Here's a Web app developed at Stanford University some time ago. It's made up of a single ontology with entries for foods and wines, an ontology agent that returns text from this ontology and a portal that manipulates the text returned by the agent.
http://onto.stanford.edu:8080/wino/index.jsp
The ontology underlying the agent used here contains hierarchies and descriptions of food and wine categories, along with restrictions on how particular instances might be paired together.
For readers wanting an easy-to-read discussion on what exactly an agent is and how it works with ontologies, see
http://www.nature.com/nature/webmatters/agents/agents.html
The next three figures are of screen shots taken from this Web app, which doesn't always employ state-of-the-art technologies like OWL (discussed in earlier posts) under the hood; but, this app provides a simple example of the most basic building blocks that are found in many of today's Semantic Web apps (also discussed in earlier posts).
Figure I - The home page
Figure II - After clicking on the "Roast Duck" link
Figure III - After clicking on the "Web Inventory Search" link
The ontology defines the concept of a wine. According to the specification, a wine is a potable liquid produced by at least one maker of type winery, and is made from at least one type of grape (such grapes are restricted to wine grapes elsewhere in the ontology.)
The declaration additionally stipulates that a wine comes from a region that is wine-producing and, most importantly to the agent, that a wine has four properties: color, sugar, body, and flavor.
The concept of a meal course underlies pairing of a food with a wine. Each course is a consumable thing comprising at least one food and at least one drink, the latter of which is stipulated to be a wine.
When the user selects a type of course, or an individual food that gets mapped to a type of course, the agent will consult that course definition for restrictions on the constituent food or wine. All such course types map back to this concept, like objects to their superclasses in object-oriented programming.
Suppose the user has selected pasta with fra diavolo, or perhaps pasta with spicy red sauce directly. The concept of such a food is defined elsewhere in the ontology. Furthermore, such courses must be a subclass of those with specific restrictions on the properties of their wines.
One wine that matches the above restrictions is the Pauillac. This individual wine is simply defined as a Pauillac whose maker is Chateau Lafite Rothschild. Together with other statements in the ontology, this allows the reasoner (discussed in an earlier post) to deduce many additional facts: that this a Medoc wine from Bordeaux, in France, and that it is red, to name a few.
The concept of a Pauillac specifies that all such wines feature full bodies and strong flavors and are made entirely from cabernet sauvignon grapes. Further, Pauillacs are a particular subset of Medocs, distinguished by their origin in the Pauillac region. It is through this additional subclass relationship that Pauillac are defined elsewhere as red and dry.
Following the above example through the ontology reveals a straightforward logical path for pairing the Pauillac with the selected course. Because these items were specified in a standardized, machine-readable format, it is an equally straightforward task for any compliant automated reasoner.
Why use an ontology?
The functionality provided by the wine agent is not unlike that which could be provided by a simple look-up table. Indeed, food/wine pairings are traditionally published in some form of tabular chart where marks appear at the intersections of columns and rows representing compatible varieties of food and wine. The wine agent demonstrates that at least this simple task can be accomplished within semantic markup technology, but what about more complicated applications like, for example, those required by electronic health rcords (EHR)? For now, I'll postpone any discussion of EHR and stick with this food-wine pairing app.
Suppose that if not the entire web, then at least some number of cooperating parties were using semantic markup to participate in this project. Rather than the traditional approach of trying to build an enormous database of foods and wines, then, the definitions would be distributed across the participating parties. A restaurant or retailer offering an on-line menu could mark each food item with standardized machine-readable definitions. Similarly, then, a wine retailer could mark its wines according to the definitions exemplified above.
Such markings would benefit from well-known advantages of ontologies. For instance, through the subclassing, adding a new Pauillac to the inventory would not require wine.com (the source of the GUI shown in Figure III) to mark all of the wine's properties; it would just be another Pauillac as specified in the example, plus any differentiating features. But more importantly in terms of software agents, all the markings would be machine readable, and could be handled by systems from any organization.
Rather than relying on a human user to select a food or food type, the agent could crawl the web for foods marked within the wines namespace and pre-compile suitable pairings. This is where Web services (discussed in earlier posts) come in.
Footnote
The notion of agent-based computing has been adopted enthusiastically in the financial trading community, where autonomous market trading agents have been said to outperform human commodity traders by 7%.
Machines can monitor stock market movements much more quickly than humans, and if you can encode the kinds of rules that you want, then it is not unreasonable to imagine that computational traders will be able to outperform humans.
I commented on high-speed trading in my August 3 post.
