The Need for a Prescriptive Ontology

A great deal of effort is invested in universities, research labs and companies to create prescriptive ontologies. Just think about large-scale project such as Cyc/OpenCyc or smaller projects build around OWL.

I use the term “prescriptive” to emphasize the fact that ontologies are usually defined in a hard-coded and formal manner. Let’s use the “Hotel” type, for example. Elements of this hypothetical ontology are capitalized and relations are in brackets:


| Hotel <is a> Building

| Hotel <is located in> City, State/Province, Country

| Hotel <is located near> Attractions

| Hotel <offers service> Parking, Pool, Gym, InternetAccess, etc.

| Hotel <has parts> HotelRoom

| HotelRoom <price> MoneyQuantity

| HotelRoom <rent> TimePeriod


The majority of semantic Web developers would agree this ontology is quite handy in the development of a hotel-related-semantic-web-2.0 application. But is it always handy? For how long? And is it really necessary?

Is it always handy?

Is this prototypal Hotel representative of all hotels? Clearly not. What about an ice hotel that melts (we would need a start and end date, as we do with an event)? What about cultural, local and special services (e.g., pet care, special shuttles, places of worship)? We can argue that there will always be a hotel with atypical characteristics.

For how long?

How long can these relations remain valid and when will new relations develop? Take the smart phone example. The first ontology for portable phones probably had no place for features such as “mail client, Internet browser, music player.” What about the recent trend of “boutique hotels?” Does our ontology represent it? What modifications must be made now and in the future?

Is it really necessary?

That’s the real question. Are prescriptive ontologies really necessary? What if we try to develop a semantic Web application without such an ontology? Could a descriptive/soft/bottom-up/empirical ontology be sufficient?

To return to the original scenario, let’s imagine an information extraction system that crawls the Web to try to fill the Hotel template for “City, Attractions and Parking.” Using prescriptive ontology, we can literally attach a pattern to these slots and hope it will work well. With of the help of good programmers, we can be sure the problem will be elegantly resolved, and with high accuracy. The advantage here is the predictability of a template-filling task. The disadvantage is the ontology’s incomplete nature and the maintenance it requires over time. Chances are that new features will simply go unnoticed, and this new ice hotel with sleigh-only parking will not be adequately represented in the current model.

In machine learning, the idea of a descriptive ontology and that of clustering are analogous. Instead of starting with a sharp definition of the world, we invest the time of our good programmers in identifying pages on hotels and cluster information in order to find typical patterns. Not surprisingly, the word “parking” would appear with common co-occurring words such as “not available,” “free,” and “$25 a day.” Moreover, other named entities such as city, museum and monument would also co-occur frequently. We can imagine quickly generating a template containing these frequent and distinctive elements. As time goes by, new features may become prominent, indicating that maintenance is required. The advantage of this empirical ontology is the boundary-free description of entities relations. The disadvantage is a higher noise potential and conceptual drifting that would require manual post-edition.

Recent interest and successes in unsupervised learning techniques suggest the second option, or a combination of both options, is viable and promising.




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