I really should not start reading Date over the weekend. It puts me in a relational frame of mind and I start thinking of explanations of topic maps in terms of the relational model.
For example, take his definition of:
First normal form: A relvar is in 1NF if and only if, in every legal value of that relvar, every tuple contains exactly one value for each attribute. (page 358)
Second normal form: (definition assuming only one candidate key, which we assume is the primary key): a relvar is in 2NF if and only if it is in 1NF and every nonkey attribute is irreducibly dependent on the primary key. (page 361)
Third normal form: (definition assuming only one candidate key, which we assume is the primary key): A relvar is in 3NF if and only if it is in 2NF and every nonkey attribute is nontransitively dependent on the primary key. (page 363)
Third normal form (even more informal definition): A relvar is in third normal form (3NF) if and only if, for all time, each tuple consists of a primary key value that identifies some entity, together with a set of zero or more mutually independent values that describe that entity in some way.
Does that mean that topic maps support ad hoc normalization? That is we don’t have to design in normalization before we start writing the topic map but can decide on what subjects need to be “normalized,” that is represented by topics that read to a single representative, after we have started writing the topic map.
Try that with a relational database and tables of any complexity. If you don’t get it right at the design stage, fixing it becomes more expensive as time goes by.
Not a “dig” at relational databases. If your domain is that slow changing and other criteria point to a relational solution, by all means, use one. Performance numbers are hard to beat.
On the other hand, if you need “normalization” an yet you have a rapidly changing environment that is subject to exploration and mappings across domains, you should give topic maps a hard look. Ask for “Ad Hoc Normalization” by name.
PS: I suspect this is what Lars Marius meant by Topic Maps Data Model (TMDM) 6. Merging, 6.1 General:
A central operation in Topic Maps is that of merging, a process applied to a topic map in order to eliminate redundant topic map constructs in that topic map. This clause specifies in which situations merging shall occur, but the rules given here are insufficient to ensure that all redundant information is removed from a topic map.
Any change to a topic map that causes any set to contain two information items equal to each other shall be followed by the merging of those two information items according to the rules given below for the type of information item to which the two equal information items belong.
But I wasn’t “hearing” “…eliminate redundant topic maps constructs…” as “normalization.”