The Enduring Myth of the SPARQL Endpoint

It surprises me that the Semantic Technology industry still talks with great frequency about the ‘SPARQL Endpoint’ (it’s come up a few times already at SemTech 2013). At best, a SPARQL Endpoint is useful as an ephemeral, unstable method to share your data. At worst, it is wasting the time and energy of providers and consumers of SPARQL endpoints due to the incompatible outcomes of scale and availability.

But before I explain this position, let me outline my understanding of what a SPARQL Endpoint is:

A technical definition:

  • An HTTP URL which accepts a SPARQL query and returns the results
  • Can return a variety of serialisations: Turtle, RDF XML etc

The intention of SPARQL endpoints

  • Give other people and organisations access to your data in a very flexible way
  • Eventually realise the potential of federated SPARQL whereby several SPARQL Endpoints are combined to allow complex queries to be run across a number of datasets
  • They are open for use by a large and varied audience

But what can SPARQL endpoints be used for? They are brilliant for hackdays, prototypes, experiments, toy projects etc. But I don’t think anything ‘real’ could ever be built using one.

There seems to be a cultural acceptance that SPARQL endpoints can be intermittently available, subject to rudimentary DOS attacks and have extremely long response times. This is no foundation for mass adoption of linked data technologies, and it certainly cannot form the fabric of web-based data infrastructure.

I want linked data to gain mainstream popularity. It is a great language for expressing meaningful data and fostering collaboration with data. But to succeed, people need to be able to confidently consume linked data to build apps and services reliably. To build a business on linked data means you need a source of regularly updated and highly available data. This takes investment, by the provider of the data, in highly available, secure and scalable APIs. This is already happening of course, but the SPARQL Endpoint endures.

How do SPARQL endpoints perform?

I thought I’d put my criticisms of SPARQL endpoints to the test, so I tried a few, and here’s what happened…

Note: the queries I have tried are intended to represent an intentional or accidental, rudimentary DOS attack. This is the kind of attack that a robust, open endpoint should be able to protect itself against.

Firstly, only 52% of known SPARQL endpoints were available on I don’t know how representative that is, but it’s not a good start.

Next, I tried some of the available ones, you’ll have to trust me that I picked these four at random…

(Apologies to the providers of these endpoints, I am not singling you out, I am making a general point).

It took 30 seconds for the query editor to load. I ran the query the suggested query, and it hung for around 2 minutes, and then I got

Virtuoso S1T00 Error SR171: Transaction timed out

It took 30 seconds for the query editor to load. The suggested query ran quickly. I changed it to:

SELECT * WHERE { ?s ?p ?o . }

The results came back quickly, but then the data stopped being streamed back and hung for over 5 minutes, before I stopped waiting.

I then tried:

SELECT * WHERE { ?s ?p ?o . ?a ?b ?c . ?e ?f ?g . }

And got:

Virtuoso 42000 Error The estimated execution time -1308622848 (sec) exceeds the limit of 1000 (sec).

That’s an ugly error message, but at least there is a protection mechanism in-place.

It worked fine for some friendly queries, but then I tried:

SELECT * WHERE { ?s ?p ?o . ?a ?b ?c . ?e ?f ?g . }

and got:

Error: Connection timed out after 30 seconds in ARC2_Reader missing stream in "getFormat" via ARC2_Reader missing stream in "readStream"

I ran this basic query when I started writing the blogpost:

SELECT * WHERE { ?s ?p ?o . }

It is still failing to load around 10 minutes later.

Update: it was pointed out that the above are all research projects, so I tried and too, and got similar results – connection reset and 60 second+ response times.

The incompatible aims of scale and availability

Whilst “premature optimisation is the root of all evil”, it would be reckless to build a software system that was fundamentally incapable of scaling. A SPARQL Endpoint is just such a system.

SPARQL is a rich and expressive querying language, and like most querying languages, it is straightforward to write highly inefficient queries. Various SPARQL engines have mechanisms for protecting against inefficient queries: timeouts, limits to the number of triples returned, but most of these are blunt tools. Applying them gives the user a highly inconsistent experience. A SPARQL endpoint can also take no advantage of returning previously computed results based on knowledge about the data update frequency, or how out-of-date it is acceptable for the data to be.

So if a SPARQL endpoint is ever intended to be successful, and have many (1000+) frequent consumers of data, and remain open to any SPARQL query, it is my opinion that it would be impossible to also have acceptable response times (< 500ms) and reasonable availability (99.99%).

There is a reason there are no ‘SQL Endpoints’.

What are the alternatives?

The main alternative to me is obvious: Open RESTful APIs:

  • Open APIs can provide access to data in only the ways that will scale
  • Open APIs can make generous use of caches to reduce the number of queries being run
  • Open APIs can make use of creative additional ways to combine data from various sources, and hide this complexity from its users
  • Open APIs can continue to provide legacy data structures even if the underlying data has changed. This is important to maintaining APIs over long periods of time.

A second alternative is data dumps. These have limited use, because the data is often not useful until is has undergone processing or ingest into a SPARQL engine.

A third alternative is a self-provisioned SPARQL endpoint. Cloud technologies are making this approach more viable. It would allow a potential data consumer to ‘spin-up’ their own, personal SPARQL endpoint which would be pre-loaded with a periodically updated RDF data dump. This approach allows the provider to massively reduce the cost of supplying and maintaining the endpoint, and a consumer takes responsibility for the stability of their own SPARQL endpoint, without affecting any other consumers.

Ontologies in software: A conflict of interest?

I build software using semantic technologies. As such, ontologies are at the heart of what I do. For me, ontologies serve two very different roles, and these can often be in conflict. The two roles that I observe are as follows:

  1. Collaborative ontologies: Initially for domain modelling discussion, with the eventual aim of standardisation, either across or within organisations
  2. Embedded ontologies: Form an integral part of a working software system, modelling both the domain, and other application-specific data structures

Below I have described some further characteristics I have observed of these two roles:

Collaborative ontologies

  • An attempt to model an entire domain
  • Discussed by a wide community, who can present edge-cases and suggested changes
  • Change slowly (or never) by necessity, allowing people to publish data without the model becoming out-of-date
  • A tendency to model more so a majority of potential users’ requirements are met
  • Designed in advance of use
  • Aspires to be a truthful representation of the domain
  • An academic approach, whereby ontologies are published by authors
  • Equate to the ‘vision’ in agile software development terms

Embedded ontologies

  • An attempt to model parts of a domain used with a software system
  • Discussed by the community using the system
  • Change frequently, in response to the software design and the requirements of the system
  • An MVO (minimal viable ontology), to match how the ontologies are used in the system
  • Designed in response to requirements of the system
  • Required to balance the demands of a software system, and the desire to be a truthful representation of the domain. For example, performance considerations
  • An engineering approach, whereby ontologies are emergent
  • Equate to the actual software in agile software development terms

In short, I would describe the most fundamental difference as follows:

  • Collaborative ontologies: designed to change as little as possible
  • Embedded ontologies: designed to be to changed as easily as possible

The idea of an embedded ontology might be less familiar, as they are certainly less common. For me, they are simply the data model of a software system, expressed using linked data. This is obviously very much the case where linked data technologies are used, such as Triple Stores or RDF, to power the software.

I think both roles are important and necessary, however the rest of this post is focused on demonstrating the need for embedded ontologies, because these are less widely used and understood.

Relationship between collaborative & embedded ontologies

Most commonly, the distinction is not made collaborative and embedded ontologies, and the conflict takes place over a single ontology. I believe a more productive approach would be to separate the embedded and collaborative ontologies, and to allow each to develop in response to it’s own pressures. The differences that emerge can inform a dialogue between abstract modelling and delivery software.

The use-case I will now present should demonstrate how this separate embedded ontology works in practice.

Embedded ontology use-case: A Sport app

To explain how embedded ontologies work, I will give an example: a software application in the sport domain.

First, to contrast, a collaborative sport ontology would be an attempt to model the domain of sport, comprising teams, sportspeople, disciplines, and perhaps sponsorship, venues, and fixtures.

Let us now assume I wanted to build a software system based around the domain of sport. My first question would be ‘what’s the MVP (minimal viable product)?’. I would hope to get an answer such as “A mobile app showing a list of teams in each English football division”. How would I go about building this? Well, good domain-driven-design would lead me to use a good model and language to describe the parts of my software. I might have a /teams endpoint in an API. I might build a TeamList component in the Javascript code. What I would not do, is attempt to model sportspeople, disciplines, sponsorship, venues and fixtures. I don’t need these yet. What I need is a Minimal Viable Ontology.

This used to be a problem, where SQL database schema were designed in advance of writing any software. Then agile came along, along with Lean, TDD, BDD and so on. These approaches showed that these upfront design approaches usually resulted in the wrong architectural design, badly modelled data structures, features nobody wants and unnecessary complexity.

So, whilst I believe that collaborative ontologies serve an important role in fostering collaboration in linked data, I also believe that we need strategies to make these approaches compatible with embedded ontologies, and therefore software engineering best practice.

Using ontologies in software

I want to now talk about some approaches to building embedded ontologies, used within software. As you will see, these contrast in a number of significant ways with approaches to building ontologies within academic communities.

These approaches are not intended to supercede collaborative ontologies, but instead, help reduce the friction between collaborative and embedded ontologies. And, therefore, between those building software using linked data, and those building models in academia.

Separate embedded & collaborative ontologies

A good first step is to get a distinct understanding of what constitutes the embedded ontology in-use when compared with the collaborative ontology. This could be done in a number of ways:

  • No collaborative ontology until necessary: For me, this is the most important. I believe the preemptive publishing of collaborative ontologies can be counter-productive; if an ontology can remain private and embedded until the software has been proven to work, then it will be a higher-quality representation of the domain. This adheres to the software principle that the best design is the design that emerges in response to iterative requirements. From my software perspective, a published and shared ontology is like an Open API, whilst the benefits of sharing can be huge, you have potentially also lost one of the most important capabilities in building software: the ability to change frequently and easily.
  • Modularisation: I would suggest this as a key second step. Breaking ontologies down into smaller parts, particularly in response to growth. This will reduce the rate at which these modules change individually when compared with the whole. This technique can avoid the necessity for mappings (see below).
  • Mapped: Where a clear mapping between the ontologies is expressed (perhaps using semantic equivalence). This comes with the overhead of maintaining two ontologies and a mapping.

Modularised ontologies

One issue with ontologies is the common practice of attempting to model a single ‘domain’ with a single ontology. A selling point for this is the ability to “work within a single namespace”. I think that this practice is counter-productive, particularly with regards to aligning collaborative and embedded ontologies.

If a modularised approach is taken, a number of options become available. If an ontology can be broken into a number of parts, with references between them, the parts can be assigned metadata to indicate the following:

  • Stability: For example, it should be possible to add a new ‘ontology module’ which is entirely experimental. In sport, this could be a ‘sponsorship’ module which is up for discussion in the community, but forms no part of the working software.
  • Module version: By separately versioning ontology modules, stability can be achieved within some modules, whilst others regularly change.
  • Equivalence/alternatives: Alternative or equivalent modules could exist if different perspectives on the same domain exist.

Dependency management between modules

Whilst I am aware that work has been done in this area, it has not matured to the level seen with library dependency management in software (e.g. Maven, Ivy etc). It is straightforward to build dependency graphs between ontologies, but the more subtle version-specific dependency management is not readily available. More efficient tooling, and a consensus on meta-ontologies in this area would lower the bar for fine-grained modularisation of ontologies.


It should now be clear that embedded ontologies are a by-product of software delivery. This in my view, is exactly how it should work (as long as the embedded ontologies are curated and crafted as the software grows, using the same principles applied to collaborative ontology design). Using this approach, I would suggest that a higher-quality, more robust ontology may emerge. It will be an ontology that has been road-tested by the necessity to deliver software that serves a particular audience. Perhaps this particular audience will skew the perspective of the ontology, but any model is, after all, just a perspective.

Once this ontology has undergone this growth, and subsequent road-testing, this is where the dialogue can get really interesting between a fit-for-purpose embedded ontology, and a collaborative ontology. Both will have much to offer, but I feel the benefits will flow in both directions.

I would be keen to hear if any of this reflects your own experiences, and, in particular, to get the perspective of individuals working exclusively on collaborative ontologies.

Introducing Tripliser

I recently had to solve the problem of how to take XML, in a predefined format, and create RDF representing the semantics of the data. I began using XSLT, but gradually the edge cases to handle inconsistencies in the input XML caused the XLST to become verbose and incomprehensible (being a mix of syntax handling and business logic). Errors were hard to diagnose and failures were not effectively recovered from. I decided to write a library to help me with this problem, called Tripliser…

>> Homepage  |  >> GitHub

Tripliser is a Java library and command-line tool for creating triple graphs, and RDF serialisations, from XML source data. It is particularly suitable for data exhibiting any of the following characteristics:

  • Messy – missing data, badly formatted data, changeable structure
  • Bulky – large volumes of data
  • Volatile – ongoing changes to data and structure, e.g. feeds

Other non-RDF source data may be supported in future such as CSV and SQL databases.

It is designed as an alternative to XSLT conversion, providing the following advantages:

  • Easy-to-read mapping format – concisely describing each mapping
  • Robust – error or partial failure tolerant
  • Detailed reporting – comprehensive feedback on the successes and failures of the conversion process
  • Extensible – custom functions, flexible API
  • Efficient – facilities for processing data in large volumes with minimal memory usage

XML files are read in, and XPath is used to extract values which can be inserted into a triple graph. The graph can be serialised in various RDF formats and is accompanied by meta-data and a property-by-property report to indicate how successful or unsuccessful the mapping process was.

Data flow in Tripliser

Here’s what a typical mapping format looks like…

<?xml version="1.0" encoding="UTF-8"?>
<rdf-mapping xmlns="" strict="false">
		<constant name="objectsUri" value="" />
		<namespace prefix="xsd" url="" />
		<namespace prefix="rdfs" url="" />
		<namespace prefix="dc" url="" />
		<namespace prefix="universe" url="" />
	<graph query="//universe-objects" name="universe-objects" comment="A graph for objects in the universe">
		<resource query="stars/star">
			<about prepend="${objectsUri}" append="#star" query="@id" />
				<property name="rdf:type" resource="true" value="universe:Star"/>
				<property name="dc:title" query="name" />
				<property name="universe:id" query="@id" />
				<property name="universe:spectralClass" query="spectralClass" />
		<resource query="planets/planet">
			<about prepend="${objectsUri}" append="#planet" query="@id" />
				<property name="rdf:type" resource="true" value="universe:Planet"/>
				<property name="dc:title" query="name" />
				<property name="universe:id" query="@id" />
				<property name="universe:adjective" query="adjective" />
				<property name="universe:numberOfSatellites" dataType="xsd:int" query="satellites" />

Go to the Homepage or to GitHub to find out more.

Aggregate! Aggregate! Aggregate! Using linked data to make websites more interesting.

The way content is modelled is pivotal to the overall, technical design of a website, in particular, the choice of content management system.

Many bespoke CMSs are built using a relational database as the content repository. Most generic, off-the-shelf CMSs now use NoSQL technology, specifically document stores, to provide the necessary flexibility of schema design. Aside from these two, key content storage technologies, a small number of CMSs, usually hosted services, make direct use of triple stores: content repositories that use linked data technology.

Each type of content repository has advantages and disadvantages:

  • Relational database: Supported by a wealth of mature technology and offers relational integrity. However, schema change can be slow and complex.
  • Document store: Provides schema flexibility, horizontal scalability and content ‘atomicity’ (the ability to handle content in discreet chunks, for versioning or workflow). However, referential integrity is hard to maintain and queries across different sets of data are harder to achieve.
  • Triple store: Provides logical inference and a fundamental ability to link concepts across domains. However, the technology is less mature.

I think there are two diametric content modelling approaches; most approaches will fit somewhere, on a sliding scale, between the two extremes. Each exhibits properties that make some things simple, and some things complex:

  • Atomic content: Content is modelled as a disconnected set of content ‘atoms’ which have no inter-relationships. Within each atom is a nugget of rich, hierarchically structured content. The following content management features are easier to implement if content is modelled atomically:
    • Versioning
    • Publishing
    • Workflow
    • Access control
    • Schema flexibility
  • Relational content: Content is modelled as a graph of small content fragments with a rich and varied set of relationships between the fragments. The following content management features are easier to implement if content is modelled relationally:
    • Logical inference
    • Relationship validation/integrity
    • Schema standardisation

Atomic vs. Relational

The following diagram indicates where the different content repositories fit on this scale:

Atomic to Relational

For the scenario outlined below, two ‘advanced’ features have been used to highlight tension between atomic and related content models. The versioning feature causes problems for more related models, and the tag relationships feature causes problems for more atomic models.

The online film shop

A use-case to explain why linked data technology can help to build rich, content-driven websites

An online film shop needs to be built. It will provide compellingly presented information about films, and allow users to purchase films and watch them in the browser.

Here are some (very) high-level user-stories:

“As a user I want to shop for films at,, and watch them online after purchase”

“As an editor I need to manage the list of available films, providing enough information to support a compelling user experience.

An analysis indicates that the following content is needed:

  • Films
    • The digital movie asset
    • Description (rich-text with screen-grabs & clips)
    • People involved (actors, directors)
    • Tags (horror, comedy, prize-winning, etc…)

Some basic functionality has been developed already…

  • A page for each film
    • A list of participants, ordered by role
  • A page for each person
    • A list of the films they have participated in
  • An A-Z film list
  • A shopping basket and check-out form
  • A login-protected viewing page for each film

But now, the following advanced functionality is needed…

  • Feature 1, Versioned film information: This supports several features including:
    • Rolling-back content if mistakes are made
    • Allowing editors to set up new versions for publishing at a later date, whilst keeping the current version live
    • Providing a workflow, allowing approval of a film’s information, whilst continuing to edit a draft version
  • Feature 2, Tagged content: Tags are used to express domain concepts, these concepts have different types of relationships which will support:
    • Rich navigation, e.g. “This director also worked with…”
    • Automated, intelligent aggregation pages, e.g. An aggregation page for ‘comedy’ films, including all sub-genres like ‘slapstick’

Using a relational database

Using a relational database, a good place to start is by modelling the entities and relationships…

Entities: Film, Person, Role, Term
Relationships: Participant [Person, Role, Film], Tag [Term, Film]

Assuming that the basic feature set has been implemented, the advanced features are now required:

Advanced feature 1: Versioned films

Trying to version data in a relational database is not easy: often a ‘parent’ entity is needed for all foreign keys to reference, with ‘child’ entities for the different versions, each annotated with version information. Handling versioning for more than one entity and the schema starts getting complicated.

Advanced feature 2: Tag relationships

When tagging content, one starts by trying to come up with a single, simple list of terms that collectively describe the concepts in the domain. Quickly, that starts to break down. Firstly a 2-level hierarchy is identified; for example, putting each tag into a category: ‘Horror: Genre’, ‘Paris: Location’, and so on. Next, a multi-level hierarchy is discovered: ‘Montmatre: Paris: France: Europe’. Finally, this breaks down as concepts start to overlap and repeat. Here are a few examples: Basque Country (France & Spain), Arnold Schwarzenegger (Actor, Politician, Businessman), things get even trickier with Bruce Dickinson. Basically, real-world concepts do not fall easily into a taxonomy, and instead form a graph.

Getting the relationships right is important for the film shop. If an editor tags a film as ‘slapstick’, they would expect it to appear on the ‘comedy’ aggregation page. Likewise, if an editor tagged a film as ‘Cannes Palme d’Or 2010 Winner’ they would expect it to be shown on the ‘2010 Cannes Nominees’ aggregation page. These two ‘inferences’ use different relationships, but achieve the correct result for the user.

With a relational database 2-level hierarchies introduce complexity, multi-level hierarchies more so, and once the schema is used to model a graph, the benefits of a relational schema have been lost.

Relational database: Advanced features summary

  • Feature 1 is complex
  • Feature 2 is complex

Using a document store

NoSQL storage solutions are currently very popular, offering massive scalability and a chance to break-free from strict schema and normal-forms. From a document store perspective. the original entities look very different, compiled into a single structure:

<name>Pulp Fiction</name>
<description>The lives of two mob hit men...</description>
<participant role="director">Quentin Tarantino</participant>
<participant role="actor">Bruce Willis</participant>
<tag>Palme d'Or Winner 1994 Cannes Film Festival</tag>
<tag>Black comedy</tag>

The lists of available tags or participants may be handled using separate documents, or possibly ‘dictionaries’ of allowed terms.

Assuming again that the basic feature set has been implemented, the advanced features are now required:

Advanced feature 1: Versioned films

Keep multiple copies the film document, each marked to indicate the different versions. All the references are inside the document, so even the tags and roles are versioned too. Many off-the-shelf CMS products offer built-in document versioning based on this process.

Advanced feature 2: Tag relationships

This is even worse now. Before, with a relational database, we could keep adding more structures, using SQL queries to extract the hierarchical relationships. It was only when moving to graphs that relationships became too complex to manage. Without SQL to query a structure of tags, bespoke plug-in code is required to extract meaning from a bespoke hierarchy of terms. This is now too complicated to build anything simple and reusable.

Document store: Advanced features summary

  • Feature 1 is simple
  • Feature 2 is very complex

Using a triple store

In triple stores, all the content is reduced to triples. So all our content would look something like this… -> dc:title -> “The Wicker Man”

Assuming again that the basic feature set has been implemented, the advanced features are now required:

Advanced feature 1: Versioned films

Versioning triples would not be straightforward. A complex overlaid system of ‘named graphs’ could be used to separate out content items into different versions, but this would be more complex than for relational databases.

Advanced feature 2: Tag relationships

This is simple! SPARQL queries will offer us all the inferred or aggregated content that we require, as long as the ontology has been created, and curated, with care.

Triple store: Advanced features summary

  • Feature 1 is very complex
  • Feature 2 is simple

Why a single content model can’t solve all your problems

In the introduction I explained how each of the different content repositories has advantages and disadvantages. It is possible to model most domains, entirely, in either relational databases, document stores or triple stores. However, as the examples above show, some features are too complicated to implement for some content repositories. The solution, in my opinion, is to use different content repositories, depending on what you need to do, even with the same domain.

As an analogy, serving a search engine from a relational database is not a good idea, as the content model is not well tuned to free-text searching. Instead a separate search engine would be created to serve the specific functionality, where relational content is flattened to produce a number of search indices, allowing fast, free-text search capabilities. As this example shows, for a particular problem, content must be modelled in an appropriate way.

Using a document store + triple store

For this solution, document content is converted into triples by means of a set of rules. These rules will be processed by means of a ‘bridge’ which will convert the change stream provided by the document store, such as Atom, into RDF or SPARQL. In this way, a triple store will contain a copy of the document data in the form of triples (with a small degree of latency due to the conversion process). The system now provides a document store for fast, scalable access to the full content, and a SPARQL endpoint providing all the inference and aggregation that is needed.

The following diagram represents the architecture used in this solution. It combines a document store, and a triple store, to provide both atomic content management and relational structures and inferences:


Continuing with the advanced feature implementation…

Advanced feature 1: Versioned films

See ‘document stores’.

Advanced feature 2: Tag relationships

See ‘triple stores’

Document store + triple store: Advanced features summary

  • Feature 1 is simple
  • Feature 2 is simple

By the leveraging the innate advantages of two different content models, all the advanced features are simple to achieve!

Cross-domain aggregation…

What seems interesting about this proposed solution, is that it involves the use of semantic web technologies without publishing any data interchange formats on the web. It would, however, seem a shame to stop short of publishing linked data. I would advocate that this approach is additionally used as a platform to support the publication of linked data, where the overhead of doing so, through the use of a triple store, is significantly reduced.

Combining this approach with ‘open linked data’ and SPARQL endpoints would demonstrate the power of modelling content as triples: expressing relationships in a reusable, standardised way, allows content to be aggregated within, or between, systems, ecosystems, or organisations.


If a relational database is not used for content, how can data integrity be maintained?

In short, it is not. Integrity is applied at the point of entry through form validation and referential look-ups. Integrity then becomes the responsibility of systems higher up the chain (for example, the code that renders the page: if an actor doesn’t exist for a film, do not show them). If data integrity really is needed (for example, financial or user data), maybe an SQL database is a better choice; it is still possible to bridge content into the triple store if it is needed.

How is the ontology data managed?

This is a tricky one, and it really depends….I think there are three choices:

  1. Curate the domain ontology manually: Editing and importing RDF/turtle. Needs people with the relevant skills. Could be ideal for small, stable ontologies
  2. Use a linked data wiki: Some tools and frameworks exist for building semantic wikis (e.g. Ontowiki). This could be too generic an approach for some, more complex, ontologies.
  3. Use a relational database CMS + data bridge: Despite the drawbacks of relational databases for content, it may still be a practical solution for ontological data. By building a CMS on a relational database we get all the mature database technology and data integrity, only leaving the tricky process of bridging the ontology data into a triple store.

And finally…

Some of the ideas in this post are based on projects happening at the BBC, many thanks in particular to the BBC News & Knowledge team for sharing their recent endeavours into semantic web technologies.