Kingsley Uyi Idehen's Weblog

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 .

Related

• Collection of Live Linked Data Demos
• Virtuoso's SPARQL-GEO Extensions

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.

Related

• Attaching ODBC or JDBC accessible Relational Tables to Virtuoso
• Using an HTML based Wizard to Generate RDF based Linked Views over Relational Tables
• Screencast Demonstrating Wizard based generation of RDF based Linked Data Views Part 1
• Screencast Demonstrating Wizard based generation of RDF based Linked Data Views Part 1
• Generating RDF based Linked Data from non RDF based Web Resources via the Sponger
• Building ADO.NET based Entity Frameworks Views over Relational Data
• Building Silverlight Rich Internat Applicaitons using ADO.NET, Entity Frameworks, and RDF based Linked Data.

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.

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:

1. 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.
2. 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).
3. 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.
4. Chrome OS: Just another operating system for exploiting the burgeoning Web of Linked Data
5. 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)
6. Mobile Video: Simplifies the production and sharing of Video annotations (comments, reviews etc.) en route to creating rich Linked Discourse Data Spaces.
7. Augmented Reality: Ditto
8. 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.
9. 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 :-)
10. 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:

1. Data Item or Object Identity
2. Data Structure -- Data Models
3. Data Representation -- Data Model Entity & Relationships Representation mechanism (as delivered by metadata oriented markup)
4. Data Storage -- Database Management Systems
5. Data Access -- Data Access Protocols
6. Data Presentation -- How you present Views and Reports from Structured Data Sources
7. 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.

Related

• HTTP URI Abstraction and Linked Data
• First Law of Data Quality
• Who's Data Is It?
• Serendipitous Discovery Quotient (SDQ)
• SDQ: The Future of SEO or an Abstract Concept?
• SPARQL & GeoSpatial Indexing (SPARQL-GEO)
• Mastering Your Own Search Index
• Solving the Paradox of Choice.

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:

1. 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.
2. 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).
3. 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.
4. Chrome OS: Just another operating system for exploiting the burgeoning Web of Linked Data
5. 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)
6. Mobile Video: Simplifies the production and sharing of Video annotations (comments, reviews etc.) en route to creating rich Linked Discourse Data Spaces.
7. Augmented Reality: Ditto
8. 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.
9. 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 :-)
10. 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:

1. Data Item or Object Identity
2. Data Structure -- Data Models
3. Data Representation -- Data Model Entity & Relationships Representation mechanism (as delivered by metadata oriented markup)
4. Data Storage -- Database Management Systems
5. Data Access -- Data Access Protocols
6. Data Presentation -- How you present Views and Reports from Structured Data Sources
7. 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.

Related

• HTTP URI Abstraction and Linked Data
• First Law of Data Quality
• Who's Data Is It?
• Serendipitous Discovery Quotient (SDQ)
• SDQ: The Future of SEO or an Abstract Concept?
• SPARQL & GeoSpatial Indexing (implications of SPARQL-GEO)
• Mastering Your Own Search Index
• Solving the Paradox of Choice.

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):

1. SPARQL Queries
2. RDF Data Stores
3. RDF Data Sets
4. 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.:

1. HTTP URLs -- LINKs to documents (Reports) that users already appreciate, across the public Web and/or Intranets
2. 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)
3. 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
4. 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
5. 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).

Related

• Exploring the Linked Data Value Proposition
• Simple Explanation of Linked Data & RDF Dynamics
• What is the Linked Data Meme About?
• Linked Data & Data Item Identifiers (Identity)

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:

1. inaccurate and incomplete definition of the Project's What, Why, Who, Where, When, and How
2. 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:

1. 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
2. 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)
3. 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:

1. List maintenance - e.g., Countries, States, Companies, Units of Measurement, Subject Headings etc.
2. Tagging - as a compliment to existing practices
3. Analytical Research - you're only a LINK (URI) away from erstwhile difficult to attain research data spread across a broad range of topics
4. 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.

Related

• 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.

We have just released an Amazon EC2 based public Snapshot of DBpedia 3.4. Thus, you can now instantiate a personal and/or service specific variant of the DBpedia 3.4 Linked Data Space. Basically, you can replicate what we host, within minutes (as opposed to days). In addition, you no longer need to squabble --on an unpredictable basis with others-- for the infrastructure resources behind DBpedia's public instance, when using the SPARQL Endpoint, Faceted Search & Find Services, or HTML Browser Pages etc.

How Does It work?

1. Instantiate a Virtuoso EC2 AMI (paid variety, which is aggressively priced at $49.99 for setup and $19.99 per month thereafter)
2. Mount the shared DBpedia 3.4 public snapshot
3. Start Virtuoso Server
4. Start exploiting the DBpedia Linked Data Space.

What Interfaces are exposed?

1. SPARQL Endpoint
2. Linked Data Viewer Pages (as you see in the public DBpedia instance)
3. Faceted Search & Find UI and Web Services (REST or SOAP)
4. All the inference rules for UMBEL, SUMO, YAGO, OpenCYC, and DBpedia-OWL data dictionaries
5. Type Correlations Between DBpedia and Freebase

Enjoy!

Situation Analysis

As the "Linked Data" meme has gained momentum you've more than likely been on the receiving end of dialog with Linked Open Data community members (myself included) that goes something like this:

"Do you have a URI", "Get yourself a URI", "Give me a de-referencable URI" etc..

And each time, you respond with a URL -- which to the best of your Web knowledge is a bona fide URI. But to your utter confusion you are told: Nah! You gave me a Document URI instead of the URI of a real-world thing or object etc..

What's up with that?

Well our everyday use of the Web is an unfortunate conflation of two distinct things, which have Identity: Real World Objects (RWOs) & Address/Location of Documents (Information bearing Resources).

The "Linked Data" meme is about enhancing the Web by unobtrusively reintroducing its core essence: the generic HTTP URI, a vital piece of Web Architecture DNA. Basically, its about so realizing the full capabilities of the Web as a platform for Open Data Identification, Definition, Access, Storage, Representation, Presentation, and Integration.

What is a Real World Object?

People, Places, Music, Books, Cars, Ideas, Emotions etc..

What is a URI?

A Uniform Resource Identifier. A global identifier mechanism for network addressable data items. Its sole function is Name oriented Identification.

URI Generic Syntax

The constituent parts of a URI (from URI Generic Syntax RFC) are depicted below:

What is a URL?

A location oriented HTTP scheme based URI. The HTTP scheme introduces a powerful and inherent duality that delivers:

1. Resource Address/Location Identifier
2. Data Access mechanism for an Information bearing Resource (Document, File etc..)

So far so good!

What is an HTTP based URI?

The kind of URI Linked Data aficionados mean when they use the term: URI.

An HTTP URI is an HTTP scheme based URI. Unlike a URL, this kind of HTTP scheme URI is devoid of any Web Location orientation or specificity. Thus, Its inherent duality provides a more powerful level of abstraction. Hence, you can use this form of URI to assign Names/Identifiers to Real World Objects (RWO). Even better, courtesy of the Identity/Address duality of the HTTP scheme, a single URI can deliver the following:

1. RWO Identfier/Name
2. RWO Metadata document Locator (courtesy of URL aspect)
3. Negotiable Representation of the Located Document (courtesy of HTTP's content negotiation feature).

What is Metadata?

Data about Data. Put differently, data that describes other data in a structured manner.

How Do we Model Metadata?

The predominant model for metadata is the Entity-Attribute-Value + Classes & Relationships model (EAV/CR). A model that's been with us since the inception of modern computing (long before the Web).

What about RDF?

The Resource Description Framework (RDF) is a framework for describing Web addressable resources. In a nutshell, its a framework for adding Metadata bearing Information Resources to the current Web. Its comprised of:

1. Entity-Attribute-Value (aka. Subject-Predictate-Object) plus Classes & Relationships (Data Dictionaries e.g., OWL) metadata model
2. A plethora of instance data representation formats that include: RDFa (when doing so within (X)HTML docs), Turtle, N3, TriX, RDF/XML etc.

What's the Problem Today?

The ubiquitous use of the Web is primarily focused on a Linked Mesh of Information bearing Documents. URLs rather than generic HTTP URIs are the prime mechanism for Web tapestry; basically, we use URLs to conduct Information -- which is inherently subjective -- instead of using HTTP URIs to conduct "Raw Data" -- which is inherently objective.

Note: Information is "data in context", it isn't the same thing as "Raw Data". Thus, if we can link to Information via the Web, why shouldn't we be able to do the same for "Raw Data"?

How Does the Link Data meme solve the problem?

The meme simply provides a set of guidelines (best practices) for producing Web architecture friendly metadata. Meaning: when producing EAV/CR model based metadata, endow Subjects, their Attributes, and Attribute Values (optionally) with HTTP URIs. By doing so, a new level of Link Abstraction on the Web is possible i.e., "Data Item to Data Item" level links (aka hyperdata links). Even better, when you de-reference a RWO hyperdata link you end up with a negotiated representations of its metadata.

Conclusion

Linked Data is ultimately about an HTTP URI for each item in the Data Organization Hierarchy :-)

Related

1. History of how "Resource" became part of URI - historic account by TimBL
2. Linked Data Design Issues Document - TimBL's initial Linked Data Guide
3. Linked Data Rules Simplified - My attempt at simplifying the Linked Data Meme without SPARQL & RDF distraction
4. Linked Data & Identity - another related post
5. The Linked Data Meme's Value Proposition
6. My Del.icio.us hosted Bookmark Data Space for Identity Schemes
7. TimBL's Ted Talk re. "Raw Linked Data"
8. Resource Oriented Architecture
9. More Famous Than Simon Cowell .
   

Based on the prevalence of confusion re. the Linked Data meme, here are a few important points to remember about the World Wide Web.

1. Its part of a human innovation continuum just like the Internet (its host)
2. Its an HTTP based Network Cluster within the Internet (remember: Networks are about meshes of Nodes connected by Links)
3. Its underlying data model is that of a Network (we've had Network Data models for eons. EAV/CR is an example)
4. Links are facilitated via URIs
5. Until recently the granularity of Networking on the Web was scoped to Data Containers (documents) (due to prevalence of URL style links
6. The Linked Data meme adds Data Item (Datum) level granularity to World Wide Web networking via HTTP URIs
7. Data Items become Web Reference-able when you Identify/Name them using HTTP based URIs
8. An HTTP URI implicitly binds a Web Reference-able Data Item (Entity, Datum, Data Object, Resource) to its Web Accessible Metadata
9. Web Accessible Metadata resides within Data Containers (documents or information resources)
10. The representation of a Web Accessible Metadata container is negotiable
11. I am able to write and dispatch this blog post courtesy of the Web features listed above
12. You are able to explore the many dimensions to data exposed by this blog should you decide to explore the Linked Data mesh exposed by this post's HTTP URI (via its permalink permalink)

The HTTP URI is the secret sauce of the Web that's powerfully and unobtrusively reintroduced via the Linked Data meme, in classic back to the future style. This powerful feature of the Web possess a unique power, courtesy of its inherent duality: how it uniquely combines Data Item Identity (think keys in traditional DBMS parlance) with Data Access (e.g. access to negotiable representations of associated Data Item Metadata).

As you can see, I've made no mention of RDF or SPARQL, and I can still articulate the inherent value of the "Linked Data" dimension that the "Linked Data" meme adds to the World Wide Web.

As per usual this post is a live demonstration of Linked Data (dog-food style) :-)

Related

• Greg Boutin's post about Linked Data Brand Management
• Ian Davis' "Linked Data Brand" post
• Paul Miller's "Does Linked Data need RDF" post