Semantics vs Models
December 24th, 2008
The idea of a semantic web or just in general providing semantics is essentially to augment data with additional data providing more information about what the original data is about (and standardise this to enable communication/interfacing). This is supposed to enable algorithms to process the original data better and in some sense understand that data. Calling this meta data seems to be justified in as far as that the additional data is actually not directly used or useful to humans, but it is intended for the algorithms. Humans can go quite far with interpreting data without this additional information, as far as their knowledge, experience and intelligence actually allows them to.
However, it seems that this means the data provider also has to provide all useful semantic information and that simply seems to be impossible. The data provider can provide this information for the data in the original context, but the data itself may have a far wider use. This would be missed by any algorithm as it would not reinterpret the data in a new, unknown context, but only use the original interpretation(s). More generally it seems to suggest a purely extrinsic notion of understanding independent of the observer. While semantics information may be useful in a very narrowly defined, specific context, a single project or precisely defined subject area, in a wider context it seems hardly achievable or useful.
Instead, a system which builds models (of any sort, not just statistically, even if statistical models seem to be important, especially for human interpretation) based on the data available seems to enable reinterpretation of the data and enable using it in different contexts. The resulting models derived from the data may then still be augmented with semantics information to make them accessible. This would base semantics on actual data instead of trying to make the data fit a particular view. But this is not always necessary or needed, depending simply on the use of the resulting models. Of course a certain bias can still be present depending on the type of models used. It also makes semantics more intrinsic, depending on the observer and how it/she/he builds the model. Many different models may even be build to explore concepts and these models may not be easily translatable into each other, if at all. Of course the question now becomes how to (reasonably quickly) build such models.
This is not just related to the semantic web, but also, say, to interpreting geometric models in the sense of describing it in terms that are meaningful to someone wishing to process (create, edit, analyse, etc.) the model. A fixed description of a model’s design intent in this context via history, geometric constraints, regularities, etc. seems to be restricting in a similar way than providing rather universal semantics to data on the web and does not allow for reinterpretation and with that reuse.
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