Computer Science departments, listen up: animation is a core software skill

What’s missing in computer science curriculums?

I teach in a computer science department, and one thing is painfully true: universities and colleges have been very slow to introduce basic animation skills into their curriculums. This is a big problem.

Why?

2D and 3D graphics and animation are popping up everywhere, and more and more, programmers discover they have to be part time artists. This is particularly true for developers of Web 2.0/3.0 apps. Web app developers find themselves using graphics tools to build both user interface controls and to create animated models. And, as applications that can convert 3D models and animations into lightweight renderings become more efficient and more powerful, Web app developers are having to add 3D tools to their quiver of arrows.

2D animation engines include Adobe Flash, Microsoft Silverlight, and HTML 5. The drag-and-drop application that generates Flash animation, Adobe Flash Developer (recently renamed from Flex Developer), along with Microsoft Blend, which generates Silverlight code, give the programmer a way to develop animation without having to work with an artist’s interface. But in truth, drag-and-drop tools only get the programmer so far. In order to refine, extend, and debug interfaces, the programmer has to master the two XML languages used by Flash Developer and Blend (MXML and XAML, respectively) to define interface components and 3D models. The programmer also needs to be comfortable with the two languages that these XML specifications compile down to (ActionScript and C#). and that means learning their extensive animation capabilities.

And it’s not just small scale modeling and animation.

Sophisticated 2D and 3D animation is also confronting the young programmer. Game, feature film, animation short, training video, and TV show development are rapid growth areas. Interestingly, while powerful GUI-based applications like Autodesk Maya, Autodesk 3DS Max, Toon Boom Animate, Vue, and Poser are used largely by non-programmers, there is a critical niche for the programmer-animator. Scripting languages are used to perform many basic modeling and refinement tasks. Not to mention the fact that someone has to build these huge animation apps, and these folks, well, they’re programmers. The point is that it’s hard to build an application that creates things you do not understand.

The emergence of canned content and cheap animation apps.

There is also an explosion of applications that provide canned animation capabilities, and there are a growing number of websites that sell animation content. This makes it feasible for programmers to create basic animations for websites and desktop applications, without the need for full-blown animation artists.  DAZ3d.com and contentparadise.com are two highly popular content sites. And sophisticated animation projects can be developed with applications that are cheap (or free). These include DAZ, Blender, and Carrara.

The bigger picture: the boundaries between disciplines are breaking down.

Perhaps the most compelling reason for universities and colleges to start treating animation as a first class academic citizen is that the nature of computing itself is rapidly undergoing an expansion. Computer science graduates are finding jobs in the financial, communication, genetic engineering, mechanical and electrical engineering, alternative fuels, architecture, advertising, business, and medical industries - and all of these professional disciplines have substantive animation components. It’s the age of merging fields, with borders collapsing, and computing skills becoming necessary in almost all walks of life. As non-technical types must be able to do basic programming and software configuration tasks, programmers are learning that they need a non-programming area of expertise in order to stay competitive - and tossing animation skills into the pot is a sure plus.

The need for declarative technology in multimedia asset management

What “declarative”  really means

In programming languages, we use the word “declarative” to refer to a language that does not force a programmer to specify more sequencing information than is strictly necessary.  The idea is for the program to tell the computer what needs to be done, and not precisely how to do it. Instead of an algorithm, we provide a static specification of what the result will look like. In an imperative (or non-declarative) language, the programmer might specify that an array is to be read from position 0 to position 99, and that at each position in the array, the value at that position is to be increased by 1.  In a declarative language, the programmer might be able to simply state that every entry in the array is to be incremented by 1.

But what does the word “declarative” really mean, English-wise?  Well, it refers to the process of making a declaration, of making a formal statement about something.

Searching web-based media assets: today’s tools

What does this have to do with the Semantic Web and/or Web 3.0? That’s what this blog is dedicated to: next generation web technology.

A major growth area for the web will be applications that manage complex forms of media, and the automatic searching of blob and continuous media, such as images, video, sound, animation, 3D models, and of mixed-mode media. These will present a major challenge. Simply put, our best technology for making advanced forms of media searchable is tagging. And this low-level tool doesn’t come close to allowing us to search according to the true meaning of media assets. Searching for blob and continuous media is still painstaking and manual.

So, how could we make things like video and 3D models more searchable? How could we improve the search process? Two important technologies offer significant help. The first is more sophisticated, high level, and content-ful tagging protocols, such as MPEG-7. Another is image processing, which is actually a highly developed area, since the U.S. government has poured many millions of dollars into it over the past several decades. It’s also true that language processing tools have been used to parse and interpret textual descriptions of media, but this sort of freeform analysis is difficult to make accurate and predictable, given the extreme complexity and ambiguity of natural language. People write “stories” with language, and a long piece of text has to be read from beginning to end, in order to understand it.

What about using notes?

But perhaps the future lies is a sort of compromise technology, one where tagging information made with tools like MPEG-7, combined with image processing, and/or natural language processing, is used to cut the search space from many thousands of media artifacts to something that could be processed interactively by humans. This is in contrast to downloading potentially huge files and viewing them in real time. Even downloading small video and audio clips and low-pixel count preview images can overwhelm the average interactive web user. These often don’t give an accurate vision of what the full pieces of media contain.

The answer might lie in highly organized “notes”, written with note-taking applications. These applications provide quick, compact, and highly visual ways for people to document their thoughts. They range from lists to outlines to hierarchically structured blocks of text to diagrammatic “mind-maps”. Note-taking applications often support video and images and sound; in a way that might seem ironic, an individual could create mini-multimedia artifacts to facilitate the searching of large multimedia assets. But in truth, this is could be a very powerful technique - because one of the primary attributes of most note-taking applications is that they provide “at-a-glance” semantics. In other words, if used right, a note or a list or a mind-map is captured on in a single screen image. And, when users build more complex notes, these applications typically facilitate very top-down structures. Notebooks have tables of content; hierarchical notes have root nodes; mind-maps are expandable.

And above all else, there is something about the note-taking philosophy that encourages compactness. In other words, they are in a sense, declarative. A note makes a quick, firm statement.

More on this, soon.

The imposing heterogeneity of media applications

This blog is dedicated to the discussion of emerging web technologies. Today, we look at a the rapidly growing world of media applications, and their impact on the Semantic Web.

The problem of searching for media assets.

We’ve already looked at advanced media, in particular video, audio, and animation data, in previous blog postings. In particular, we’ve looked at the subtle and complex nature of media asset semantics. We’ve seen that interpreting a piece of video, for example, is far, far more difficult than interpreting an integer or character field. Since the goal of the Semantic Web effort is to make the searching of the web highly automated, advanced media is becoming a huge and critical research and development focus for the builders of next-generation web development applications.

Just how do we provide an environment where media assets can be searched in a mostly automatic fashion, so that a human does not have to painfully paw through hundreds or thousands (or millions) of video chunks to find the right one? We’ve looked at emerging technologies for marking up advanced media information, and for making it usable in a variety of web applications. We’ve also looked at the dramatic challenge presented by mega apps to would-be users; the interfaces to these applications are truly massive and cannot present to the user the way in which they are meant to be used.

The problem of proprietary formats.

One specific, and very difficult problem, is the massive heterogeneity, not just of media formats, compression technologies, and container technologies, but of the applications themselves. If we are going to automate the searching of complex modeling, video, audio, and other media assets, we’re going to have to address a key question: since many media apps make use of their own proprietary data formats, how are we going to provide automated ways of searching media assets that are stored in these formats?

The problem of highly imperfect generic formats.

There are indeed many existing, as well as soon-to-emerge, standards for importing and exporting data between powerful media applications, but transformations in and out of these formats are often “lossy”, in that information is lost or changed. In fact, locating and downloading assets that are in supposedly-generic form is often very frustrating, because these assets end up not performing well. They can be difficult to edit and reuse. 3D animation models regularly blow up when animators try to import them into animation applications and the manipulate them. A hawk may look like a hawk until you try to render it with its wings flapping, and suddenly it’s a blob of geometric garbage.

One possible direction.

So, what do we do about the fact that many media assets must be manipulated by the original applications that created them? How can we facilitate reuse? It’s extremely unrealistic to expect users to master perhaps dozens of video or audio or animation applications. Filtering assets according to their file extensions is a good idea, and it is a well established practice.

But what we really need is a globally-known site that either literally or conceptually centralizes the massive network of import/export relationships, along with information about the relative success of these mappings. Are they ever lossy? If so, can they be fixed? What series of applications might we want an asset to be imported/exported through so that in the end it is in a usable format, given the applications that the user owns and has mastered?

There is much to be done. Right now, searching for and reusing media assets is a painstaking, trial-and-error-prone process.

Personal Information Management Applications and Web 3.0

This blog is devoted to the discussion of Semantic Web and Web 2.0/3.0 technology.

Managing personal and small group information.

When it comes to so-called Web 2.0 and 3.0 technology, one of the most proliferate marketplaces involves the explosion of applications for managing information for individuals and small groups. Looking only at applications developed for Macs, we see an array of information management technologies.

Notebooks.

One of the most popular formats for managing information uses the paradigm of a notebook. The user can create a notebook, often selecting from multiple canned formats, such as a diary, class notes, or a novel, complete perhaps with a notebook cover and a spiral wire down the left side. The application creates a table of contents, and users can create sections and pages - and stuff virtually any kind of information on each page. Two very good examples of this approach are NoteShare and Notebook.

Interestingly, and perhaps because many of the applications in this category have been around for a number of years, these tend to not be true web applications. Often you can share notebooks, including full read/write access, via a URL and a simple browser interface, and you can publish a notebook at a URL. But the products are primarily for single-user, desktop use.

A good example of a notebook application that is a true web application is Zoho Notebook. (Zoho actually provides a large set of web based applications, of which the note program is just one.)

Buckets.

The other very popular note format uses the bucket or folder approach. The application may or may not support the nesting of these buckets and/or the creation of conceptual buckets, so that a given note can exist in more than one bucket. Two very good applications that use this approach are SOHO Notes and Yojimbo. These two applications are desktop-based, although most applications in this category support the synching of notes over multiple machines, using the Apple web-synching technology.

A hybrid desktop/web application is Evernote, which has elegant desktop applications for Windows machines, Macs, and a variety of handhelds and cell phones. It also has a very effective web interface. The user can sync multiple Evernote desktop instances via Evernote’s web server. Users can thus avoid ever using the web interface.

Outlines.

One specialized sort of information management application involves the creation of embedded outlines and bulleted lists. These applications, such as OmniOutliner, actually provide a full notebook functionality as well. OmniOutliner notebooks can be published on the web, but it is very definitely a desktop application.

Task lists.

An even more specialized class of information management applications support To-Do lists. Great examples are Zenbe Lists (they also provide integrated email and collaborative software) and rememberthemilk.com. These are web applications.

Photos and video.

There are a rapidly growing number of applications that allow users to collect, sort, tag, edit, and share photographs and video. Apple’s iPhoto is a great example. It is very much a desktop app, although applications in this class typically support the publication of images and video on the web, and sometimes, even read/write access via the web.

Stories, scripts, novels, and storyboards.

There are a number of highly specialized applications that support the development of fiction, including Final Draft and Montage (scripts), Scrivener and StoryMill (fiction prose), and Toon Boom storyboard (which is actually an impressive drawing program). Again, users can often publish to the web. Interestingly, many of these applications can easily be used as full blown, generic note applications, and can manage many forms of media.

Diary Applications.

Perhaps the most popular diary application on Macs is MacJournal (by the Montage and StoryMill folks). An interesting twist is that it is also an excellent blogging program. I use it to write this blog. This is, of course, one of the most widely used vehicles for sharing information on the web, and you can expect other sorts of personal information management systems to have blogging capabilities added to them.

Small, forms-based database management systems.

These applications are desktop apps. Apple’s Bento is a very good example. It actually is a sort of hybrid database/spreadsheet application. The most recent release allows multiple instances of Bento to share databases running on computers on a shared network.

Mind-Mapping.

The “circles and lines” applications have become highly specialized. The most well known one is MindManager, and there are versions for Windows machines and Macs. These are desktop apps. The vender, MindJet, recently introduced both web interfaces for sharing and updating desktop mind maps, as well as a web-based application that has a fresh, smooth interface, and provides team collaboration tools. Many forms of media can be placed in MindManager, including data from a wide variety of relational database management systems.

Screen and audio capture.

There are a number of applications that allow users to capture desktop video, along with audio voice-overs. Camtasia (which has Windows and Mac products) and Screenium are popular products.

These applications are, in a way, successors to slide applications like Microsoft Powerpoint and Apple Keynote. More and more presentations are being engineered with screen capture and audio applications, and these applications often support text and image data, as well as the insertion of video capture of the speaker. Sometimes, Powerpoint slides can be imported.

Conferencing apps.

There are several applications that provide hybrid desktop/browser live communication, including video, sound, and collaborative white-boarding. The best known one is probably Cisco WebEx, which comes in varieties for Macs and Windows machines. Skype supports a similar, limited product - which is free. One of the nice things about these products is that they come with their own voice lines. Other products, like Adobe ConnectNow, require the use of a cell phone to carry voice. With most of these products, a conference can be recorded for later use.

Finally…

Importantly, we note that in this rapidly-exploding marketplace, the borders between these various categories are being broken down, and applications often support a number of these capabilities at once. A good example is Curio, a desktop application that supports notes, lists, video, audio, white-boarding, mind-mapping, and limited web publishing.

Making information management scale: leveraging metadata on the new Web

Previous postings of this blog.

This blog is dedicated to advanced Web development tools and concepts. Previous blog postings have focused on the emerging Semantic Web, which promises to make the Web radically easier to search and to greatly enhance the value of the vast sea of currently-disconnected information spread across the Web. We have also looked at Web 3.0 efforts, which promise to make multimedia websites highly usable and capable of conveying far more information than the current generation of websites. Previous postings describe breadth and depth of cutting edge Web technology.

Metadata: making that ratio small.

Here’s something that’s very important: Much of the ongoing research and development that is loosely categorized as Semantic Web and Web 3.0 efforts is focused on a specific technical goal, one that has been at the core of information management technology since the mainframe era that was epitomized by the IBM 360 series. That goal is to leverage metadata as much as possible.

It’s our best weapon against the truly staggering amount of information on the Web. This includes traditional text-based and numeric data, as well as books, medical advice, photographs, entertainment and training videos, music and recorded books, investment information, educational materials, scientific materials, e-government information, etc., etc. How can we possibly organize information and then search it in a way that scales? The Web is far from a closed world. In traditional data processing environments like banking, insurance, and credit card processing, we could get our arms around all of the data, as vast as it may have seemed. But the world of information today is an open world, effectively infinite in size.

Very informally, if you look at the size of the metadata divided by the size of the data itself, the smaller that fraction the better. In traditional relational databases (built with database management systems, such as Oracle, MS SQL Server, MySQL, PostgreSQL, or DB2), the extreme focus on minimizing this ratio has enabled the fast processing of extremely large volumes of data. The tradeoff is that the table definitions (or the “schema”), which form the heart of the metadata are very, very simplistic.

The old days: relational database schemas.

An insurance claim may be defined as a table with such columns as Subscriber_Name, Medical_Provider, etc., and thus, may consist of little or no more than a series of simple character and numeric fields. But if we need to process fifty thousand of them tonight, we must be able to bring many such table rows into memory at once, and quickly move through them. The database world was an extension of the paper world: a row in an insurance claim table was effectively an electronic successor to the traditional claim form.

Today: a far more challenging problem.

But on the new Web, information can be far more complex in nature, making the metadata to data ratio far larger. We’ve looked at some of the emerging technology and technical trends for embedding metadata in advanced forms of data (and for processing that metadata); this data includes books, images, video, modeling and animation, and sound. This new generation of information formats make up our personal health records and medical records images, industrial training materials, university “distance” courses, and the like. Each instance of these tends to be far more unique than individual insurance claim forms. And, it takes a lot of metadata to properly convey their “meaning”.

The challenge.

What we’re struggling with right now is to succinctly specify the meaning of modern media assets and to automate searching based on this metadata. This is our only hope for leveraging that ratio of metadata size divided by data size.

Multimedia: The Problem of Subtle Semantics

The challenge of the Semantic Web.

We’ve looked at the emerging Semantic Web technology in the previous postings of this blog. The idea is to have a far, far smarter Web, one where the process of finding and interpreting and making use of far flung information can be largely automated. This is in sharp contrast with today’s Web, where these things have to be done in a painful, extremely time-consuming fashion.

So that is the key challenge. It has to do with searching the kinds of information that are important to us in our daily lives. This information, as it turns out, is very difficult to process automatically. Why is this?

The complexity of modern multimedia.

I teach a very basic 3D animation class to mostly computer science students. We use Maya, arguably the most popular 3D animation application, one that is used in the making of many animated features. The interesting thing about animation is that it is truly multimedia. It can give us a lot of insight into what we need the new Web to do for us.

That’s because the number and diversity of applications that are used for drawing, documenting, modeling, animating, motion capture, texturing, video rendering, video editing, video conversion and compression, sound editing, in even small projects, can be very impressive. Correspondingly, the wide variety and complexity of media formats involved in an animation project can be overwhelming.

What happens in an animation project? The workflow might begin with vector storyboard drawings to break the story down into scenes. In a typical animation project, 3D models in a variety of proprietary formats are used. Models must be transformed as they are exported from one application and imported into the next. Multiple video renders of animated models are made, and they must be edited together, along with multiple sound files. Multiple video and audio formats might be used. 2D images are used for textures; photographs of butterfly wings can be used to make an animated butterfly very realistic, and a checkerboard image made with Photoshop can be used to make a Linoleum floor. And along the way, a variety of note taking, screen capture, and conferencing software might be used to facilitate group communication.

There is also a heavy focus on reuse in an animation project. Building every model, editing every texture, creating every environment and background, recording every sound from scratch is frequently intractable. If existing assets cannot be tailored and reused, the project would be far too expensive and time consuming, and would demand too wide a variety of professionals to always be available. This raises the multimedia stakes, as assets of widely differing forms must be constantly reconfigured and used in concert in new ways.

But what’s the real problem? We aren’t all trying to produce complex animated videos. But very interestingly, in our everyday lives we essentially face the animator’s challenge when we try to find and use information on the Web. That’s because we’re often looking for things whose meaning, whose interpretation, demands focused human thought. We are looking not for business data, but for pieces of media, and the problem is that today, most of our searching has to be based on tags or brief textual descriptions that are associated with pieces of media, and not on the true meaning of the media itself.

The needs of the business world are not our needs.

It’s the subjective nature of media assets - this is what is at the heart of the problem facing us. Existing technology for searching the web is based on keywords and very short pieces of text.

There is other technology, though, under active development, stuff that serves as the information storage backbone of most commercial websites. It’s the technology that has for decades been used in-house (not on the Web) by businesses when they process large databases. But this stuff was designed to handle traditional business data forms, like integers, character strings, real numbers, dates, timestamps, and full text.

There is more, though. All of the major database management systems, along with tools for building and searching advanced websites are being retrofitted (or in some cases, built from the ground up) to manage more than keywords and text, more than standard business data.

But up to now, the focus has not been on supporting the kinds of information you and I are most interested in. The focus has been on extending database and Web technology to support xml documents, as well as more complex data objects, like those inside a Java program, as well as other forms of data found inside programs. This includes arrays and lists and short pieces of textual data, like the names of diseases.

In other words, we’ve been busy extending our support of the business world, so they can store complex business data in databases and make that information processable over the Web. You and I have largely been left out.

Finally, we are attacking our needs.

But there now many ongoing efforts to extend database and Web technology to make it useful to us. The new focus is on supporting blob and continuous media like images, video, and audio. This is extremely hard to do.

Why? Because the strongest means by which we deduce the meeting of business data is by looking at its internal structure and the terms that are used to describe that structure. A relational table named Prescriptions, with a character attributes Patient Name, Doctor’s Name, and Medication, and with a numeric attribute Dosage, is pretty easy to interpret.

But what do we do with a photograph, which is just a grid of pixels with no internal structure? Or a long series of images, along with a sound track, put together to form a piece of video?

The U.S. military has been pumping money into image processing for several decades, and so all is not lost. There is a vast body of mathematical research and software development that allows us to write programs that can find a particular face in a crowd and search satellite photos for airplane runways. But in general, we cannot at this time write a program that can process an arbitrary photo or video clip and tell us what it means. That means we can’t quickly search vast media database for useful pieces of information.

The goal behind the Semantic Web effort is to build a new generation of websites whose information can be searched automatically, and where information from multiple sites can be automatically integrated. To do this with numeric and character based data is quite doable. But when it comes to multimedia, like images and sound and video and 3D models and engineering designs, well, we have a long way to go. The meaning - in other words, the semantics - of these forms of data are complex and subtle, and highly dependent upon an individual’s interpretation of that media.

So, we see that we have only just begun our journey to create the new Web.

Semantics and the new Web: Built out of very old ideas.

Describing the real world in computers.

The word “semantic” has been a buzzword in computer science for decades. The youthful Artificial Intelligence world invented these things called Semantic Networks or Semantic Nets a half century ago. The idea was to come up with a crisp, formal language for representing real world things inside a computer. This took the form of a small set of constructs that would be general purpose, in that they could be applied to almost any sort of information. Further, these constructs would somehow be intuitive and natural, in that they would get to the heart of what it means to describe everything from horses to insurance claims to marriages to the contents of the Bill of Rights.

Basic, long-standing, core concepts.

What emerged has certainly stood the test of time. Big time. Opinions differ widely on just what constitutes the core constructs. Different people have used different names for these terms, and, although the idea was to specify something formal, the definitions of these constructs were generally sloppy. But here is a reasonable specification, in its most rudimentary form:

There are objects (which might also be called entities, things, or concepts). Objects have unique names.

Objects are interrelated by attributes (which might also be called relationships or properties). Attributes are directional, and they have names.

In other words, things in the world can be represented as a simple directed graph. We could say that there are objects called Chickens that have an attribute called Are. The value of this might be an object called Birds. Birds might have an attribute called Lives-In, which links Birds to the object Barnyard. There might be an object called Mr. Fried, which has an attribute called IS, which connects Mr. Fried to the object Chickens.

There are many popular various of this basic idea that have emerged, and they tend to be of the following nature:

One idea is to make a sharp distinction between the notion of a subtype (or sub-kind or subset) and other attributes. So, our attribute Are might become a core concept itself, and we might name it Is-A. Chickens IS-A Birds, People IS-A Biped, etc. Other attributes like Lives-In would be considered inherently different from Is-A.

We could introduce another generalization. A general term for attributes Lives-In and other similar attributes might be Has-A. In fact, we could stop using special words for attributes in general, and just use the terms Is-A and Has-A. We would then say that Marriages Has-A Wife, as well as a Husband, as well as a Date.

These general ideas are actually old, and actually significantly predate computing. We have been struggling with the problem of describing real world objects (like Cows), real world concepts (like Marriages and Respects), and their interrelationships and categories since the emergence of the earliest philosophers. Aristotle distinguished between objects and their attributes, and carefully studied and described many animals and plants.

What does it all mean for the new Web?

So, what does all this mean to us, today, and what does it have to do with modern Web technology? Well, first of all, these concepts of objects and attributes have spread throughout all of computer science.

There have been some significant extensions, like distinguishing between an attribute that we might call a relationship, which interconnects complex objects or notions (like a driver owning a car) and attributes that interconnect complex objects and notions with atomic or simple things (like a car having a color or a driver having a name). Generally, these latter, simple kinds of attributes are now what we call attributes, and are considered inherently different from (and simpler than) relationships.

Another extension that has become a core concept in programming languages is something we might call an object identifier, which is a unique number or other identifier for individual objects; this allows us to carefully distinguish between two people who have the same mother, and two people who have mothers who just happen to have the same name.

Programing languages also introduced the concept of methods, or little programs that can give life to objects. You might be able to tell a marriage object to tell us the names of the husband and wife.

But basic concepts have not changed. There seems to be something natural and fundamental about them.

Building a new world out of old concepts.

And the Web? A revolution is happening today. We are developing languages that allow Web designers to embed machine-readable specifications in Web-resident information. This will largely automate the process of searching the Web, as well as the integration of information at multiple sites. This will in turn lead to the discovery of knowledge by putting together diverse information from across the Web. We have discussed these emerging technologies in the previous postings of this blog; they are heavily and deliberately built on top of ideas that date back to the 1950’s, and in fact can trace their roots to ancient Greece.

Dynamic pages, hidden data, and infered information: the danger of scale.

The good and bad sides of the powerful Semantic Web.

So what happens when the Semantic Web is here? It’s supposed to largely automate the process of searching the Web by allowing us to attach machine-readable assertions (perhaps by using RDF) to information posted on the Web. Then, instead of us poor flailing humans having to painstakingly chase down countless URLs until we get what we want, smart search engines would be able to find precisely what we want in a single shot.

There is an obvious danger to all of this. The new Web will scale, in both good ways and bad. I am certainly not the first person to point out that the smarter the Web, the easier it will be for software to peruse the Web and dig up personal information about us. There will be software that carefully crafts ads in Spam mail that will target our vulnerabilities and our preferences. Websites will dynamically create webpages that target us individually, as well. When we shop online, when we read news, when we make social connections online, the Web will be disarmingly efficient and effective, and this leaves lots of room for fraud and manipulation.

This is already happening to a significant degree, and most of us are aware of it.

The no-longer-hidden database factor.

There is something more subtle about all of this, however. One of the most difficult things to do with traditional Web technology is to expose the content of databases to Web visitors. That’s because the pages that deliver up content pulled from databases are highly dynamic in nature, and so it is very hard for web designers to make search engines (like Google) find and index the content of these databases. There are simple and somewhat effective things web designers can do, like creating static pages that contain terms that are meant to draw web visitors to their sites. These pages are not “destination” pages; rather, they exist only as a way of advertising the information contained in databases.

In the future, RDF assertions (and other machine-readable content) will be added to websites, and they will server as far more effective draws.

But what about privacy? Will web designers inadvertently facilitate fraud and identity theft by enabling the automatic cross-referencing of detailed information existing in databases that have been built and deployed on the Web in isolation? This capability is at the heart of the Semantic Web effort. Information that right now can only be obtained by individual users manipulating individual web interfaces will be discoverable by smart search engines.

The real problem: it will scale.

This is a big deal. It’s not just that previously hidden information will now be discoverable. Because standardized terms and assertions will be used to describe information in databases, smart search engines will be able to automatically interrelate data from otherwise unrelated database systems. When information from multiple places is integrated, new information is effectively created.

For a moment, let’s forget about databases and look at a simple example of information that might be stored statically in two websites. Here is an example adapted from the previous posting of this blog:

Assertion 1: Joe is tall for an athlete.
Assertion 2: Tall athletes should try out for basketball.

A new inference: Joe should try out for basketball.

The point here is that this new inference can be inferred automatically, without the intervention of a human being.

We noted in the previous posting that the information about Joe and the information about basketball might be on different websites. These websites could easily have been built independently. But a key notion - and that is the semantics of the word “tall” in the context of basketball - is what allows this information to be automatically integrated. Another site might point out that Timmy is tall for a kindergarten student, but this would not trigger the suggestion that Timmy try out for the NBA.

Now, let’s get back to database systems, these things that can contain countless terabytes of personal information. Perhaps there is a database at one site containing information about many thousands of athletes. Perhaps there are hundreds or thousands of such sites. The Semantic Web would allow us to find tall athletes without having to know in advance what databases around the world have this sort of data inside them, data that previously could only have been extracted through tedious, time-consume human/computer interaction. Now, a high school counselor or a sports agent looking for new clients can be far more effective at their jobs.

Or, maybe it’s a drug company matching potential customers up with expensive drugs targeted toward specific diseases, or toward people who might have vague symptoms of various diseases, and who might be easily convinced they are sick. Ora con artist looking to scam elderly people who are likely to have dementias.

Or - well, get it? The Semantic Web will scale because it will have access to huge databases, and not just a world wide web of static pages. That’s the danger.

Real-World Look at the Semantic Web, part 2

This blog is dedicated to the study of emerging Web technology, in particular, ongoing research and development aimed at building software tools that will underlie the emerging Semantic Web. Last time, we looked at DBpedia, something that a former graduate student at my university, Greg Ziebold, pointed me toward.

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.

A Real-World Look at the Semantic Web, part 1

This blog is dedicated to the study of emerging Web technology, in particular, ongoing research and development aimed at building software tools that will underlie the emerging Semantic Web. In this posting, we look at a little-known website that has the potential of setting the pace for the developers of the Semantic Web.

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.