Kingsley Idehen's Blog Data Space

URIBurner: Painless Generation & Exploitation of Linked Data (Update 1 - Demo Links Added)

Wed, 10 Mar 2010 17:52:03 GMT

What is URIBurner?

A service from OpenLink Software, available at: http://uriburner.com, that enables anyone to generate structured descriptions -on the fly- for resources that are already published to HTTP based networks. These descriptions exist as hypermedia resource representations where links are used to identify:

the entity (data object or datum) being described,
each of its attributes, and
each of its attributes values (optionally).

The hypermedia resource representation outlined above is what is commonly known as an Entity-Attribute-Value (EAV) Graph. The use of generic HTTP scheme based Identifiers is what distinguishes this type of hypermedia resource from others.

Why is it Important?

The virtues (dual pronged serendipitous discovery) of publishing HTTP based Linked Data across public (World Wide Web) or private (Intranets and/or Extranets) is rapidly becoming clearer to everyone. That said, the nuance laced nature of Linked Data publishing presents significant challenges to most. Thus, for Linked Data to really blossom the process of publishing needs to be simplified i.e., "just click and go" (for human interaction) or REST-ful orchestration of HTTP CRUD (Create, Read, Update, Delete) operations between Client Applications and Linked Data Servers.

How Do I Use It?

In similar vane to the role played by FeedBurner with regards to Atom and RSS feed generation, during the early stages of the Blogosphere, it enables anyone to publish Linked Data bearing hypermedia resources on an HTTP network. Thus, its usage covers two profiles: Content Publisher and Content Consumer.

Content Publisher

The steps that follow cover all you need to do:

place a tag within your HTTP based hypermedia resource (e.g. within section for HTML )
use a URL via the @href attribute value to identify the location of the structured description of your resource, in this case it takes the form: http://linkeddata.uriburner.com/about/id/{scheme-or-protocol}/{your-hostname-or-authority}/{your-local-resource}
for human visibility you may consider adding associating a button (as you do with Atom and RSS) with the URL above.

That's it! The discoverability (SDQ) of your content has just multiplied significantly, its structured description is now part of the Linked Data Cloud with a reference back to your site (which is now a bona fide HTTP based Linked Data Space).

Examples

HTML+RDFa based representation of a structured resource description:

<link rel="describedby" title="Resource Description (HTML)"type="text/html" href="http://linkeddata.uriburner.com/about/id/http/example.org/xyz.html"/>

JSON based representation of a structured resource description:

<link rel="describedby" title="Resource Description (JSON)" type="application/json" href="http://linkeddata.uriburner.com/about/id/http/example.org/xyz.html"/>

N3 based representation of a structured resource description:

<link rel="describedby" title="Resource Description (N3)" type="text/n3" href="http://linkeddata.uriburner.com/about/id/http/example.org/xyz.html"/>

RDF/XML based representations of a structured resource description:

<link rel="describedby" title="Resource Description (RDF/XML)" type="application/rdf+xml" href="http://linkeddata.uriburner.com/about/id/http/example.org/xyz.html"/>

Content Consumer

As an end-user, obtaining a structured description of any resource published to an HTTP network boils down to the following steps:

go to: http://uriburner.com
drag the Page Metadata Bookmarklet link to your Browser's toolbar
whenever you encounter a resource of interest (e.g. an HTML page) simply click on the Bookmarklet
you will be presented with an HTML representation of a structured resource description (i.e., identifier of the entity being described, its attributes, and its attribute values will be clearly presented).

Examples

Description of a Book culled from an Amazon web page
Description of a product offering culled from a BestBuy web page
Description of a product (a camera) culled from a CNET web page
Description of the same CNET product as an Offer on eBay (exposed by the description above via seeAlso property value).

If you are a developer, you can simply perform an HTTP operation request (from your development environment of choice) using any of the URL patterns presented below:

HTML:

curl -I -H "Accept: text/html" http://linkeddata.uriburner.com/about/id/{scheme}/{authority}/{local-path}

JSON:

curl -I -H "Accept: application/json" http://linkeddata.uriburner.com/about/id/{scheme}/{authority}/{local-path}
curl http://linkeddata.uriburner.com/about/data/json/{scheme}/{authority}/{local-path}

Notation 3 (N3):

curl -I -H "Accept: text/n3" http://linkeddata.uriburner.com/about/id/{scheme}/{authority}/{local-path}
curl http://linkeddata.uriburner.com/about/data/n3/{scheme}/{authority}/{local-path}

curl -I -H "Accept: text/turtle" http://linkeddata.uriburner.com/about/id/{scheme}/{authority}/{local-path}
curl http://linkeddata.uriburner.com/about/data/ttl/{scheme}/{authority}/{local-path}

RDF/XML:

curl -I -H "Accept: application/rdf+xml" http://linkeddata.uriburner.com/about/id/{scheme}/{authority}/{local-path}
curl http://linkeddata.uriburner.com/about/data/xml/{scheme}/{authority}/{local-path}

Conclusion

URIBurner is a "deceptively simple" solution for cost-effective exploitation of HTTP based Linked Data meshes. It doesn't require any programming or customization en route to immediately realizing its virtues.

If you like what URIBurner offers, but prefer to leverage its capabilities within your domain -- such that resource description URLs reside in your domain, all you have to do is perform the following steps:

download a copy of Virtuoso (for local desktop, workgroup, or data center installation) or
instantiate Virtuoso via the Amazon EC2 Cloud
enable the Sponger Middleware component via the RDF Mapper VAD package (which includes cartridges for over 30 different resources types)

When you install your own URIBurner instances, you also have the ability to perform customizations that increase resource description fidelity in line with your specific needs. All you need to do is develop a custom extractor cartridge and/or meta cartridge.

Virtuoso Sponger Middleware -- (technology behind URIBurner Service)
Animation demonstrating how the Virtuoso Sponger works.

Meshups Demonstrating How SPARQL-GEO Enhances Linked Data Exploitation (Update 1)

Sat, 06 Mar 2010 22:43:49 GMT

Deceptively simple demonstrations of how Virtuoso's SPARQL-GEO extensions to SPARQL lay critical foundation for Geo Spatial solutions that seek to leverage the burgeoning Web of Linked Data.

Setup Information

SPARQL Endpoint: Linked Open Data Cache (8.5 Billion+ Quad Store which includes data from Geonames and the Linked GeoData Project Data Sets) .

Live Linked Data Meshup Links:

LinkedGeoData things within 2km ORDER BY Dist LIMIT 10 (Use from iPhone only since its an iPhone oriented Linked Data driven application)
LinkedGeoData things within 2km of Trafalgar Square | ORDER By Distance - closest first | ORDER By Distance - most distant first .

Revisiting HTTP based Linked Data (Update 1 - Demo Video Links Added)

Thu, 04 Mar 2010 15:16:14 GMT

Motivation for this post arose from a series of Twitter exchanges between Tony Hirst and I, in relation to his blog post titled: So What Is It About Linked Data that Makes it Linked Data™ ?

At the end of the marathon session, it was clear to me that a blog post was required for future reference, at the very least :-)

What is Linked Data?

"Data Access by Reference" mechanism for Data Objects (or Entities) on HTTP networks. It enables you to Identify a Data Object and Access its structured Data Representation via a single Generic HTTP scheme based Identifier (HTTP URI). Data Object representation formats may vary; but in all cases, they are hypermedia oriented, fully structured, and negotiable within the context of a client-server message exchange.

Why is it Important?

Information makes the world tick!

Information doesn't exist without data to contextualize.

Information is inaccessible without a projection (presentation) medium.

All information (without exception, when produced by humans) is subjective. Thus, to truly maximize the innate heterogeneity of collective human intelligence, loose coupling of our information and associated data sources is imperative.

How is Linked Data Delivered?

Linked Data is exposed to HTTP networks (e.g. World Wide Web) via hypermedia resources bearing structured representations of data object descriptions. Remember, you have a single Identifier abstraction (generic HTTP URI) that embodies: Data Object Name and Data Representation Location (aka URL).

How are Linked Data Object Representations Structured?

A structured representation of data exists when an Entity (Datum), its Attributes, and its Attribute Values are clearly discernible. In the case of a Linked Data Object, structured descriptions take the form of a hypermedia based Entity-Attribute-Value (EAV) graph pictorial -- where each Entity, its Attributes, and its Attribute Values (optionally) are identified using Generic HTTP URIs.

Examples of structured data representation formats (content types) associated with Linked Data Objects include:

text/html
text/turtle
text/n3
application/json
application/rdf+xml
Others

How Do I Create Linked Data oriented Hypermedia Resources?

You markup resources by expressing distinct entity-attribute-value statements (basically these a 3-tuple records) using a variety of notations:

(X)HTML+RDFa,
JSON,
Turtle,
N3,
TriX,
TriG,
RDF/XML, and
Others (for instance you can use Atom data format extensions to model EAV graph as per OData initiative from Microsoft).

You can achieve this task using any of the following approaches:

Notepad
WYSIWYG Editor
Transformation of Database Records via Middleware
Transformation of XML based Web Services output via Middleware
Transformation of other Hypermedia Resources via Middleware
Transformation of non Hypermedia Resources via Middleware
Use a platform that delivers all of the above.

Practical Examples of Linked Data Objects Enable

Describe Who You Are, What You Offer, and What You Need via your structured profile, then leave your HTTP network to perform the REST (serendipitous discovery of relevant things)
Identify (via map overlay) all items of interest based on a 2km+ radious of my current location (this could include vendor offerings or services sought by existing or future customers)
Share the latest and greatest family photos with family members *only* without forcing them to signup for Yet Another Web 2.0 service or Social Network
No repetitive signup and username and password based login sequences per Web 2.0 or Mobile Application combo
Going beyond imprecise Keyword Search to the new frontier of Precision Find - Example, Find Data Objects associated with the keywords: Tiger, while enabling the seeker disambiguate across the "Who", "What", "Where", "When" dimensions (with negation capability)
Determine how two Data Objects are Connected - person to person, person to subject matter etc. (LinkedIn outside the walled garden)
Use any resource address (e.g blog or bookmark URL) as the conduit into a Data Object mesh that exposes all associated Entities and their social network relationships
Apply patterns (social dimensions) above to traditional enterprise data sources in combination (optionally) with external data without compromising security etc.

How Do OpenLink Software Products Enable Linked Data Exploitation?

Our data access middleware heritage (which spans 16+ years) has enabled us to assemble a rich portfolio of coherently integrated products that enable cost-effective evaluation and utilization of Linked Data, without writing a single line of code, or exposing you to the hidden, but extensive admin and configuration costs. Post installation, the benefits of Linked Data simply materialize (along the lines described above).

Our main Linked Data oriented products include:

OpenLink Data Explorer -- visualizes Linked Data or Linked Data transformed "on the fly" from hypermedia and non hypermedia data sources
URIBurner -- a "deceptively simple" solution that enables the generation of Linked Data "on the fly" from a broad collection of data sources and resource types
OpenLink Data Spaces -- a platform for enterprises and individuals that enhances distributed collaboration via Linked Data driven virtualization of data across its native and/or 3rd party content manager for: Blogs, Wikis, Shared Bookmarks, Discussion Forums, Social Networks etc
OpenLink Virtuoso -- a secure and high-performance native hybrid data server (Relational, RDF-Graph, Document models) that includes in-built Linked Data transformation middleware (aka. Sponger).

Hypertext Transfer Protocol 1.1 RFC
Open Data Protocol Glossary
Simple Explanation of RDF and Linked Data Dynamics
Collection of post from the past about Linked Data
Are We There Yet Re. Web++? -- includes link to podcast conversation with Jon Udell
Web of Linked Data Pivoting Demo from TED -- by Microsoft's Gary Flake
Microsoft Pivot atop Virtuoso Quad Store's Faceted Browser Engine-- My Demonstration of EAV model transcending data representation variations (i.e., RDF's EAV data model data served up in Microsoft CXML data representation format).

Linked Data & Socially Enhanced Collaboration (Enterprise or Individual) -- Update 1

Tue, 02 Mar 2010 20:47:54 GMT

Socially enhanced enterprise and invididual collaboration is becoming a focal point for a variety of solutions that offer erswhile distinct content managment features across the realms of Blogging, Wikis, Shared Bookmarks, Discussion Forums etc.. as part of an integrated platform suite. Recently, Socialtext has caught my attention courtesy of its nice features and benefits page . In addition, I've also found the Mike 2.0 portal immensely interesting and valuable, for those with an enterprise collaboration bent.

Anyway, Socialtext and Mike 2.0 (they aren't identical and juxtaposition isn't seeking to imply this) provide nice demonstrations of socially enhanced collaboration for individuals and/or enterprises is all about:

Identifying Yourself
Identifying Others (key contributors, peers, collaborators)
Serendipitous Discovery of key contributors, peers, and collaborators
Serendipitous Discovery by key contributors, peers, and collaborators
Develop and sustain relationships via socially enhanced professional network hybrid
Utilize your new "trusted network" (which you've personally indexed) when seeking help or propagating a meme.

As is typically the case in this emerging realm, the critical issue of discrete "identifiers" (record keys in sense) for data items, data containers, and data creators (individuals and groups) is overlooked albeit unintentionally.

How HTTP based Linked Data Addresses the Identifier Issue

Rather than using platform constrained identifiers such as:

email address (a "mailto" scheme identifier),
a dbms user account,
application specific account, or
OpenID.

It enables you to leverage the platform independence of HTTP scheme Identifiers (Generic URIs) such that Identifiers for:

You,
Your Peers,
Your Groups, and
Your Activity Generated Data,

simply become conduits into a mesh of HTTP -- referencable and accessible -- Linked Data Objects endowed with High SDQ (Serendipitious Discovery Quotient). For example my Personal WebID is all anyone needs to know if they want to explore:

My Profile (which includes references to data objects associated with my interests, social-network, calendar, bookmarks etc.)
Data generated by my activities across various data spaces (via data objects associated with my online accounts e.g. Del.icio.us, Twitter, Last.FM)
Linked Data Meshups via URIBurner (or any other Virtuoso instance) that provide an extend view of my profile

How FOAF+SSL adds Socially aware Security

Even when you reach a point of equilibrium where: your daily activities trigger orchestratestration of CRUD (Create, Read, Update, Delete) operations against Linked Data Objects within your socially enhanced collaboration network, you still have to deal with the thorny issues of security, that includes the following:

Single Sign On,
Authentication, and
Data Access Policies.

FOAF+SSL, an application of HTTP based Linked Data, enables you to enhance your Personal HTTP scheme based Identifer (or WebID) via the following steps (peformed by a FOAF+SSL compliant platform):

Imprint WebID within a self-signed x.509 based public key (certificate) associated with your private key (generated by FOAF+SSL platform or manually via OpenSSL)
Store public key components (modulous and exponent) into your FOAF based profile document which references your Personal HTTP Identifier as its primary topic
Leverage HTTP URL component of WebID for making public key components (modulous and exponent) available for x.509 certificate based authentication challenges posed by systems secured by FOAF+SSL (directly) or OpenID (indirectly via FOAF+SSL to OpenID proxy services).

Contrary to conventional experiences with all things PKI (Public Key Infrastructure) related, FOAF+SSL compliant platforms typically handle the PKI issues as part of the protocol implementation; thereby protecting you from any administrative tedium without compromising security.

Conclusions

Understanding how new technology innovations address long standing problems, or understanding how new solutions inadvertently fail to address old problems, provides time tested mechanisms for product selection and value proposition comprehension that ultimately save scarce resources such as time and money.

If you want to understand real world problem solution #1 with regards to HTTP based Linked Data look no further than the issues of secure, socially aware, and platform independent identifiers for data objects, that build bridges across erstwhile data silos.

If you want to cost-effectively experience what I've outlined in this post, take a look at OpenLink Data Spaces (ODS) which is a distributed collaboration engine (enterprise of individual) built around the Virtuoso database engines. It simply enhances existing collaboration tools via the following capabilities:

Addition of Social Dimensions via HTTP based Data Object Identifiers for all Data Items (if missing)

Ability to integrate across a myriad of Data Source Types rather than a select few across RDBM Engines, LDAP, Web Services, and various HTTP accessible Resources (Hypermedia or Non Hypermedia content types)
Addition of FOAF+SSL based authentication
Addition of FOAF+SSL based Access Control Lists (ACLs) for policy based data access.

Get Yourself A WebID in 5 Minutes or Less via OpenLink Data Spaces (an application layer built atop Virtuoso)
How To Share Resources Securely Using FOAF+SSL
FOAF+SSL & WebID Demonstration
OpenLink Data Spaces & Data Portability.

OpenLink Virtuoso - Product Value Proposition Overiew

Fri, 26 Feb 2010 19:12:32 GMT

Situation Analysis

Since the beginning of the modern IT era, each period of innovation has inadvertently introduced its fair share of Data Silos. The driving force behind this anomaly remains an overemphasis on the role of applications when selecting problem solutions. Unfortunately, most solution selecting decision makers remain oblivious to the fact that most applications are architecturally monolithic; i.e., they fail to separate the following five layers that are critical to all solutions:

Data Unit (Datum or Data Object) Identity,
Data Storage/Persistence,
Data Access,
Data Representation, and
Data Presentation/Visualization.

The rise of the Internet, and its exponentially-growing user-friendly enclave known as the World Wide Web, is bringing the intrinsic costs of the monolithic application architecture anomaly to bear -- in manners unanticipated by many. For example, the emergence of network-oriented solutions across the realms of Enterprise 2.0-based Collaboration and Web 2.0-based Software-as-a-Service (SaaS), combined with the overarching influence of Social Media, are producing more heterogeneously-structured and disparately-located data sources than people can effectively process.

As is often the case, a variety of problem and product monikers have emerged for the data access and integration challenges outlined above. Contemporary examples include Enterprise Information Integration, Master Data Management, and Data Virtualization. Labeling aside, the fundamental issues of the unresolved Data Integration challenge boil down to the following:

Data Model Heterogeneity
Data Quality (Cleanliness)
Semantic Variance across Contexts (e.g., weights and measures).

Effectively solving today's data integration challenges requires a move away from monolithic application architecture to loosely-coupled, network-centric application architectures. Basically, we need a ubiquitous network-centric application protocol that lends itself to loosely-coupled across-the-wire orchestration of data interactions. In short, this will be what revitalizes the art of application development and deployment.

The World Wide Web is built around a network application protocol called HTTP. This protocol intrinsically separates the five layers listed earlier, thereby enabling:

Use of Generic HTTP URIs as Data Object (Entity) Identifiers;
Identifier Co-reference, such that multiple Data Object Identifiers may reference the same Data Object;
Use of the Entity-Attribute-Value Model to describe Data Objects using real world modeling friendly conceptual graphs;
Use of HTTP URLs to Identify Locations of Resources that bear (host) Data Object Descriptions (Representations);
Data Access mechanism for retrieving Data Object Representations from persistent or transient storage locations.

What is Virtuoso?

A uniquely designed to address today's escalating Data Access and Integration challenges without compromising performance, security, or platform independence. At its core lies an unrivaled commitment to industry standards combined with unique technology innovation that transcends erstwhile distinct realms such as:

Data Management (Relational, RDF Graph, or Document),
Data Access Middleware,
Web Application & Services Deployment,
Linked Data Deployment, and
Messaging.

When Virtuoso is installed and running, HTTP-based Data Objects are automatically created as a by-product of its powerful data virtualization, transcending data sources and data representation formats. The benefits of such power extend across profiles such as:

Product Benefits Summary

Enterprise Agility — Virtuoso lets you mix-&-match best-of-class combinations of Operating Systems, Programming Environments, Database Engines and Data-Access Middleware when building or tweaking your IS infrastructure, without the typical impedance of vendor-lock-in.
Data Model Dexterity — By supporting multiple protocols and data models in a single product, Virtuoso protects you against costly vulnerabilities such as: perennial acquisition and accumulation of expensive data model specific DBMS products that still operate on the fundamental principle of: proprietary technology lock-in, at a time when heterogeneity continues to intrinsically define the information technology landscape.
Cost-effectiveness — By providing a single point of access (and single-sign-on, SSO) to a plethora of Web 2.0-style social networks, Web Services, and Content Management Systems, and by using Data Object Identifiers as units of Data Virtualization that become the focal points of all data access, Virtuoso lowers the cost to exploit emerging frontiers such as socially-enhanced enterprise collaboration.
Speed of Exploitation — Virtuoso provides the ability to rapidly assemble 360-degree conceptual views of data, across internal line-of-business application (CRM, ERP, ECM, HR, etc.) data and/or external data sources, whether these are unstructured, semi-structured, or fully structured.

Bottom line, Virtuoso delivers unrivaled flexibility and scalability, without compromising performance or security.

Re-introducing the Virtuoso Virtual Database Engine

Wed, 17 Feb 2010 21:38:01 GMT

In recent times a lot of the commentary and focus re. Virtuoso has centered on the RDF Quad Store and Linked Data. What sometimes gets overlooked is the sophisticated Virtual Database Engine that provides the foundation for all of Virtuoso's data integration capabilities.

In this post I provide a brief re-introduction to this essential aspect of Virtuoso.

What is it?

This component of Virtuoso is known as the Virtual Database Engine (VDBMS). It provides transparent high-performance and secure access to disparate data sources that are external to Virtuoso. It enables federated access and integration of data hosted by any ODBC- or JDBC-accessible RDBMS, RDF Store, XML database, or Document (Free Text)-oriented Content Management System. In addition, it facilitates integration with Web Services (SOAP-based SOA RPCs or REST-fully accessible Web Resources).

Why is it important?

In the most basic sense, you shouldn't need to upgrade your existing database engine version simply because your current DBMS and Data Access Driver combo isn't compatible with ODBC-compliant desktop tools such as Microsoft Access, Crystal Reports, BusinessObjects, Impromptu, or other of ODBC, JDBC, ADO.NET, or OLE DB-compliant applications. Simply place Virtuoso in front of your so-called "legacy database," and let it deliver the compliance levels sought by these tools

In addition, it's important to note that today's enterprise, through application evolution, company mergers, or acquisitions, is often faced with disparately-structured data residing in any number of line-of-business-oriented data silos. Compounding the problem is the exponential growth of user-generated data via new social media-oriented collaboration tools and platforms. For companies to cost-effectively harness the opportunities accorded by the increasing intersection between line-of-business applications and social media, virtualization of data silos must be achieved, and this virtualization must be delivered in a manner that doesn't prohibitively compromise performance or completely undermine security at either the enterprise or personal level. Again, this is what you get by simply installing Virtuoso.

How do I use it?

The VDBMS may be used in a variety of ways, depending on the data access and integration task at hand. Examples include:

Relational Database Federation

You can make a single ODBC, JDBC, ADO.NET, OLE DB, or XMLA connection to multiple ODBC- or JDBC-accessible RDBMS data sources, concurrently, with the ability to perform intelligent distributed joins against externally-hosted database tables. For instance, you can join internal human resources data against internal sales and external stock market data, even when the HR team uses Oracle, the Sales team uses Informix, and the Stock Market figures come from Ingres!

Conceptual Level Data Access using the RDF Model

You can construct RDF Model-based Conceptual Views atop Relational Data Sources. This is about generating HTTP-based Entity-Attribute-Value (E-A-V) graphs using data culled "on the fly" from native or external data sources (Relational Tables/Views, XML-based Web Services, or User Defined Types).

You can also derive RDF Model-based Conceptual Views from Web Resource transformations "on the fly" -- the Virtuoso Sponger (RDFizing middleware component) enables you to generate RDF Model Linked Data via a RESTful Web Service or within the process pipeline of the SPARQL query engine (i.e., you simply use the URL of a Web Resource in the FROM clause of a SPARQL query).

It's important to note that Views take the form of HTTP links that serve as both Data Source Names and Data Source Addresses. This enables you to query and explore relationships across entities (i.e., People, Places, and other Real World Things) via HTTP clients (e.g., Web Browsers) or directly via SPARQL Query Language constructs transmitted over HTTP.

Conceptual Level Data Access using ADO.NET Entity Frameworks

As an alternative to RDF, Virtuoso can expose ADO.NET Entity Frameworks-based Conceptual Views over Relational Data Sources. It achieves this by generating Entity Relationship graphs via its native ADO.NET Provider, exposing all externally attached ODBC- and JDBC-accessible data sources. In addition, the ADO.NET Provider supports direct access to Virtuoso's native RDF database engine, eliminating the need for resource intensive Entity Frameworks model transformations.

The Business Of Linked Data (BOLD) Discussion Space

Sun, 31 Jan 2010 22:48:36 GMT

I've created a new discussion space that's squarely focused on the business development and marketing aspects of "HTTP based Linked Data" (Linked Data). As its name indicates, It's a BOLD attempt to fill a VoiD. :-)

Background

A few months ago, Aldo Bucchi posted a message to the LOD mailing list seeking a discussion space for more business and marketing oriented topic, in relation to Linked Data. At the time, my assumption was that the existing LOD mailing list served that purpose absolutely fine, but in due course I came to realize that Aldo's request had a much lager foundation than I initially suspected.

Historic Oversight

Linked Data, like its umbrella Semantic Web Project, has suffered from an inadvertent oversight on the parts of many of its enthusiasts (myself included): 100% of the discussion spaces are created by, geared towards, or dominated by researchers (from Academia primarily) and/or developers. Thus, at the very least, we've been operating in an echo chamber that only feed the existing void between the core community and those who are more interested in discussing business and marketing related topics.

The new discussion space seeks to cover the following:

Brainstorming Value Proposition Articulation
War Story Exchanges
Case Studies and Use-cases
Market Research & Positioning (for instance Linked Data is killer technology that redefines Data Integration, but none of the major research firms currently make that connection)

How Do I Join The Conversation? Simply sign up on the Google hosted BOLD mailing list, introduce yourself (ideally), and then start conversing! :-)

Getting The Linked Data Value Pyramid Layers Right (Update #2)

Sun, 31 Jan 2010 22:46:47 GMT

One of the real problems that pervades all routes to Linked Data value prop. incomprehension stems from the layering of its value pyramid; especially when communicating with -initially detached- end-users.

Note to Web Programmers: Linked Data is about Data (Wine) and not about Code (Fish). Thus, it isn't a "programmer only zone", far from it. More than anything else, its inherently inclusive and spreads its participation net widely across: Data Architects, Data Integrators, Power Users, Knowledge Workers, Information Workers, Data Analysts, etc.. Basically, everyone that can "click on a link" is invited to this particular party; remember, it is about "Linked Data" not "Linked Code", after all. :-)

Problematic Value Pyramid Layering

Here is an example of a Linked Data value pyramid that I am stumbling across --with some frequency-- these days (note: 1 being the pyramid apex):

SPARQL Queries
RDF Data Stores
RDF Data Sets
HTTP scheme URIs

Basically, Linked Data deployment (assigning de-referencable HTTP URIs to DBMS records, their attributes, and attribute values [optionally] ) is occurring last. Even worse, this happens in the context of Linked Open Data oriented endeavors, resulting in nothing but confusion or inadvertent perpetuation of the overarching pragmatically challenged "Semantic Web" stereotype.

As you can imagine, hitting SPARQL as your introduction to Linked Data is akin to hitting SQL as your introduction to Relational Database Technology, neither is an elevator-style value prop. relay mechanism.

In the relational realm, killer demos always started with desktop productivity tools (spreadsheets, report-writers, SQL QBE tools etc.) accessing, relational data sources en route to unveiling the "Productivity" and "Agility" value prop. that such binding delivered i.e., the desktop application (clients) and the databases (servers) are distinct, but operating in a mutually beneficial manner to all, courtesy of a data access standards such as ODBC (Open Database Connectivity).

In the Linked Data realm, learning to embrace and extend best practices from the relational dbms realm remains a challenge, a lot of this has to do with hangovers from a misguided perception that RDF databases will somehow completely replace RDBMS engines, rather than compliment them. Thus, you have a counter productive variant of NIH (Not Invented Here) in play, taking us to the dreaded realm of: Break the Pot and You Own It (exemplified by the 11+ year Semantic Web Project comprehension and appreciation odyssey).

From my vantage point, here is how I believe the Linked Data value pyramid should be layered, especially when communicating the essential value prop.:

HTTP URLs -- LINKs to documents (Reports) that users already appreciate, across the public Web and/or Intranets
HTTP URIs -- typically not visually distinguishable from the URLs, so use the Data exposed by de-referencing a URL to show how each Data Item (Entity or Object) is uniquely identified by a Generic HTTP URI, and how clicking on the said URIs leads to more structured metadata bearing documents available in a variety of data representation formats, thereby enabling flexible data presentation (e.g., smarter HTML pages)
SPARQL -- when a user appreciates the data representation and presentation dexterity of a Generic HTTP URI, they will be more inclined to drill down an additional layer to unravel how HTTP URIs mechanically deliver such flexibility
RDF Data Stores -- at this stage the user is now interested data sources behind the Generic HTTP URIs, courtesy of natural desire to tweak the data presented in the report; thus, you now have an engaged user ready to absorb the "How Generic HTTP URIs Pull This Off" message
RDF Data Sets -- while attempting to make or tweak HTTP URIs, users become curious about the actual data loaded into the RDF Data Store, which is where data sets used to create powerful Lookup Data Spaces (e.g., DBpedia) come into play such as those from the LOD constellation as exemplified by DBpedia (extractions from Wikipedia).

What is the DBpedia Project? (Updated)

Sun, 31 Jan 2010 22:45:55 GMT

The recent Wikipedia imbroglio centered around DBpedia is the fundamental driver for this particular blog post. At time of writing this blog post, the DBpedia project definition in Wikipedia remains unsatisfactory due to the following shortcomings:

inaccurate and incomplete definition of the Project's What, Why, Who, Where, When, and How
inaccurate reflection of project essence, by skewing focus towards data extraction and data set dump production, which is at best a quarter of the project.

Here are some insights on DBpedia, from the perspective of someone intimately involved with the other three-quarters of the project.

What is DBpedia?

A live Web accessible RDF model database (Quad Store) derived from Wikipedia content snapshots, taken periodically. The RDF database underlies a Linked Data Space comprised of: HTML (and most recently HTML+RDFa) based data browser pages and a SPARQL endpoint.

Note: DBpedia 3.4 now exists in snapshot (warehouse) and Live Editions (currently being hot-staged). This post is about the snapshot (warehouse) edition, I'll drop a different post about the DBpedia Live Edition where a new Delta-Engine covers both extraction and database record replacement, in realtime.

When was it Created?

As an idea under the moniker "DBpedia" it was conceptualized in late 2006 by researchers at University of Leipzig (lead by Soren Auer) and Freie University, Berlin (lead by Chris Bizer). The first public instance of DBpedia (as described above) was released in February 2007. The official DBpedia coming out party occurred at WWW2007, Banff, during the inaugural Linked Data gathering, where it showcased the virtues and immense potential of TimBL's Linked Data meme.

Who's Behind It?

OpenLink Software (developers of OpenLink Virtuoso and providers of Web Hosting infrastructure), University of Leipzig, and Freie Univerity, Berlin. In addition, there is a burgeoning community of collaborators and contributors responsible DBpedia based applications, cross-linked data sets, ontologies (OpenCyc, SUMO, UMBEL, and YAGO) and other utilities. Finally, DBpedia wouldn't be possible without the global content contribution and curation efforts of Wikipedians, a point typically overlooked (albeit inadvertently).

How is it Constructed?

The steps are as follows:

RDF data set dump preparation via Wikipedia content extraction and transformation to RDF model data, using the N3 data representation format - Java and PHP extraction code produced and maintained by the teams at Leipzig and Berlin
Deployment of Linked Data that enables Data browsing and exploration using any HTTP aware user agent (e.g. basic Web Browsers) - handled by OpenLink Virtuoso (handled by Berlin via the Pubby Linked Data Server during the early months of the DBpedia project)
SPARQL compliant Quad Store, enabling direct access to database records via SPARQL (Query language, REST or SOAP Web Service, plus a variety of query results serialization formats) - OpenLink Virtuoso since first public release of DBpedia

In a nutshell, there are four distinct and vital components to DBpedia. Thus, DBpedia doesn't exist if all the project offered was a collection of RDF data dumps. Likewise, it doesn't exist if you have a SPARQL compliant Quad Store without loaded data sets, and of course it doesn't exist if you have a fully loaded SPARQL compliant Quad Store is up to the cocktail of challenges presented by live Web accessibility.

Why is it Important?

It remains a live exemplar for any individual or organization seeking to publishing or exploit HTTP based Linked Data on the World Wide Web. Its existence continues to stimulate growth in both density and quality of the burgeoning Web of Linked Data.

How Do I Use it?

In the most basic sense, simply browse the HTML pages en route to discovery erstwhile relationships that exist across named entities and subject matter concepts / headings. Beyond that, simply look at DBpedia as a master lookup table in a Web hosted distributed database setup; enabling you to mesh your local domain specific details with DBpedia records via structured relations (triples or 3-tuples records) comprised of HTTP URIs from both realms e.g., owl:sameAs relations.

What Can I Use it For?

Expanding on the Master-Details point above, you can use its rich URI corpus to alleviate tedium associated with activities such as:

List maintenance - e.g., Countries, States, Companies, Units of Measurement, Subject Headings etc.
Tagging - as a compliment to existing practices
Analytical Research - you're only a LINK (URI) away from erstwhile difficult to attain research data spread across a broad range of topics
Closed Vocabulary Construction - rather than commence the futile quest of building your own closed vocabulary, simply leverage Wikipedia's human curated vocabulary as our common base.

Pre-loaded and Pre-configured instances of DBpedia 3.4 - via publicly shared Amazon Elastic Block Storage Snapshots
Virtuoso & DBpedia Tunning Guide
What's In a Name & The Linked Data Police.

Getting The Linked Data Value Pyramid Layers Right (Update #2)

Sun, 31 Jan 2010 22:44:04 GMT

Problematic Value Pyramid Layering

Here is an example of a Linked Data value pyramid that I am stumbling across --with some frequency-- these days (note: 1 being the pyramid apex):

SPARQL Queries
RDF Data Stores
RDF Data Sets
HTTP scheme URIs

From my vantage point, here is how I believe the Linked Data value pyramid should be layered, especially when communicating the essential value prop.:

HTTP URLs -- LINKs to documents (Reports) that users already appreciate, across the public Web and/or Intranets
HTTP URIs -- typically not visually distinguishable from the URLs, so use the Data exposed by de-referencing a URL to show how each Data Item (Entity or Object) is uniquely identified by a Generic HTTP URI, and how clicking on the said URIs leads to more structured metadata bearing documents available in a variety of data representation formats, thereby enabling flexible data presentation (e.g., smarter HTML pages)
SPARQL -- when a user appreciates the data representation and presentation dexterity of a Generic HTTP URI, they will be more inclined to drill down an additional layer to unravel how HTTP URIs mechanically deliver such flexibility
RDF Data Stores -- at this stage the user is now interested data sources behind the Generic HTTP URIs, courtesy of natural desire to tweak the data presented in the report; thus, you now have an engaged user ready to absorb the "How Generic HTTP URIs Pull This Off" message
RDF Data Sets -- while attempting to make or tweak HTTP URIs, users become curious about the actual data loaded into the RDF Data Store, which is where data sets used to create powerful Lookup Data Spaces (e.g., DBpedia) come into play such as those from the LOD constellation as exemplified by DBpedia (extractions from Wikipedia).

What is the DBpedia Project? (Updated)

Sun, 31 Jan 2010 22:43:08 GMT

inaccurate and incomplete definition of the Project's What, Why, Who, Where, When, and How
inaccurate reflection of project essence, by skewing focus towards data extraction and data set dump production, which is at best a quarter of the project.

Here are some insights on DBpedia, from the perspective of someone intimately involved with the other three-quarters of the project.

What is DBpedia?

When was it Created?

Who's Behind It?

How is it Constructed?

The steps are as follows:

RDF data set dump preparation via Wikipedia content extraction and transformation to RDF model data, using the N3 data representation format - Java and PHP extraction code produced and maintained by the teams at Leipzig and Berlin
Deployment of Linked Data that enables Data browsing and exploration using any HTTP aware user agent (e.g. basic Web Browsers) - handled by OpenLink Virtuoso (handled by Berlin via the Pubby Linked Data Server during the early months of the DBpedia project)
SPARQL compliant Quad Store, enabling direct access to database records via SPARQL (Query language, REST or SOAP Web Service, plus a variety of query results serialization formats) - OpenLink Virtuoso since first public release of DBpedia

Why is it Important?

How Do I Use it?

What Can I Use it For?

Expanding on the Master-Details point above, you can use its rich URI corpus to alleviate tedium associated with activities such as:

List maintenance - e.g., Countries, States, Companies, Units of Measurement, Subject Headings etc.
Tagging - as a compliment to existing practices
Analytical Research - you're only a LINK (URI) away from erstwhile difficult to attain research data spread across a broad range of topics
Closed Vocabulary Construction - rather than commence the futile quest of building your own closed vocabulary, simply leverage Wikipedia's human curated vocabulary as our common base.

Pre-loaded and Pre-configured instances of DBpedia 3.4 - via publicly shared Amazon Elastic Block Storage Snapshots
Virtuoso & DBpedia Tunning Guide
What's In a Name & The Linked Data Police.

5 Very Important Things to Note about HTTP based Linked Data

Sun, 31 Jan 2010 22:31:35 GMT

It isn't World Wide Web Specific (HTTP != World Wide Web)
It isn't Open Data Specific
It isn't about "Free" (Beer or Speech)
It isn't about Markup (so don't expect to grok it via "markup first" approach)
It's about Hyperdata - the use of HTTP and REST to deliver a powerful platform agnostic mechanism for Data Reference, Access, and Integration.

When trying to understand HTTP based Linked Data, especially if you're well versed in DBMS technology use (User, Power User, Architect, Analyst, DBA, or Programmer) think:

Open Database Connectivity (ODBC) without operating system, data model, or wire-protocol specificity or lock-in potential
Java Database Connectivity (JDBC) without programming language specificity
ADO.NET without .NET runtime specificity and .NET bound language specificity
OLE-DB without Windows operating system & programming language specificity
XMLA without XML format specificity - with Tabular and Multidimensional results formats expressible in a variety of data representation formats.
All of the above scoped to the Record rather than Container level, with Generic HTTP scheme URIs associated with each Record, Field, and Field value (optionally)

Remember the need for Data Access & Integration technology is the by product of the following realities:

Human curated data is ultimately dirty, because:
- our thick thumbs, inattention, distractions, and general discomfort with typing, make typos prevalent
- database engines exist for a variety of data models - Graph, Relational, Hierarchical;
- within databases you have different record container/partition names e.g. Table Names;
- within a database record container you have records that are really aspects of the same thing (different keys exist in a plethora of operational / line of business systems that expose aspects of the same entity e.g., customer data that spans Accounts, CRM, ERP application databases);
- different field names (one database has "EMP" while another has "Employee") for the same record
Units of measurement is driven by locale, the UK office wants to see sales in Pounds Sterling while the French office prefers Euros etc.
All of the above is subject to context halos which can be quite granular re. sensitivity e.g. staff travel between locations that alter locales and their roles; basically, profiles matters a lot.

5 Game Changing Things about the OpenLink Virtuoso + AWS Cloud Combo

Sun, 31 Jan 2010 22:29:34 GMT

Here are 5 powerful benefits you can immediately derive from the combination of Virtuoso and Amazon's AWS services (specifically the EC2 and EBS components):

Acquire your own personal or service specific data space in the Cloud. Think DBase, Paradox, FoxPRO, Access of yore, but with the power of Oracle, Informix, Microsoft SQL Server etc.. using a Conceptual, as opposed to solely Logical, model based DBMS (i.e., a Hybrid DBMS Engine for: SQL, RDF, XML, and Full Text)
Ability to share and control access to your resources using innovations like FOAF+SSL, OpenID, and OAuth, all from one place
Construction of personal or organization based FOAF profiles in a matter of minutes; by simply creating a basic DBMS (or ODS application layer) account; and then using this profile to create strong links (references) to all your Data silos (esp. those from the Web 2.0 realm)
Load data sets from the LOD cloud or Sponge existing Web resources (i.e., on the fly data transformation to RDF model based Linked Data) and then use the combination to build powerful lookup services that enrich the value of URLs (think: Web addressable reports holding query results) that you publish
Bind all of the above to a domain that you own (e.g. a .Name domain) so that you have an attribution-friendly "authority" component for resource URLs and Entity URIs published from your Personal Linked Data Space on the Web (or private HTTP network).

In a nutshell, the AWS Cloud infrastructure simplifies the process of generating Federated presence on the Internet and/or World Wide Web. Remember, centralized networking models always end up creating data silos, in some context, ultimately! :-)

Virtuoso Chronicles from the Field: Nepomuk, KDE, and the quest for a sophisticated RDF DBMS.

Thu, 28 Jan 2010 16:14:04 GMT

For this particular user experience chronicle, I've simply inserted the content of Sebastian Trueg's post titled: What We Did Last Summer (And the Rest of 2009) – A Look Back Onto the Nepomuk Development Year ..., directly into this post, without any additional commentary or modification.

2009 is over. Yeah, sure, trueg, we know that, it has been over for a while now! Ok, ok, I am a bit late, but still I would like to get this one out - if only for my archive. So here goes.

Virtuoso

Let’s start with the major topic of 2009 (and also the beginning of 2010): The new Nepomuk database backend: Virtuoso. Everybody who used Nepomuk had the same problems: you either used the sesame2 backend which depends on Java and steals all of your memory or you were stuck with Redland which had the worst performance and missed some SPARQL features making important parts of Nepomuk like queries unusable. So more than a year ago I had the idea to use the one GPL’ed database server out there that supported RDF in a professional manner: OpenLink’s Virtuoso. It has all the features we need, has a very good performance, and scales up to dimensions we will probably never reach on the desktop (yeah, right, and 64k main memory will be enough forever!). So very early I started coding the necessary Soprano plugin which would talk to a locally running Virtuoso server through ODBC. But since I ran into tons of small problems (as always) and got sidetracked by other tasks I did not finish it right away. OpenLink, however, was very interested in the idea of their server being part of every KDE installation (why wouldn’t they ;)). So they not only introduced a lite-mode which makes Virtuoso suitable for the desktop but also helped in debugging all the problems that I had left. Many test runs, patches, and a Virtuoso 5.0.12 release later I could finally announce the Virtuoso integration as usable.

Then end of last year I dropped the support for sesame2 and redland. Virtuoso is now the only supported database backend. The reason is simple: Virtuoso is way more powerful than the rest - not only in terms of performance - and it is fully implemented in C(++) without any traces of Java. Maybe even more important is the integration of the full text index which makes the previously used CLucene index unnecessary. Thus, we can finally combine full text and graph queries in one SPARQL query. This results in a cleaner API and way faster return of search results since there is no need to combine the results from several queries anymore. A direct result of that is the new Nepomuk Query API which I will discuss later.

So now the only thing I am waiting for is the first bugfix release of Virtuoso 6, i.e. 6.0.1 which will fix the bugs that make 6.0.0 fail with Nepomuk. Should be out any day now. :)

The Nepomuk Query API

Querying data in Nepomuk pre-KDE-4.4 could be done in one of two ways: 1. Use the very limited capabilities of the ResourceManager to list resources with certain properties or of a certain type; or 2. Write your own SPARQL query using ugly QString::arg replacements.

With the introduction of Virtuoso and its awesome power we can now do pretty much everything in one query. This allowed me to finally create a query API for KDE: Nepomuk::Query::Query and friends. I won’t go into much detail here since I did that before.

All in all you should remember one thing: whenever you think about writing your own SPARQL query in a KDE application - have a look at libnepomukquery. It is very likely that you can avoid the hassle of debugging a query by using the query API.

The first nice effect of the new API (apart from me using it all over the place obviously) is the new query interface in Dolphin. Internally it simply combines a bunch of Nepomuk::Query::Term objects into a Nepomuk::Query::AndTerm. All very readable and no ugly query strings.

Dolphin Search Panel in KDE SC 4.4

Shared Desktop Ontologies

An important part of the Nepomuk research project was the creation of a set of ontologies for describing desktop resources and their metadata. After the Xesam project under the umbrella of freedesktop.org had been convinced to use RDF for describing file metadata they developed their own ontology. Thanks to Evgeny (phreedom) Egorochkin and Antonie Mylka both the Xesam ontology and the Nepomuk Information Elements Ontology were already very close in design. Thus, it was relatively easy to merge the two and be left with only one ontology to support. Since then not only KDE but also Strigi and Tracker are using the Nepomuk ontologies.

At the Gran Canaria Desktop Summit I met some of the guys from Tracker and we tried to come up with a plan to create a joint project to maintain the ontologies. This got off to a rough start as nobody really felt responsible. So I simply took the initiative and released the shared-desktop-ontologies version 0.1 in November 2009. The result was a s***-load of hate-mails and bug reports due to me breaking KDE build. But in the end it was worth it. Now the package is established and other projects can start to pick it up to create data compatible to the Nepomuk system and Tracker.

Today the ontologies (and the shared-desktop-ontologies package) are maintained in the Oscaf project at Sourceforge. The situation is far from perfect but it is a good start. If you need specific properties in the ontologies or are thinking about creating one for your own application - come and join us in the bug tracker…

Timeline KIO Slave

It was at the Akonadi meeting that Will Stephenson and myself got into talking about mimicking some Zeitgeist functionality through Nepomuk. Basically it meant gathering some data when opening and when saving files. We quickly came up with a hacky patch for KIO and KFileDialog which covered most cases and allowed us to track when a file was modified and by which application. This little experiment did not leave that state though (it will, however, this year) but another one did: Zeitgeist also provides a fuse filesystem which allows to browse the files by modification dates. Well, whatever fuse can do, KIO can do as well. Introducing the timeline:/ KIO slave which gives a calendar view onto your files.

Tips And Tricks

Well, I thought I would mention the Tips And Tricks section I wrote for the techbase. It might not be a big deal but I think it contains some valuable information in case you are using Nepomuk as a developer.

Google Summer Of Code 2009

This time around I had the privilege to mentor two students in the Google Summer of Code. Alessandro Sivieri and Adam Kidder did outstanding work on Improved Virtual Folders and the Smart File Dialog.

Adam’s work lead me to some heavy improvements in the Nepomuk KIO slaves myself which I only finished this week (more details on that coming up). Alessandro continued his work on faceted file browsing in KDE and created:

Sembrowser

Alessandro is following up on his work to make faceted file browsing a reality in 2010 (and KDE SC 4.5). Since it was too late to get faceted browsing into KDE SC 4.4 he is working on Sembrowser, a stand-alone faceted file browser which will be the grounds for experiments until the code is merged into Dolphin.

Faceted Browsing in KDE with Sembrowser

Nepomuk Workshops

In 2009 I organized the first Nepomuk workshop in Freiburg, Germany. And also the second one. While I reported properly on the first one I still owe a summary for the second one. I will get around to that - sooner or later. ;)

CMake Magic

Soprano gives us a nice command line tool to create a C++ namespace from an ontology file: onto2vocabularyclass. It produces nice convenience namespaces like Soprano::Vocabulary::NAO. Nepomuk adds another tool named nepomuk-rcgen. Both were a bit clumsy to use before. Now we have nice cmake macros which make it very simple to use both.

See the techbase article on how to use the new macros.

Bangarang

Without my knowledge (imagine that!) Andrew Lake created an amazing new media player named Bangarang - a Jamaican word for noise, chaos or disorder. This player is Nepomuk-enabled in the sense that it has a media library which lets you browse your media files based on the Nepomuk data. It remembers the number of times a song or a video has been played and when it was played last. It allows to add detail such as the TV series name, season, episode number, or actors that are in the video - all through Nepomuk (I hope we will soon get tvdb integration).

Edit metadata directly in Bangarang

Dolphin showing TV episode metadata created by Bangarang

And of course searching for it works, too...

And it is pretty, too...

I am especially excited about this since finally applications not written or mentored by me start contributing Nepomuk data.

Gran Canaria Desktop Summit

2009 was also the year of the first Gnome-KDE joint-conference. Let me make a bulletin for completeness and refer to my previous blog post reporting on my experiences on the island.

Well, that was by far not all I did in 2009 but I think I covered most of the important topics. And after all it is ‘just a blog entry’ - there is no need for completeness. Thanks for reading.

One Technology That Will Rock 2010 (Update 1)

Sat, 02 Jan 2010 17:30:38 GMT

Thanks to the TechCrunch post titled: Ten Technologies That Will Rock 2010, I've been able to quickly construct a derivative post that condenses the ten item list down to a Single Technology That Will Rock 2010 :-)

Sticking with the TechCrunch layout, here is why all roads simply lead to Linked Data come 2010 and beyond:

The Tablet: a new form factor addition re. Internet and Web application hosts which is just another way of saying: Linked Data will be accessible from Tablet applications.
Geo: GPS chips are now standard features of mobile phones, so geolocation is increasingly becoming a necessary feature for any killer app. Thus, GeoSpatial Linked Data and GeopSpatial Queries are going to be a critical success factor for any endeavor that seeks to engage mobile applications developers and ultimately their end-users. Basiacally, you want to be able to perform Esoteric Search from these devices of the form: Find Vendors of a Camcorder (e.g., with a Zoom Factor: Weight Ratio of X) within a 2km Radius of my current location. Or how many items from my WishList are available from a Vendor within a 2km radius of my current location. Conversely, provide Vendors with the ability to spot potential Customers within a 2km of a given "clicks & mortar" location (e.g. BestBuy store).
Realtime Search: Rich Structured Profiles that leverage standards such as FOAF and FOAF+SSL will enable Highly Personalized Realtime Search (HPRS) without compromisng privacy. Tecnically, this is about WebIDs securely bound to X.509 Certificates, providing access to verifiable and highly navigable Personal Profile Data Spaces that also double as personal search index entry points.
Chrome OS: Just another operating system for exploiting the burgeoning Web of Linked Data
HTML5: Courtesy of RDFa, just another mechanism for exposing Linked Data by making HTML+RDFa a bona fide markup for metadata (i.e., format for describing real world objects via their attribute-value graphs)
Mobile Video: Simplifies the production and sharing of Video annotations (comments, reviews etc.) en route to creating rich Linked Discourse Data Spaces.
Augmented Reality: Ditto
Mobile Transactions: As per points 1&2 above, Vendor Discovery and Transaction Conusmation will increasingly be driven by high SDQ applications. The "Funnel Effect" (more choices based on individual preferences) will be a critical success factor for any one operating in the Mobile Transaction realm. Note, without Linked Data you cannot deliver scalable solutions that handle the combined requirements of: SDQ, "Funnel Effect", and Mobile Device form factor, will simply maginify the importance of Web accessible Linked Data.
Android: An additional platform for items 1-8; basically, 2010 isn't going to be an iPhone only zone. Personally, this reminds me of a battle from the past i.e., Microsoft vs Apple, re. desktop computing dominance. Google has studied history very well :-)
Social CRM: this is simply about applying points 1-9 alongide the construction of Linked Data from eCRM Data Spaces.

As I've stated in the past (across a variety of mediums), you cannot build applications that have long term value without addressing the following issues:

Data Item or Object Identity
Data Structure -- Data Models
Data Representation -- Data Model Entity & Relationships Representation mechanism (as delivered by metadata oriented markup)
Data Storage -- Database Management Systems
Data Access -- Data Access Protocols
Data Presentation -- How you present Views and Reports from Structured Data Sources
Data Security -- Data Access Policies

The items above basically showcase the very essence of the HTTP URI abstraction that drives HTTP based Linked Data; which is also the basic payload unit that underlies REST.

Conclusion

I simply hope that the next decade marks a period of broad appreciation and comprehension of Data Access, Integration, and Management issues on the parts of: application developers, integrators, analysts, end-users, and decision makers. Remember, without structured Data we cannot produce or share Information, and without Information, we cannot produce of share Knowledge.

Kingsley Idehen's Blog Data Space

URIBurner: Painless Generation & Exploitation of Linked Data (Update 1 - Demo Links Added)

What is URIBurner?

Why is it Important?

How Do I Use It?

Content Publisher

Examples

Content Consumer

Examples

JSON:

Notation 3 (N3):

RDF/XML:

Conclusion

Related:

Meshups Demonstrating How SPARQL-GEO Enhances Linked Data Exploitation (Update 1)

Setup Information

Live Linked Data Meshup Links:

Related

Revisiting HTTP based Linked Data (Update 1 - Demo Video Links Added)

What is Linked Data?

Why is it Important?

How is Linked Data Delivered?

How are Linked Data Object Representations Structured?

How Do I Create Linked Data oriented Hypermedia Resources?

Practical Examples of Linked Data Objects Enable

How Do OpenLink Software Products Enable Linked Data Exploitation?

Related

Linked Data & Socially Enhanced Collaboration (Enterprise or Individual) -- Update 1

How HTTP based Linked Data Addresses the Identifier Issue

How FOAF+SSL adds Socially aware Security

Conclusions

Related:

OpenLink Virtuoso - Product Value Proposition Overiew

Situation Analysis

What is Virtuoso?

Product Benefits Summary

Related

Re-introducing the Virtuoso Virtual Database Engine

What is it?

Why is it important?

How do I use it?

Relational Database Federation

Conceptual Level Data Access using the RDF Model

Conceptual Level Data Access using ADO.NET Entity Frameworks

Related

The Business Of Linked Data (BOLD) Discussion Space

Background

Historic Oversight

Getting The Linked Data Value Pyramid Layers Right (Update #2)

Problematic Value Pyramid Layering

Related

What is the DBpedia Project? (Updated)

What is DBpedia?

When was it Created?

Who's Behind It?

How is it Constructed?

Why is it Important?

How Do I Use it?

What Can I Use it For?

Related

Getting The Linked Data Value Pyramid Layers Right (Update #2)

Problematic Value Pyramid Layering

Related

What is the DBpedia Project? (Updated)

What is DBpedia?

When was it Created?

Who's Behind It?

How is it Constructed?

Why is it Important?

How Do I Use it?

What Can I Use it For?

Related

5 Very Important Things to Note about HTTP based Linked Data

Related

5 Game Changing Things about the OpenLink Virtuoso + AWS Cloud Combo

Virtuoso Chronicles from the Field: Nepomuk, KDE, and the quest for a sophisticated RDF DBMS.

Virtuoso

The Nepomuk Query API

Shared Desktop Ontologies

Timeline KIO Slave