The Traveler had it easy.

There is a series of three science fiction novels by John Twelve Hawks. They concern a “Traveler” who battles the “Vast Machine”, which is a global grid of security cameras, governmental and corporate databases, and computers that collect information on people, track them, and manipulate society. They are very popular novels.

But these books are not all that imaginative.

Why not? If and when the Semantic Web ever emerges (please see previous postings of this blog), there will be a lot more than security camera footage and passive database systems out there. In his books, Twelve Hawks describes programmers working for the Vast Machine who pull information out of databases and plant information in databases, and who somehow locate and integrate information from many sources. It’s not clear how they do it.

The problem is tractability. Extracting the meaning of data (its “semantics”) is extremely difficult, and given today’s Web, it is a highly manual, painstaking, and ultimately intractable problem. Twelve Hawks’ Vast Machine isn’t all that much of a threat.

Consider, however, the emerging Semantic Web.

The whole idea of the Semantic Web, on the other hand, is to make databases proactive, to let them announce their content by using globally accepted standards. In this blog, we have looked at one proposed standard, called RDF, which is based on “triples” that interrelate information, and a Web-hopping query language called SPARQL that can concatenate triples that define information at diverse, independently-created websites – thus inferring new information. We’ve looked at the beginnings of this technology as it is taking form on the Web.

In other words, it might not be long at all before the least of our problems would be dastardly hackers who break into databases and pluck information – because the finding, integrating, and interpreting of data from highly divergent sources will become, in large part, automatic.

It will make the intractable quite tractable.

Okay, I confess…

It is not as simple as that, of course, and I am grossly overstating the danger. Presumably, private databases belonging to corporations and governments will not be loaded up with this sort of semantic metadata and placed on the open Web. And the sorts of inferences that can be made by unifying metadata from multiple sites will be fairly low-level, leaving a lot of difficult work for any Vast Machine that wants to manipulate our every move and thought.

Still…

But it is true that the potential for misuse will increase sharply. There will indeed be many isolated instances where innocently posted information from two or more sites will be automatically linked together because of uniformly-specified metadata. If one triple at one site has data marked up as “People OWN Kinds-0f-StampCollections”, and another site says that “Kinds-of-StampCollections HAVE Certain-Values”, a thief who knows little about philatelics might learn that Bob owns stamps from the Southern Confederacy, and that stamps from the Southern Confederacy are worth hundreds of thousands of dollars…

Just a thought for the next sci-fi writer.

The Semantic MediaWiki.

In this posting, we look at the Semantic MediaWiki, something else that Greg told me about. It is an extension of MediaWiki, the application that the Wikipedia is built out of. You can learn all about it at the Semantic MediaWiki website. The idea behind Semantic MediaWiki is to provide a more powerful wiki tool, namely one that supports more than just human-readable things like text and images.

RDF and namespaces: creating machine-readable, web-based information.

The idea is to allow entries in wikis that contain machine-readable information, so that searching can be performed in a largely automatic fashion. Specifically, the Semantic MediaWiki allows users to export information from a wiki in RDF format. An RDF specification consists of “triples” that form “assertions”. Consider the following

Assertion 1: Joe is tall.
Assertion 2: Tall People should try out for Basketball.

The idea is for terms in triples (“Joe”, “tall”, “is”, “Tall People”, etc.) to be taken from predefined and globally accessible namespaces. This would ensure that everyone who uses a given term (like “tall” or “Should try out for”) will have the same meaning in mind. In this way, rather than having to painfully search for information that pertains to Tall People, for example, a smart search engine could do the searching for us.

Building locally, growing globally.

There is more to this. These namespaces can be available on the Web, and RDF statements can point to the relevant namespaces. This means that software searching the Web, and processing these triples, can easily find the relevant namespaces.

Also, the things in the right and left side of a triple (like “Joe” and “tall”) can themselves be Web-based resources. This means that information scattered around the Web can be interconnected – but all the work can be done locally. No one has to manually integrate millions of websites. The job can be done little by little, in a quiet way, as people start to store their information in an RDF compatible fashion.

This is how the Semantic Web will scale. Everyone will use shared namespaces and shared protocols like RDF. This will, in essence, turn the Web into one big website that can be searched in a partly automatic fashion.

SPARQL: querying RDF-based information.

How will we interrelate data scattered around the Web?

There is a query language out there, called SPARQL, that can be used to search the Web. SPARQL can follow RDF connections around the globe. How is this done? It has to do with being able to “infer” new things. Consider a fact that can be automatically deduced from the two assertions above:

A new inference: Joe should try out for Basketball.

Assertion 1 could be on a server in Detroit, and assertion 2 could be on a server in Miami, and SPARQL could do the job of making the leap that leads to the new inference.

This means that we could figure out what Joe should be doing right now without having to find the two pieces of information manually (the fact that he is tall, and that tall people should play basketball), and without having to make the inference ourselves.

This is a big deal. This sort of automation is what the Semantic Web is all about.

So what do real people do with the Semantic MediaWiki? We’ll look at this next.

DBpedia.

It’s called DBpedia. A former graduate student at my university, Greg Ziebold, pointed me toward it. The goal of the DBpedia is to transform data from the Wikipedia into a chunk of the Semantic Web. To do this, DBpedia is using RDF technology, something we have discussed is past postings of this blog. Behind RDF is an extremely simple concept, but one that has proven extremely powerful and versatile.

The general idea is to break knowledge up into “triples” that describe relationships between pieces of information. These triples can be chained together to discover new relationships. And, importantly, triples must make use of widely shared sets of terminology, called namespaces, in order for knowledge from different places on the Web to be properly chained together.

RDF, triples, assertions, and inferences.

A thorough example can be found in a previous posting of this blog.

Here is a very simple example of triples (also known as “assertions”) and how they can be put together into “inferences”.

Assertion 1: Joe is tall.
Assertion 2: Tall People should try out for Basketball.
A new inference: Joe should try out for Basketball.

Keep in mind that we would want to make sure that the words used in these assertions have precise, global meanings. We might take the terms in these two assertions from a basketball namespace, one that would carefully dictate exactly what “tall” means in the basketball world. Certainly, it would be quite different from the meaning of “tall” in a kindergarten namespace.

More on DBpedia.

There’s a fancy word for sets of triples that use namespaces and represent various areas of knowledge. They are called “ontologies”, taken from the term used by philosophers to argue about the existence of various things, like God. The DBpedia is essentially a vast ontology, formed from triples and namespaces. Most of the knowledge defined by this ontology comes from the Wikipedia. The folks behind the DBpedia have been given direct access to the flow of information into the Wikipedia, so that the DBpedia can stay current.

One way to look at the DBpedia is that it takes the Wikipedia and reforms it into something that can be searched far more effectively. Right now, to search the Wikipedia, most of us simply type in terms (either into Google/Yahoo or into the Wikipedia search page). We try various terms and follow links inside the Wikipedia until we find what we think we are looking for. With the DBpedia, users can search with SPARQL, a language based on the structure of SQL and engineered specifically for searching large bases of triples. SPARQL allows us to traverse networks that consists of triples linked by inferences.

That way, if we were a coach looking for promising candidates for our team, we would use SPARQL to make the connection between Joe being tall and the fact that tall people should try out for basketball. This is clearly much faster and more accurate than googling things like “tall”, “basketball”, etc, until we happened to find Joe in one of the web pages that pop up.

The DBpedia website, by the way, claims to have a triple base that consists of 274 million RDF triples.

More on this in the next posting.

From the last posting.

Here is a piece of the RDF code from the previous posting:

<rdf:RDF

xmls:rdf=”http://www.w3.org/1999/02/22-rdf-syntax-ns#”>
xmls:zx=”http://www.someurl.org/zx/”>

<rdf:Description

rdf:about=”http://www.awebsite.org/index.html”>

<zx:created-by>http://www.anotherurl.org/buzz</zx:created-by>

</rdf:Description>

</rdf:RDF>

This can be interpreted as the webpage at awesite.org/index.html was created by Buzz.

Another representation of RDF-based information: 3 triples.

We see from the above that RDF simply represents triples. We could simplify it even more as:

http://awesite.org/index.html was created by Buzz

Part of the reason that the original RDF code above is so much more complex is that the full syntax lets us specify that we are using terms that are defined at specific web addresses. This allows people to use standardized terms and greatly enhances the specitifity of an RDF specification. The full syntax also allows us to reference pieces of information that reside on the Web. (See the previous three postings, 1, 2, 3.)

Before we launch into a SPARQL example, we need to make an important distinction between syntax and symantics. The code above is written in a particular syntax for RDF, one that uses XML. We note that because syntax needs to be very precise, it tends to be verbose. This can cause syntax to obsure the conceptual simplicity of underlaying semantics, or meaning.

But this isn’t the only way to specify RDF triples. Let’s look at some information that is much simpler, and at the same time, let’s look at using a different syntax for specifying RDF-like triples. Here are three triples:

<http://awebsite.org/ > was-created-by “Buzz”

<http://awebsite.org/ > was-created-by “Suzy”

<http://anotherwebsite.org/> was-created-by “Alice”

This is a very simple program. It consists of a two triples that say that a website named awebsite was created by Buzz and Suzy, and another triple that says that Alice created a website called anotherwebsite. We are not saying that was-created-by is a widely used term; it may have been invented only for particular RDF specification, and its meaning would therefore not be precise. We can only interpret it from our general understanding of English words. We also have no idea who these people Buzz and Suzy and Alice are, and we have no other information about them.

SPARQL: searching triples distributed across the Web.

Now, here is a piece of code:

prefix website1: <http://awebsite.org/ >
SELECT ?x
WHERE
{ website1:was-created-by ?x }

We’re getting very close to real SPARQL, by the way, and if you know SQL, you can see the extremely similarity. But syntax is not our issue here. We’re trying to look at concepts.

This code will find the creators of http://awebsite.org. You could imagine that there are actually many thousands of these triples, and that they tell us who built a large number of different websites. Now, we see the power of this query. It will search through all of these triples and find the two of interest to us, and then pluck off the names of the creators.

In fact, these triples could be distributed all around the Web, and we could imagine a search engine taking this query and running it everywhere on the Web where was-created-by triples are stored, and then having it bring back all the creators of awebsite, even if there are a hundred developers, and even if these names are spread around the Internet.

Next, the bigger issue.

In the next posting, we’ll look more closely at SPARQL. One thing we will consider is why it does look so much like SQL. There is a powerful reason for this that has to do with searching information in general.

In the previous two postings, we looked at RDF, which is an excellent example of solid software technology: It serves an important purpose. It is easy to use. And, even if you don’t write any RDF yourself, it is easy to understand what it does, and therefore, how it will impact your life.

RDF, in its simple, quiet way, allows us to interconnect any resources that exist on the Web, and at the same time, make use of standardized terminologies. This provides a highly flexible and semantically expressive way of building the new Semantic Web.

SPARQL: what is it?

RDF is great stuff, but it’s only half the story. If knowledge on the emerging Semantic Web is going to be glued together into RDF triples, how will that information be searched? It doesn’t do any good to have a book that will solve all your problems if you can’t read it or search through it.

SPARQL stands for Protocol And RDF Query Language, with an S tossed into the beginning so we can say it as “sparkle”. Interestingly, when something is called a “query” language, we start thinking in terms of SQL, that largely declarative relational language that is the core of almost all successful relational database management systems. Indeed, as we will see in a later blog posting about XQuery, the language for searching XML-based data, SQL, has served as the model for SPARQL.

A blast from the past.

There’s something about triples that we should look at before moving on. It has to do with the fact that triples are also known as “assertions”, and that assertions can be chained together to make “inferences”. Here are two triples/assertions, specified very informally: THE BALL is ORANGE. ORANGE is an UGLY COLOR. The inference we can make is THE BALL is an UGLY COLOR.

Or, getting back to the Web and RDF, below are two triples specified in RDF; the first one comes from the previous posting of this blog.

<rdf:RDF

xmls:rdf=”http://www.w3.org/1999/02/22-rdf-syntax-ns#”>
xmls:zx=”http://www.someurl.org/zx/”>

<rdf:Description

rdf:about=”http://www.awebsite.org/index.html”>

<zx:created-by>http://www.anotherurl.org/buzz</zx:created-by>

</rdf:Description>

</rdf:RDF>

This first one can be interpreted as the webpage at awesite.org/index.html was created by Buzz.

Here is the second one RDF triple:

<rdf:RDF

xmls:rdf=”http://www.w3.org/1999/02/22-rdf-syntax-ns#”>
xmls:zx=”http://www.someurl.org/zx/”>

<rdf:Description

rdf:about=”http://www.anotherurl.org/buzz”>

<zx:is>http://www.yetanotherurl.org/professor</zx:Is>

</rdf:Description>

</rdf:RDF>

This one can be interpreted as Buzz is the guy described at yetanotherurl.org/professor.

We can chain them together to deduce that the guy who built the page at awebsite.org/index.html is Buzz the professor.

This is an inference.

The point is that if you take a bunch of RDF statements and chain them together, you get what looks a lot like an object-oriented graph of related objects, somewhat like you see in Java.  In a sense, RDF takes an object representation and breaks in down into triples.  There’s really nothing new in RDF, other than the fact that any part of an RDF assertion (triple) can be something found on the Web.

Back to SPARQL.

So, what is SPARQL?  It is a language that can be used to traverse graphs that consist of RDF triples that are chained together into an object network.

We will look at some SPARQL code in the next posting.

The Semantic Web.

In this posting, we will focus on the Semantic Web, which is a global effort at radically improving our ability to search the Web.

Currently, to search the web, we type in keywords into a search engine like Google, which then searches its vast index of webpages for pages that have these keywords in them. Because this sort of search is very low-level, and not at all tied to the true meaning or purpose of the information stored in webpages, searching is painfully iterative and interactive.  A user must chase down countless URLs returned by a search engine to see if any of them are relevant.  Quite frequently, they are not.  And so, the user must refine the set of keywords and tries again.  It might take many attempts before a satisfactory result is obtained.

One of the primary goals of the Semantic Web is to automate the process of searching the Web.  There are two stages to this.  First, people who post information on the Web must capture knowledge about the meaning of their information; this knowledge is commonly called “metadata”.  The metadata is then store with the posted information.

The second stage happens when users search the Web.  Rather than using the low level keyword search approach, the search is at least partly automated.  The iterative process is sharply reduced by employing a smart search engine that knows how to find relevant information by searching for metadata that pertains precisely to whatever it is that the user is seeking.

The bottom line.

The goal?

The Semantic Web would be able to ease the burden of searching for information, as well as find vast stores of “hidden data” that reside in databases that are accessible via webpages, but whose contents right now are not seen by search engines.

Ultimately, we would want the Web to be entirely searchable by software, without any humans guiding the process.  This would be the true Semantic Web.

Namespaces and triples.

In past postings of this blog, we have discussed a handful of key approaches to implement the Semantic Web.  One idea is to tag information with standardized sets of terminology called “namespaces“.

We have also looked at the idea of embedding these tags in things called “triples“.  In this posting, we look at this concept more closely and consider an existing language that would allow people to specify these triples.

RDF and SPARQL.

The most well-known standard for specifying triples is RDF, which stands for the Research Description Framework.  SPARQL is a query language, heavily influenced by SQL, that can be used to search data that has been structured using RDF.

This is the first of a series of blog postings in which we will first look at RDF, and then at SPARQL.  Then, we’ll consider the big issue: will RDF and SPARQL enable the development of the true Semantic Web?

RDF.

So, what is RDF?  At its highest level, RDF is used to describe anything that can be found on the Web.  RDF has an XML syntax; in other words, RDF can be written as an XML program, using a set of predefined “element” and “attribute” tags.   (XML and XML languages were discussed in an earlier posting of this blog, as was XML and declarative languages.)

We might remember that on its own, XML is impotent.  It is not in itself a programming language.  It is simply a language standard for taking a set of tags and using them as “elements” and “attributes” in a declarative, data-intensive languages.  A good example is SMIL, which is used to define multimedia presentations.

Here is a fragment in RDF, using its XML syntax.  Note that XML languages are embedded languages, with opening tags beginning with <> and closing ones ending in </>

<rdf:RDF

xmls:rdf=”http://www.w3.org/1999/02/22-rdf-syntax-ns#”
xmls:zx=”http://www.someurl.org/zx/”>

<rdf:Description

rdf:about=”http://www.awebsite.org/index.html”>
<zx:topic>funstuff</zx:topic>

</rdf:Description>

</rdf:RDF>

This looks complicated, but it’s not.  This simple example illustrates the power of RDF.  It uses a set of standardized RDF-specific tags, and the second line of code tells us where these tags come from: the w3.org site, which contains a vast store of information about advanced web technology.  In other words, we can go to w3.org to find the precise definition of RDF specific tags.

RDF is engineered to also use other sets of tags, in particular, domain-specific tags.  In this example, these tags come from a (non-existing) url called someurl.org.  The tags themselves are prefaced with “zx:” in the rest of the code, so we know which tags are native RDF and which come from a domain-specific set of tags  (called a namespace).

The xml “element” called Description is an RDF-specific tag that tells us we are giving the description of some resource on the Web, namely one at a (non-existing) website called awebsite.org.

The whole piece of code is one triple: It says that the topic of the resource at www.awebsite.org/index.html is funstuff.  Here it is as a triple, with all the xml syntax and the namespace information removed:

www.awebsite.org/index.html <topic> funstuff.

Let’s overview this again.  RDF is an XML language, so it uses the syntax of XML.  One of the primary concepts in XML is that of an “element”, and Description is an XML element, one defined in the RDF standard.  The piece of code begins with two namespace statements, one telling us which RDF specification we are using, and the second telling us that we will also be using some tags from another, domain-specific specification, which includes the tag “topic”.  Then there is the guts of the triple, telling us that we are listing the topic of a Web-resident resource.

More on this in the next posting…