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The differences between subject identifiers, item identifers, and subject locators

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This article describes the differences between subject identifiers, item identifiers, and subject locators in the TMDM.

Subject locators

Subject locators are used when you want to refer to resource that is the subject. If you want to make statements about the website (not the company) of Microsoft, you’ll use http://www.microsoft.com/ as subject locator. That means, the topic which contains the subject locator represents the website with that particular IRI. Example in CTM:

= http://www.microsoft.com/
       isa website;
       - "Website of the company Microsoft".

Subject identifiers

Subject Identifiers are used when you want to refer to a resource that describes a particular subject. If you want to make statements about the month “January”, you can, for example, use the IRI http://psi.semagia.com/datetime/January. That resource describes the subject “January” and provides a unique identifier for the month. Example in CTM:

     isa month;
     - "January".

Item identifiers

Item identifiers do not define a “strong” identity (like subject locators and subject identifiers do). They can be used to identify all kinds of Topic Maps constructs (i.e. names, occurrences, topics) within a topic map. If you, state in a CTM source:

merlyn isa person.

merlyn and person are local identifiers which are converted into item identifiers. But these identifiers are very weak, they cannot be used to identify the person “Merlyn” uniquely. You’ll need a subject identifier for that purpose. I.e. if the users Alice and Bob want to exchange information about a person named “Merlyn”, they shouldn’t use item identifiers but subject identifiers, otherwise it’s impossible to detect if they are referring to the same person or to different persons.

More information about subject identifiers, subject locators and item identifiers provides the Topic Maps Data Model.

Authors of this document are

Lars Heuer

No contact information available. 




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TMDM is the abbreviation for Topic Maps Data Model. You will find a compact illustration of all relationships within the TMDM


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As a former information scientist, I am fascinated since 1999 by the capabilities for building Topic Maps-based knowledge systems having the potential to augment human mind. One can model arbitrary knowledge organization systems, deal with semantic heterogeneity, collocate all facts about one subject in one logical place, and with TMQL have semantic retrieval on federated semantic networks. Therefore I expect bright prospects for business concepts building on the exchange of such knowledge snippets via semantic knowledge services.

Alexander Sigel
Topic Maps Lab auf der Cebit 2011

Graduate from the Topic Maps Lab