linking process must approximate zero.

Don't reinvent and don't covet! The extent of graphs that are built within organisations should not stop at their boundaries; as the BBC has shown so beautifully with their use of linked data on the revamped BBC web site, the inherent value of their property was increased radically by not only linking to datasets provided elsewhere, openly on the "global graph," but also by enabling reuse of their properties. The BBC's top-level principles for the revamped site are all about openness and long-term value:

The site has been developed against the principles of linked open data and RESTful architecture where the creation of persistent URLs is a primary objective. The initial sources of data are somewhat limited but this will be extended over time. Here's our mini-manifesto: Persistence...Linked open data...RESTful...One web

The BBC has created a valuable "ecosystem"; their use of other resources, especially MusicBrainz and DBPedia, has not only made the BBC site richer but in turn has increased the value of those properties. And those properties will continue to increase in value; by the principle of preferential attachment, every relationship "into" a dataset by valuable entities such as the BBC in turn increases the likelihood that other relationships will be established.

Links are not enough. It should be obvious that simply exposing datasets and providing value-added links to others isn't enough; as Eric Hellman notes, dataset publishers must see themselves service providers who add value beyond simply exposing data. Some will add value to the global graph by gathering, maintaining, publishing useful datasets and fostering a community of users and developers; others will add value by combining datasets from other services in novel ways, possibly decorated by their own. Eric has argued that the only winners in the linked open data space have indeed been those who have provided such merged datasets as a service.

Provide value-adding services and foster community. I would argue that dataset providers asking how they might realise the full value potential of publishing their datasets on the Web should examine whether, based on the principles I've outlined above, they have done everything they can to make their datasets part of the Web (rather than merely "on" the web) and have truly added value to the global graph. Do they view themselves as a service? Have they made their datasets as useful and easy-to-use as possible? Have they provided the best possible community support, including wikis and other mechanisms? Have they fully documented their vocabularies? Have they clearly defined any claimed rights, and in particular have they considered adopting open data principles?

Linking Rights to Aggregations of Data (Part 2)

In my background research for today's entry I discovered that the smart people at Talis, especially Ian Davis, have been working the problem I outlined in Linking Rights to Aggregations of Data (Part 1). Specifically, back in July 2009 Ian proposed WAIVER: A vocabulary for waivers of rights. In Ian's words,

(The WAIVER) vocabulary defines properties for use when describing waivers of rights over data and content. A waiver is the voluntary relinquishment or surrender of some known right or privilege. This vocabulary is designed for use with the Open Data Commons Public Domain Dedication and License and with the Creative Commons CC-0 waiver

In his July 2009 post Linked Data and the Public Domain Ian argues for providers to unambiguously declare their datasets public domain and explains how to use the WAIVER vocabulary to do this, in the context of a voID description of a dataset. (See also this email discussion thread involving several of the thought leaders in this area on this issue) Ian provides the following example, which I repeat here to illustrate (a) use of voID to describe a dataset named "myDataset," (b) use of the wv:waiver property to link the dataset to the Open Data Commons PDDL waiver, (c) use of the wv:declaration property to include a human-readable declaration of the waiver, and (d) use of the wv:norms property to link the dataset to the community norms he suggests, ODC Attribution and Share-alike.

<?xml version="1.0"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
  xmlns:dc="http://purl.org/dc/terms/"
  xmlns:wv="http://vocab.org/waiver/terms/"
  xmlns:void="http://rdfs.org/ns/void#">
  <void:Dataset rdf:about="http://myOrganisation.org/myDataset">
    <dc:title>myDataset</dc:title>
    <wv:waiver rdf:resource="http://www.opendatacommons.org/odc-public-domain-dedication-and-licence/"/>
    <wv:norms rdf:resource="http://www.opendatacommons.org/norms/odc-by-sa/" />
    <wv:declaration>
      To the extent possible under law, myOrganisation
      has waived all copyright and related or neighboring rights to 
      myDataset
    </wv:declaration>
  </void:Dataset>
</rdf:RDF>

WAIVER and OAI-ORE: As I proposed in Part 1, we should be able to combine the voID and OAI-ORE approaches. The only conceptual difference is by OAI-ORE guidelines the RDF file shown above would be treated as the resource map for the aggregation URI (in this example, "http://myOrganisation.org/myDataset") and would have a URI unto itself (perhaps "http://myOrganisation.org/myDataset.rdf").

What about other rights? It is critically important for the reader to understand that Ian's example (repeated above) only shows how to declare a waiver of rights, which by its nature is intended to promote the reuse of data based on open principles. Today, this is mostly what the linked data world has focused on, but as the NYTimes open data experiment is showing us, providers will want to assert rights where they can. In a future post I'll applied what we've learned so far, to consider approaches for declaring dataset rights in legal regimes where this is actually possible.

Linking Rights to Aggregations of Data (Part 1)

In my previous post Protecting your Linked Data I considered the dual questions of what legal regimes are available to linked data providers for the protection of their published datasets, and what technical frameworks and best practices exist especially within the realm of RDF and linked data to make such rights assertions. In this (shorter!) post I begin to consider an attribution scheme that comes to mind on the heels of discussions on the The New York Times Linked Open Data Community list, that of using named graphs (see also here) and specifically the OAI-ORE data model to associate specific rights to aggregations of resources.

What's the problem? Given a set -- an aggregation -- of data assertions, how might we properly assert rights over those assertions, especially in a way that a responsible client won't lose track of the ownership context? Lets assume a file of RDF triples is read into store. Consist with the NYTimes LOD discussion, we'll call the file people.rdf. Since "all RDF stores support named graphs these days" (Richard Cyganiak), a named graph URI shall be assumed to have been created and names the aggregation of assertions imported from "people.rdf" (i.e. the assertions in the file "people.rdf" from the provider become members of the named graph "people.rdf" in the client's RDF store.

Recall that a named graph is "a set of triples named by an URI." [ref] The OAI-ORE data model extends this with a set of guidelines for making assertions about aggregations that "describe" the named graph. ORE's core idea is to create one URI to represent the aggregation itself, and another to represent the resource map that we created to describe that aggregation. It should be in this OAI-ORE resource map that rights expressions applying to the aggregation should appear.

In my next post I'll take a stab a mocking up -- and hopefully not mucking up -- what an implementation of this might look like...

Long Tails and "Scaling Down" Linked Data Services

Chris Anderson's newest book FREE: The Future of a Radical Price received some attention this summer, but I've actually been meditating on principles he laid out three years ago in his blog post, Scaling up is good. Scaling down is even better. In that post he marveled at Google et.al.'s ability to scale down, to run themselves efficiently enough to serve users who generate no revenue at all. Anderson's principles are guidance on approaches to conducting business such that even if only a tiny percentage of ones visitors "convert" into paying customers, by ensuring this small percentage is of a very large number one can still achieve big-time profitability.

My goal with this post is to consider how these ideas might be applied to the domain of Linked Data, and specifically how they pertain to the provision of unique data that adds real value to the greater "Web of Data."

In his blog Anderson gives us four keys to scaling down: Self-service, "Freemium" services, No-frills products and Crowdsourcing...

1. Self-service: give customers all the tools they need to manage their own accounts. It's cheap, convenient, and they'll thank you for it. Control is power, and the person who wants the work done is the one most motivated in seeing that it's done properly.

"Self-service" applies to linked data services in oh-so-many ways! Self- service in this case is not as much about support (see "Crowdsourcing," below) as it is about eliminating any and all intervention customers might need to customize or specialize how services perform for them. In principle, the goal should be to provide users with a flexible API and let them figure it out, with the support of their peers. Ensure that everything is doable from their side, and step out of the way.

The (negative) corollary is this: if you "baby sit" your customers by providing specialized services that require maintenance, then you own it and must eat the cost. By making the specializations a user-side function, then they own it. But they won't be alone; they'll have the support of their community!

2. "Freemium" services: As VC Fred Wilson puts it, "give your service away for free, possibly ad supported but maybe not, acquire a lot of customers very efficiently through word of mouth, referral networks, organic search marketing, etc, then offer premium priced value added services or an enhanced version of your service to your customer base." Free scales down very nicely indeed.

There are any number of ways providers might apply this concept to the linked data world:

Free Access	Premium Access
Restricted vocabulary of assertions	Full access, all assertions
Limited query rate	Unlimited query rate
Limited query extent	Unlimited query extent
Limited data	Unlimited data size
Read-only	Term upload capability
Narrow reuse rights	Broad reuse rights
Community support	Private/ dedicated support
...	...

3. No-frills products: Some may come for the low cost, others for the simplicity. But increasingly consumers are sophisticated enough to know that they don't need, or want to pay for premium brands and unnecessary features. It's classic market segmentation, with most of the growth coming at the bottom.

In the linked data world, achieving "no frills" would seem easy because by definition it is only about the data! For linked data a "frill" is added data complexity that serves no purpose or detracts from the utility of the service. Avoid any temptation to "add value" on behalf of customers, such as merging your core graph with others in an attempt to "make it easy" for them. Providers should also avoid "pruning" graphs, except in the case of automated filtering in order to differentiate between Freemium and Premium services.

4. Crowdsourcing: From Amazon reviews to eBay listings, letting the customers do the work of building the service is the best way to expand a company far beyond what employees could do on their own.

By now it is not only obvious, but imperative that providers should develop communities around their services. Usually communities are about evangelism, and this is certainly true for linked data provides, but increasingly service provides realize well-groomed communities can radically reduce their service costs.

Linked data providers should commit themselves to a minimum of direct support and invest in fostering an active community around their service. Every provider should have a means for members of their community to support each other. Every provider should leverage this community to demonstrate to potential adopters the richness of the support and the inherent value of their dataset.

Finally: In a thought-provoking post Linked Data and the Enterprise: A Two-way Street Paul Miller reminds the skeptical enterprise community that they, not merely their user community, will ultimately benefit from the widespread use of their data, and when developing their linked data strategy they should consider how they can "enhance" the value of the Web of Data, for paying and non-paying users alike:

...[A] viable business model for the data-curating Enterprise might be to expose timely and accurate enrichments to the Linked Data ecosystem; enrichments that customers might pay a premium to access more quickly or in more convenient forms than are available for free...

I've purposely avoiding considering the legal and social issues associated with publishing certain kinds of enterprise data as linked data (see also this), which I'll address in a future post...