Conceptual Space Markup Language (CSML)

I've recently come across a great paper by Benjamin Adams and Martin Raubal called Conceptual Space Markup Language (CSML): Towards the Cognitive Semantic Web. I found their paper interesting on many levels because it lies at the nexus of many rather diverse topics that I’m interested in. CSML directly involves Computational Geometry and the Semantic Web, but indirectly involves the philosophy of meaning, mind, and color. Also, as it grows in popularity, I believe force-based organizational algorithms and neural networks will become a heavily used mechanism for generating and using CSML data. Basically, CSML takes Conceptual Spaces, which are already at an interesting intersection of multiple mind sciences and multiple strands of philosophy, and connects it with multiple threads of engineering and informatics.

Wait.
What are Conceptual Spaces?

Conceptual Spaces are multidimensional spaces made up of quality dimensions. Quality dimensions are basically just any property you can think of that has a (pseudo-) continuous range of values. Think: size, mass, brightness, beauty, craziness, unicornity or anything else that you think you can make sense of on some sort of numerical range. You can now consider points and (convex) shapes within your set of quality dimensions, which will correspond to concepts. Consider the classic-cool-colour-cone example to the lower right. That’s a representation of a conceptual space with quality dimensions of hue, value, and saturation. 

But this isn’t just a fancy mathematical model. Structures like this are in some sense actually “held” by neurons in your brain. You may have heard people talk about "levels of reality" (or "levels of description of reality") such as the physical level, biological level, psychological level, sociological level, etc. Well, the idea is that when you look into the brain, which is (of course) very complex, you can look at it "at different levels" (presumably of granularity in this case). Conceptual Spaces are one of those lesser-known but very useful levels between the neuronal level and the psychological level (and below the symbolic level if you think there is one). Other people have much more comprehensive and informative explanations that I’m not going to try to repeat here. If you’re interested in the multitude of potential philosophical implications check out Paul Chruchland’s State-Space Semantics and Meaning Holism. (He’s also got some good stuff to say about colors outside of the classic-cool-color-cone here.)

CSML describes Conceptual Spaces

CSML, the Conceptual Space Markup Language, is an XML serialization of conceptual spaces. CSML brings a whole new engineering dimension to conceptual spaces which fits into the realm of Semantic Web technologies and is actually analogous to OWL, but with radically different implications. In my previous post, which was actually written in June 2008, I dreamed of a “smooth semantic web” that didn’t always require rigid categorization. CSML looks like a better candidate to handle that kind of data. CSML is specifically designed to handle context-dependent meaning and (relative) similarity of concepts, which are both difficult to handle in OWL. (How would you represent a large squirrel and a tiny planet consistently? What about brightness, beauty, craziness, and unicorniness?) After trying to use a few units ontologies for measurement data (at iCAPTURE for Mark Wilkinson) I also have high hopes that CSML can help simplify the problems on that front.

The most exciting part for me is that conceptual spaces lend themselves to fancy techniques for being automatically generated by way of artificial neural networks and force-based organizational algorithms, which brings in a few more of the theoretical engineering topics I’ve been interested in over the years. Starting with similarity data, force-based (or tension reduction) algorithms could help identify quality dimensions. Neural networks can nicely use quality dimension coordinates as input and also learn to precisely place items into conceptual spaces. I can’t wait to see the tools that will be created to allow for generating, reasoning over, and visualizing CSML data and how they will integrate with existing semantic web technologies and machine learning techniques.

Getting rough data into the semantic web

It's nice when regular relational databases, excel spreadsheets, and other sources that people have created can be attached to the semantic web. But there are problems with the way most data is recorded, especially for data about time. In short, the problem is that people round values. This may seem like a trivial unimportant problem, but I don't think so if you want to be able to use the rough data that makes up most of the world's data. Here are the issues:

1) Datatype granularity. Datatypes allow for a wide range of possible values, but not all values. We may need to know in some situations whether the data value written is an exact match to the value that was intended. For instance, 1/3 can't be represented fully as a double and 3pm today can't be represented as a date without dropping the 3pm part.

2) Granularity used vs datatype granularity. This is a much bigger problem than (1). People often write things to a granularity that is much less fine grained than the datatype granularity. For instance, you may be recording distance to 2 decimal places and storing it as a float. This needs to be taken into account for situations where values are compared. We don't want to say that two things have the same height just because they are almost the same height. Also, people round times to the minute, 5 minutes, 10 minutes, 15 minutes, hour, and many other ways. Are they rounding down? Are they rounding to the nearest? Some other recording method?

An ontology that has commonly used recording methods to describe the data recording process would allow more justified inferences about the data to be made. This would allow a higher level of trust (in the second sense, explained here) when using the data which is going to be of great importance as the semantic web grows.