Kingsley Idehen's Blog Data Space

What is SPARQL?

A declarative query language from the W3C for querying structured propositional data (in the form of 3-tuple [triples] or 4-tuple [quads] records) stored in a deductive database (colloquially referred to as triple or quad stores in Semantic Web and Linked Data parlance).

SPARQL is inherently platform independent. Like SQL, the query language and the backend database engine are distinct. Database clients capture SPARQL queries which are then passed on to compliant backend databases.

Why is it important?

Like SQL for relational databases, it provides a powerful mechanism for accessing and joining data across one or more data partitions (named graphs identified by IRIs). The aforementioned capability also enables the construction of sophisticated Views, Reports (HTML or those produced in native form by desktop productivity tools), and data streams for other services.

Unlike SQL, SPARQL includes result serialization formats and an HTTP based wire protocol. Thus, the ubiquity and sophistication of HTTP is integral to SPARQL i.e., client side applications (user agents) only need to be able to perform an HTTP GET against a URL en route to exploiting the power of SPARQL.

How do I use it, generally?

1. Locate a SPARQL endpoint (DBpedia, LOD Cloud Cache, Data.Gov, URIBurner, others), or;
2. Install a SPARQL compliant database server (quad or triple store) on your desktop, workgroup server, data center, or cloud (e.g., Amazon EC2 AMI)
3. Start the database server
4. Execute SPARQL Queries via the SPARQL endpoint.

How do I use SPARQL with Virtuoso?

What follows is a very simple guide for using SPARQL against your own instance of Virtuoso:

1. Software Download and Installation
2. Data Loading from Data Sources exposed at Network Addresses (e.g. HTTP URLs) using very simple methods
3. Actual SPARQL query execution via SPARQL endpoint.

Installation Steps

1. Download Virtuoso Open Source or Virtuoso Commercial Editions
2. Run installer (if using Commercial edition of Windows Open Source Edition, otherwise follow build guide)
3. Follow post-installation guide and verify installation by typing in the command: virtuoso -? (if this fails check you've followed installation and setup steps, then verify environment variables have been set)
4. Start the Virtuoso server using the command: virtuoso-start.sh
5. Verify you have a connection to the Virtuoso Server via the command: isql localhost (assuming you're using default DB settings) or the command: isql localhost:1112 (assuming demo database) or goto your browser and type in: http://<virtuoso-server-host-name>:[port]/conductor (e.g. http://localhost:8889/conductor for default DB or http://localhost:8890/conductor if using Demo DB)
6. Go to SPARQL endpoint which is typically -- http://<virtuoso-server-host-name>:[port]/sparql
7. Run a quick sample query (since the database always has system data in place): select distinct * where {?s ?p ?o} limit 50 .

Troubleshooting

1. Ensure environment settings are set and functional -- if using Mac OS X or Windows, so you don't have to worry about this, just start and stop your Virtuoso server using native OS services applets
2. If using the Open Source Edition, follow the getting started guide -- it covers PATH and startup directory location re. starting and stopping Virtuoso servers.
3. Sponging (HTTP GETs against external Data Sources) within SPARQL queries is disabled by default. You can enable this feature by assigning "SPARQL_SPONGE" privileges to user "SPARQL". Note, more sophisticated security exists via WebID based ACLs.

Data Loading Steps

1. Identify an RDF based structured data source of interest -- a file that contains 3-tuple / triples available at an address on a public or private HTTP based network
2. Determine the Address (URL) of the RDF data source
3. Go to your Virtuoso SPARQL endpoint and type in the following SPARQL query: DEFINE GET:SOFT "replace" SELECT DISTINCT * FROM <RDFDataSourceURL> WHERE {?s ?p ?o}
4. All the triples in the RDF resource (data source accessed via URL) will be loaded into the Virtuoso Quad Store (using RDF Data Source URL as the internal quad store Named Graph IRI) as part of the SPARQL query processing pipeline.

Note: the data source URL doesn't even have to be RDF based -- which is where the Virtuoso Sponger Middleware comes into play (download and install the VAD installer package first) since it delivers the following features to Virtuoso's SPARQL engine:

1. Transformation of data from non RDF data sources (file content, hypermedia resources, web services output etc..) into RDF based 3-tuples (triples)
2. Cache Invalidation Scheme Construction -- thus, subsequent queries (without the define get:soft "replace" pragma will not be required bar when you forcefully want to override cache).
3. If you have very large data sources like DBpedia etc. from CKAN, simply use our bulk loader .

SPARQL Endpoint Discovery

Public SPARQL endpoints are emerging at an ever increasing rate. Thus, we've setup up a DNS lookup service that provides access to a large number of SPARQL endpoints. Of course, this doesn't cover all existing endpoints, so if our endpoint is missing please ping me.

Here are a collection of commands for using DNS-SD to discover SPARQL endpoints:

1. dns-sd -B _sparql._tcp sparql.openlinksw.com -- browse for services instances
2. dns-sd -Z _sparql._tcp sparql.openlinksw.com -- output results in Zone File format

Related

1. Using HTTP from Ruby -- you can just make SPARQL Protocol URLs re. SPARQL
2. Using SPARQL Endpoints via Ruby -- Ruby example using DBpedia endpoint
3. Interactive SPARQL Query By Example (QBE) tool -- provides a graphical user interface (as is common in SQL realm re. query building against RDBMS engines) that works with any SPARQL endpoint
4. Other methods of loading RDF data into Virtuoso
5. Virtuoso Sponger -- architecture and how it turns a wide variety of non RDF data sources into SPARQL accessible data
6. Using OpenLink Data Explorer (ODE) to populate Virtuoso -- locate a resource of interest; click on a bookmarklet or use context menus (if using ODE extensions for Firefox, Safari, or Chrome); and you'll have SPARQL accessible data automatically inserted into your Virtuoso instance.
7. W3C's SPARQLing Data Access Ingenuity -- an older generic SPARQL introduction post
8. Collection of SPARQL Query Examples -- GoodRelations (Product Offers), FOAF (Profiles), SIOC (Data Spaces -- Blogs, Wikis, Bookmarks, Feed Collections, Photo Galleries, Briefcase/DropBox, AddressBook, Calendars, Discussion Forums)
9. Collection of Live SPARQL Queries against LOD Cloud Cache -- simple and advanced queries.

What is SPARQL?

A declarative query language from the W3C for querying structured propositional data (in the form of 3-tuple [triples] or 4-tuple [quads] records) stored in a deductive database (colloquially referred to as triple or quad stores in Semantic Web and Linked Data parlance).

SPARQL is inherently platform independent. Like SQL, the query language and the backend database engine are distinct. Database clients capture SPARQL queries which are then passed on to compliant backend databases.

Why is it important?

Like SQL for relational databases, it provides a powerful mechanism for accessing and joining data across one or more data partitions (named graphs identified by IRIs). The aforementioned capability also enables the construction of sophisticated Views, Reports (HTML or those produced in native form by desktop productivity tools), and data streams for other services.

Unlike SQL, SPARQL includes result serialization formats and an HTTP based wire protocol. Thus, the ubiquity and sophistication of HTTP is integral to SPARQL i.e., client side applications (user agents) only need to be able to perform an HTTP GET against a URL en route to exploiting the power of SPARQL.

How do I use it, generally?

1. Locate a SPARQL endpoint (DBpedia, LOD Cloud Cache, Data.Gov, URIBurner, others), or;
2. Install a SPARQL compliant database server (quad or triple store) on your desktop, workgroup server, data center, or cloud (e.g., Amazon EC2 AMI)
3. Start the database server
4. Execute SPARQL Queries via the SPARQL endpoint.

How do I use SPARQL with Virtuoso?

What follows is a very simple guide for using SPARQL against your own instance of Virtuoso:

1. Software Download and Installation
2. Data Loading from Data Sources exposed at Network Addresses (e.g. HTTP URLs) using very simple methods
3. Actual SPARQL query execution via SPARQL endpoint.

Installation Steps

1. Download Virtuoso Open Source or Virtuoso Commercial Editions
2. Run installer (if using Commercial edition of Windows Open Source Edition, otherwise follow build guide)
3. Follow post-installation guide and verify installation by typing in the command: virtuoso -? (if this fails check you've followed installation and setup steps, then verify environment variables have been set)
4. Start the Virtuoso server using the command: virtuoso-start.sh
5. Verify you have a connection to the Virtuoso Server via the command: isql localhost (assuming you're using default DB settings) or the command: isql localhost:1112 (assuming demo database) or goto your browser and type in: http://<virtuoso-server-host-name>:[port]/conductor (e.g. http://localhost:8889/conductor for default DB or http://localhost:8890/conductor if using Demo DB)
6. Go to SPARQL endpoint which is typically -- http://<virtuoso-server-host-name>:[port]/sparql
7. Run a quick sample query (since the database always has system data in place): select distinct * where {?s ?p ?o} limit 50 .

Troubleshooting

1. Ensure environment settings are set and functional -- if using Mac OS X or Windows, so you don't have to worry about this, just start and stop your Virtuoso server using native OS services applets
2. If using the Open Source Edition, follow the getting started guide -- it covers PATH and startup directory location re. starting and stopping Virtuoso servers.
3. Sponging (HTTP GETs against external Data Sources) within SPARQL queries is disabled by default. You can enable this feature by assigning "SPARQL_SPONGE" privileges to user "SPARQL". Note, more sophisticated security exists via WebID based ACLs.

Data Loading Steps

1. Identify an RDF based structured data source of interest -- a file that contains 3-tuple / triples available at an address on a public or private HTTP based network
2. Determine the Address (URL) of the RDF data source
3. Go to your Virtuoso SPARQL endpoint and type in the following SPARQL query: DEFINE GET:SOFT "replace" SELECT DISTINCT * FROM <RDFDataSourceURL> WHERE {?s ?p ?o}
4. All the triples in the RDF resource (data source accessed via URL) will be loaded into the Virtuoso Quad Store (using RDF Data Source URL as the internal quad store Named Graph IRI) as part of the SPARQL query processing pipeline.

Note: the data source URL doesn't even have to be RDF based -- which is where the Virtuoso Sponger Middleware comes into play (download and install the VAD installer package first) since it delivers the following features to Virtuoso's SPARQL engine:

1. Transformation of data from non RDF data sources (file content, hypermedia resources, web services output etc..) into RDF based 3-tuples (triples)
2. Cache Invalidation Scheme Construction -- thus, subsequent queries (without the define get:soft "replace" pragma will not be required bar when you forcefully want to override cache).
3. If you have very large data sources like DBpedia etc. from CKAN, simply use our bulk loader .

SPARQL Endpoint Discovery

Public SPARQL endpoints are emerging at an ever increasing rate. Thus, we've setup up a DNS lookup service that provides access to a large number of SPARQL endpoints. Of course, this doesn't cover all existing endpoints, so if our endpoint is missing please ping me.

Here are a collection of commands for using DNS-SD to discover SPARQL endpoints:

1. dns-sd -B _sparql._tcp sparql.openlinksw.com -- browse for services instances
2. dns-sd -Z _sparql._tcp sparql.openlinksw.com -- output results in Zone File format

Related

1. Using HTTP from Ruby -- you can just make SPARQL Protocol URLs re. SPARQL
2. Using SPARQL Endpoints via Ruby -- Ruby example using DBpedia endpoint
3. Interactive SPARQL Query By Example (QBE) tool -- provides a graphical user interface (as is common in SQL realm re. query building against RDBMS engines) that works with any SPARQL endpoint
4. Other methods of loading RDF data into Virtuoso
5. Virtuoso Sponger -- architecture and how it turns a wide variety of non RDF data sources into SPARQL accessible data
6. Using OpenLink Data Explorer (ODE) to populate Virtuoso -- locate a resource of interest; click on a bookmarklet or use context menus (if using ODE extensions for Firefox, Safari, or Chrome); and you'll have SPARQL accessible data automatically inserted into your Virtuoso instance.
7. W3C's SPARQLing Data Access Ingenuity -- an older generic SPARQL introduction post
8. Collection of SPARQL Query Examples -- GoodRelations (Product Offers), FOAF (Profiles), SIOC (Data Spaces -- Blogs, Wikis, Bookmarks, Feed Collections, Photo Galleries, Briefcase/DropBox, AddressBook, Calendars, Discussion Forums)
9. Collection of Live SPARQL Queries against LOD Cloud Cache -- simple and advanced queries.

There are some very powerful benefits that accrue from the use of HTTP based Hypermedia. 7 that come to mind immediately include:

1. Structured & Platform Independent Enterprise Data Virtualization -- concrete conceptual level access and provisioning of abstract domain entities such as Customers, Orders, Employees, Products, Countries, Competitors etc.
2. Distributed Application State (REST) -- application state transitions via links
3. Structured Data Representation (Linked Data) -- whole data data representation via links
4. Structured Identity (WebID) -- verifiable distributed identity
5. Structured Profiles (FOAF) -- platform independent profiles for people and organizations
6. Articulation of Structured Value Propositions (GoodRelations) -- Product & Service Offers, Business Entities, Locations, Business Hours, etc.
7. Structured Collaboration Spaces (SIOC) -- Blogs, Wikis, File Sharing, Discussion Forums, Aggregated Feeds, Statuses, Photo Galleries, Polls etc.

What is Linked Data?

The primary topic of a meme penned by TimBL in the form of a Design Issues Doc (note: this is how TimBL has shared his thoughts since the Beginning of the Web).

There are a number of dimensions to the meme, but its primary purpose is the reintroduction of the HTTP URI -- a vital component of the Web's core architecture.

What's Special about HTTP URIs?

They possess an intrinsic duality that combines persistent and unambiguous Data Identity with platform & representation format independent Data Access. Thus, you can use a string of characters that look like a contemporary Web URL to unambiguously achieve the following:

1. Identity or Name Anything of Interest
2. Describe Anything of Interest by associating the Description Subject's Identity with a constellation of Attribute and Value pairs (technically: an Entity-Attribute-Value or Subject-Predicate-Object graph)
3. Make the Description of Named Things of Interest discoverable on the Web by implicitly binding the aforementioned to Documents that hold their descriptions (technically: metadata documents or information resources)

What's the basic value proposition of the Linked Data meme?

Enabling more productive use of the Web by users and developers alike. All of which is achieved by tweaking the Web's Hyperlinking feature such that it now includes Hypertext and Hyperdata as link types.

Note: Hyperdata Linking is simply what an HTTP URI facilitates.

Examples problems solved by injecting Linked Data into the Web:

1. Federated Identity by enabling Individuals to unambiguously Identify themselves (Profiles++) courtesy of existing Internet and Web protocols (e.g., FOAF+SSL's WebIDs which combine Personal Identity with X.509 certificates and HTTPs based client side certification)
2. Security and Privacy challenge alleviation by delivering a mechanism for policy based data access that feeds off federated individual identity and social network (graph) traversal
3. Spam Busting via the above
.
4. Increasing the Serendipitous Discovery Quotient (SDQ) of Web accessible resources by embedding Rich Metadata into (X)HTML Documents e.g., structured descriptions of your "WishLists" and "OfferLists" via a common set of terms offered by vocabularies such as GoodRelations and SIOC
5. Coherent integration of disparate data across the Web and/or within the Enterprise via "Data Meshing" rather than "Data Mashing"
6. Moving beyond imprecise statistically driven "Keyword Search" (e.g. Page Rank) to "Precision Find" driven by typed link based Entity Rank plus Entity Type and Entity Property filters.

Conclusion

If all of the above still falls into the technical mumbo-jumbo realm, then simply consider Linked Data as delivering Open Data Access in granular form to Web accessible data -- that goes beyond data containers (documents or files).

The value proposition of Linked Data is inextricably linked to the value proposition of the World Wide Web. This is true, because the Linked Data meme is ultimately about an enhancement of the current Web; achieved by reintroducing its architectural essence -- in new context -- via a new level of link abstraction, courtesy of the Identity and Access duality of HTTP URIs.

As a result of Linked Data, you can now have Links on the Web for a Person, Document, Music, Consumer Electronics, Products & Services, Business Opening & Closing Hours, Personal "WishLists" and "OfferList", an Idea, etc.. in addition to links for Properties (Attributes & Values) of the aforementioned. Ultimately, all of these links will be indexed in a myriad of ways providing the substrate for the next major period of Internet & Web driven innovation, within our larger human-ingenuity driven innovation continuum.

Related

• Recipes for Describing Your Business and its Offerings using the GoodRelations Vocabulary / Schema
• Solving Real Problems with RDF based Linked Data
• Other Linked Data Posts from this Blog oriented Linked Data Space (goes back a few years!)
• Various practical Linked Data demo links from my Del.icio.us Bookmark oriented Data Space
• My personal WebID which is conduit to a Linked Data mesh covering vast variety of things I've opted to share with others via the Web (best viewed using a Linked Data aware User Agent like ODE).

What is Linked Data?

The primary topic of a meme penned by TimBL in the form of a Design Issues Doc (note: this is how TimBL has shared his thoughts since the Beginning of the Web).

There are a number of dimensions to the meme, but its primary purpose is the reintroduction of the HTTP URI -- a vital component of the Web's core architecture.

What's Special about HTTP URIs?

They possess an intrinsic duality that combines persistent and unambiguous Data Identity with platform & representation format independent Data Access. Thus, you can use a string of characters that look like a contemporary Web URL to unambiguously achieve the following:

1. Identity or Name Anything of Interest
2. Describe Anything of Interest by associating the Description Subject's Identity with a constellation of Attribute and Value pairs (technically: an Entity-Attribute-Value or Subject-Predicate-Object graph)
3. Make the Description of Named Things of Interest discoverable on the Web by implicitly binding the aforementioned to Documents that hold their descriptions (technically: metadata documents or information resources)

What's the basic value proposition of the Linked Data meme?

Enabling more productive use of the Web by users and developers alike. All of which is achieved by tweaking the Web's Hyperlinking feature such that it now includes Hypertext and Hyperdata as link types.

Note: Hyperdata Linking is simply what an HTTP URI facilitates.

Examples problems solved by injecting Linked Data into the Web:

1. Federated Identity by enabling Individuals to unambiguously Identify themselves (Profiles++) courtesy of existing Internet and Web protocols (e.g., FOAF+SSL's WebIDs which combine Personal Identity with X.509 certificates and HTTPs based client side certification)
2. Security and Privacy challenge alleviation by delivering a mechanism for policy based data access that feeds off federated individual identity and social network (graph) traversal
3. Spam Busting via the above
.
4. Increasing the Serendipitous Discovery Quotient (SDQ) of Web accessible resources by embedding Rich Metadata into (X)HTML Documents e.g., structured descriptions of your "WishLists" and "OfferLists" via a common set of terms offered by vocabularies such as GoodRelations and SIOC
5. Coherent integration of disparate data across the Web and/or within the Enterprise via "Data Meshing" rather than "Data Mashing"
6. Moving beyond imprecise statistically driven "Keyword Search" (e.g. Page Rank) to "Precision Find" driven by typed link based Entity Rank plus Entity Type and Entity Property filters.

Conclusion

If all of the above still falls into the technical mumbo-jumbo realm, then simply consider Linked Data as delivering Open Data Access in granular form to Web accessible data -- that goes beyond data containers (documents or files).

The value proposition of Linked Data is inextricably linked to the value proposition of the World Wide Web. This is true, because the Linked Data meme is ultimately about an enhancement of the current Web; achieved by reintroducing its architectural essence -- in new context -- via a new level of link abstraction, courtesy of the Identity and Access duality of HTTP URIs.

As a result of Linked Data, you can now have Links on the Web for a Person, Document, Music, Consumer Electronics, Products & Services, Business Opening & Closing Hours, Personal "WishLists" and "OfferList", an Idea, etc.. in addition to links for Properties (Attributes & Values) of the aforementioned. Ultimately, all of these links will be indexed in a myriad of ways providing the substrate for the next major period of Internet & Web driven innovation, within our larger human-ingenuity driven innovation continuum.

Related

• Recipes for Describing Your Business and its Offerings using the GoodRelations Vocabulary / Schema
• Solving Real Problems with RDF based Linked Data
• Other Linked Data Posts from this Blog oriented Linked Data Space (goes back a few years!)
• Various practical Linked Data demo links from my Del.icio.us Bookmark oriented Data Space
• My personal WebID which is conduit to a Linked Data mesh covering vast variety of things I've opted to share with others via the Web (best viewed using a Linked Data aware User Agent like ODE).

The first salvo of what we've been hinting about re. server side faceted browsing over Unlimited Data within configurable Interactive Time-frames is now available for experimentation at: http://b3s.openlinksw.com/fct/facet.vsp.

Simple example / demo:

Enter search pattern: Microsoft

You will get the usual result from a full text pattern search i.e., hits and text excerpts with matching patterns in boldface. This first step is akin to throwing your net out to sea while fishing.

Now you have your catch, what next? Basically, this is where traditional text search value ends since regex or xpath/xquery offer little when the structure of literal text is the key to filtering or categorization based analysis of real-world entities. Naturally, this is where the value of structured querying of linked data starts, as you seek to use entity descriptions (combination of attribute and relationship properties) to "Find relevant things".

Continuing with the demo.

Click on "Properties" link within the Navigation section of the browser page which results in a distillation and aggregation of the properties of the entities associated with the search results. Then use the "Next" link to page through the properties until to find the properties that best match what you seek. Note, this particular step is akin to using the properties of the catch (using fishing analogy) for query filtering, with each subsequent property link click narrowing your selection further.

Using property based filtering is just one perspective on the data corpus associated with the text search pattern; thus, you can alter perspectives by clicking on the "Class" link so that you can filter you search results by entity type. Of course, in a number of scenarios you would use a combination of entity types and entity properties filters to locate the entities of interest to you.

A Few Notes about this demo instance of Virtuoso:

• Lookup Data Size (Local Linked Data Corpus): 2 Billion+ Triples (entity-attribute-value tuples)
• This is a *temporary* teaser / precursor to the LOD (Linking Open Data Cloud) variant of our Linked Data driven "Search" & "Find" service; we decided to implement this functionality prior to commissioning a larger and more up to date instance based on the entire LOD Cloud
• The browser is simply using a Virtuoso PL function that also exists in Web Service form for loose binding by 3rd parties that have a UI orientation and focus (our UI is deliberately bare boned).
• The properties and entity types (classes) links expose formal definitions and dictionary provenance information materialized in an HTML page (of course your browser or any other HTTP user agent can negotiation alternative representations of this descriptive information)
• UMBEL based inference rules are enabled, giving you a live and simple demonstration of the virtues of Linked Data Dictionaries for example: click on the description link of any property or class from the foaf (friend-of-a-friend vocabulary), sioc (semantically-interlinked-online-communities ontology), mo (music ontology), bibo (bibliographic data ontology) namespaces to see how the data between these lower level vocabularies or ontologies are meshed with OpenCyc's upper level ontology.

Related

• Faceted Search: Unlimited Data in Interactive Time
• Virtuoso Anytime: No Query Is Too Complex

What is it?

A pre-installed edition of Virtuoso for Amazon's EC2 Cloud platform.

What does it offer?

1. Low cost entry point to a game-changing Web 3.0+ (and beyond) platform that combines SQL, RDF, XML, and Web Services functionality
2. Flexible variable cost model (courtesy of EC2 DevPay) tightly bound to revenue generated by your services
3. Delivers federated and/or centralized model flexibility for you SaaS based solutions
4. Simple entry point for developing and deploying sophisticated database driven applications (SQL or RDF Linked Data Web oriented)
5. Complete framework for exploiting OpenID, OAuth (including Role enhancements) that simplifies exploitation of these vital Identity and Data Access technologies
6. Easily implement RDF Linked Data based Mail, Blogging, Wikis, Bookmarks, Calendaring, Discussion Forums, Tagging, Social-Networking as Data Space (data containers) features of your application or service offering
7. Instant alleviation of challenges (e.g. service costs and agility) associated with Data Portability and Open Data Access across Web 2.0 data silos
8. LDAP integration for Intranet / Extranet style applications.

From the DBMS engine perspective it provides you with one or more pre-configured instances of Virtuoso that enable immediate exploitation of the following services:

1. RDF Database (a Quad Store with SPARQL & SPARUL Language & Protocol support)
2. SQL Database (with ODBC, JDBC, OLE-DB, ADO.NET, and XMLA driver access)
3. XML Database (XML Schema, XQuery/Xpath, XSLT, Full Text Indexing)
4. Full Text Indexing.

From a Middleware perspective it provides:

1. RDF Views (Wrappers / Semantic Covers) over SQL, XML, and other data sources accessible via SOAP or REST style Web Services
2. Sponger Service for converting non RDF information resources into RDF Linked Data "on the fly" via a large collection of pre-installed RDFizer Cartridges.

From the Web Server Platform perspective it provides an alternative to LAMP stack components such as MySQL and Apace by offering

1. HTTP Web Server
2. WebDAV Server
3. Web Application Server (includes PHP runtime hosting)
4. SOAP or REST style Web Services Deployment
5. RDF Linked Data Deployment
6. SPARQL (SPARQL Query Language) and SPARUL (SPARQL Update Language) endpoints
7. Virtuoso Hosted PHP packages for MediaWiki, Drupal, Wordpress, and phpBB3 (just install the relevant Virtuoso Distro. Package).

From the general System Administrator's perspective it provides:

1. Online Backups (Backup Set dispatched to S3 buckets, FTP, or HTTP/WebDAV server locations)
2. Synchronized Incremental Backups to Backup Set locations
3. Backup Restore from Backup Set location (without exiting to EC2 shell).

Higher level user oriented offerings include:

1. OpenLink Data Explorer front-end for exploring the burgeoning Linked Data Web
2. Ajax based SPARQL Query Builder (iSPARQL) that enables SPARQL Query construction by Example
3. Ajax based SQL Query Builder (QBE) that enables SQL Query construction by Example.

For Web 2.0 / 3.0 users, developers, and entrepreneurs it offers it includes Distributed Collaboration Tools & Social Media realm functionality courtesy of ODS that includes:

1. Point of presence on the Linked Data Web that meshes your Identity and your Data via URIs
2. System generated Social Network Profile & Contact Data via FOAF?
3. System generated SIOC (Semantically Interconnected Online Community) Data Space (that includes a Social Graph) exposing all your Web data in RDF Linked Data form
4. System generated OpenID and automatic integration with FOAF
5. Transparent Data Integration across Facebook, Digg, LinkedIn, FriendFeed, Twitter, and any other Web 2.0 data space equipped with RSS / Atom support and/or REST style Web Services
6. In-built support for SyncML which enables data synchronization with Mobile Phones.

How Do I Get Going with It?

• Standard Installation Guide
• Personal or Service Specific DBpedia Installation Guide

Unfortunately, I could only spend 4 days at the recent WWW2008 event in Beijing (I departed the morning following the Linked Data Workshop), so I couldn't take my slot on the "Commercializing the Semantic Web panel" etc.. Anyway, thanks to the Web I can still inject my points of view in the broad Web based discourse. Well so I hoped, when I attempted to post a comment to Paul Miller's ZDNet domain hosted blog thread titled: Commercialising the Semantic Web.

Unfortunately, the cost of completing ZDNet's unwieldy signup process simply exceeded the benefits of dropping my comments in their particular space :-( Thus, I'll settle for a trackback ping instead.

What follows is the cut and paste of my intended comment contributions to Paul's post.

Paul,

As discussed earlier this week during our podcast session, commercialization of Semantic Web technology shouldn't be a mercurial matter at this stage in the game :-) It's all about looking at how it provides value :-)

From the Linked Data angle, the ability to produce, dispatch, and exploit "Context" across an array of "Perspectives" from a plethora of disparate data sources on the Web and/or behind corporate firewalls, offers immense commercial value.

Yahoo's Searchmonkey effort will certainly bring clarity to some of the points I made during the podcast re. the role of URIs as "value consumption tickets" (Data Services are exposed via URIs). There has to be a trigger (in user space) that compels Web users to seek broader, or simply varied, perspectives as a response to data encountered on the Web. Yahoo! is about to put this light on in a big way (imho).

The "self annotating" nature of the Web is what ultimately drives the manifestation of the long awaited Semantic Web. I believe I postulated about "Self Annotation & the Semantic Web" in a number of prior posts which, by the way, should be DataRSS compatible right now due to Yahoo's support of OpenSearch Data Providers (which this Blog Space has been for eons).

Today, have many communities adding strucuture to the Web (via their respective tools of preference) without explicitly realizing what they are contributing. Every RSS/Atom feed, Tag, Weblog, Shared Bookmark, Wikiword, Microformat, Microformat++ (eRDF or RDFa), GRDDL stylesheet, and RDFizer etc.. is a piece of structured data.

Finally, the different communities are all finding ways to work together (thank heavens!) and the results are going to be cataclysmic when it all plays out :-)

Data, Structure, and Extraction are the keys to the Semantic Life! First you get the Data in a container (information resource), and then you add Structure to the information resource (RSS, Atom, microformats, RDFa, eRDF, SIOC, FOAF, etc.), once you have Structure RDFization (i.e. transformation to Linked Data) is a synch thanks to RDF Middleware (as per earlier RDF middleware posts).

Wordpress is a Weblog platform comprised of the following:

1. User Interface - PHP
2. Application Logic - PHP
3. Data Storage (SQL RDBMS) - MySQL via PHP-MySQL
4. Application Server - Apache

In the form above (the norm), Wordpress data can be injected into the Linked Data Web via RDFization middleware such as theVirtuoso Sponger (built into all Virtuoso instances) and Triplr. The downside of this approach is that the blog owner doesn't necessary possess full control over their contributions to the emerging Giant Global Graph or Linked Data.

Another route to Linked Data exposure is via Virtuoso's Metaschema Language for producing RDF Views over ODBC/JDBC accessible Data Sources, that enables the following setup:

1. User Interface - PHP
2. Application Logic - PHP
3. Data Storage (SQL RDBMS) - MySQL via the PHP-MySQL data access interface
4. Virtual Database linkage of MySQL Tables into Virtuoso
5. RDF View generated over the Virtual SQL Tables
6. Application Server - Virtuoso which provides Linked Data Deployment such that RDF Linked Data is exposed when requested by Web User Agents.

Alternatively, you can also exploit Virtuoso as the SQL DBMS, RDF DBMS, Application Server, and Linked Data Deployment platform:

1. User Interface - PHP
2. Application Logic - PHP
3. Data Storage (SQL RDBMS) - Virtuoso via PHP-ODBC data access interface (* ODBC is Virtuoso's native SQL CLI/API *)
4. RDF View generated over the Native SQL Tables
5. Application Server - Virtuoso which provides Linked Data Deployment such that RDF Linked Data is exposed when requested by Web User Agents (e.g. OpenLink RDF Browser, Zitgist Data Viewer, DISCO Hyperdata Browser, and Tabulator).

Benefits?

• Each user account gets a proper Linked Data URI (ID) that can me meshed/smushed with other IDs (so you add data from this new blog space to other linked data sources associated with you other URIs/IDs)
• Each post gets a proper URI All data is now query-able via SPARQL Discoverability increases exponentially (without drop in relevance in either direction i.e. discovering or being discovered)

How Do I map the WordPress SQL Schema to RDF using Virtuoso?

• Determine the RDF Schema or Ontologies that define the Classes for which you will be producing instance data (e.g. SIOC and FOAF)
• Declare URI/IRI generator functions (*special Virtuoso functions*)
• Use SPARQL Graph patterns to apply URI/IRI generator functions to Tables, Views, Table Values mode Stored Procedures, Query Resultsets as part of RDBMS to RDF mapping

Read the Meta Schema Language guide or simply apply our "WordPress SQL Schema to RDF" script to your Virtuoso hosted instance. Of course, there are other mappings that cover other PHP applications deployed via Virtuoso:

• phpBB3 SQL Schema to RDF
• Drupal SQL Schema to RDF
• MediaWiki SQL Schema to RDF

Live Demos?

• Virtuoso Hosting phpBB3 (example User URI)
• Virtuoso Hosting Drupal (example User URI)
• Virtuoso Hosting MediaWiki (example User URI)

I just listen to, and very much enjoyed (lots of chuckling) Dave Beckett's podcast interview on the Talis podcast network. Clearly Dave has a bent for funny project names etc.. He also introduced "Inter-Webs" (Web Data Spaces in my parlance) towards the end of the interview.

Trent Adams, Steve Greenberg, and I, also had a podcast chat about Web Data Portability and Accessibility (Linked Data). I also remixed Jon Breslin's "Data Portability & Me" presentation to produce: "Data Accessibility & Me".

The podcasts interviews and presentations provide contributions to the broadening discourse about Open Data Access / Connectivity on the Web.