Category Archives: semantics

Ambiguity and identity

Patrick Durusau suggests that properties help identify a subject representative in topic maps. For example, he says that

… All naming conventions are subject to ambiguity and “expanded” naming conventions, such as a list of properties in a topic map, may make the ambiguity a bit more manageable.

That depends on a presumption that if more information is added and a user advised of it, the risk of ambiguity will be reduced. …

In another post, he writes:

… Topic maps provide the ability to offer more complete descriptions of subjects in hopes of being understood by others.

With the ability to add descriptions of subjects from others, offering users a variety of descriptions of the same subject. …

I have some doubts whether this description approach, as opposed to the naming approach (subject indicator and subject locator) now in place in topic maps, will work. IMO, adding more descriptions (by different people with different models of the world, opinions, values, perceptions …) increases the risk of introducing divergent viewpoints and thus runs counter to the original intent (to determine identity and reduce ambiguity). Adding more information is likely to make description and reference more ambiguous, as Pat Hayes and Harry Halpin have argued in their paper “In defense of ambiguity”. Common sense might tell you that the more you define something, pin it down, the more precise it becomes. But precision is not necessarily achieved by more descriptions – subject identification rather gets prone to ambiguity.

Some more questions about identity and disambiguation:

  • How do you decide that a description has added a dimension to a concept that makes that concept so different that it becomes “another” concept? Who draws the line (someone else might disagree)? Can we formulate criteria for this process, or is it arbitrary?
  • How can we handle time- and context-sensitive descriptions – properties that change over time or according to context? Does a property that changes still define identity, and does identity change too, then? Maybe there are “minor” and “major” properties – if a “major” property changes, does that entail a change of identity, whereas with the change of a “minor”, less typical property identity stays the same? How can either of these “property classes” be determined?

I wonder whether we fall back into the Aristotelian system by stressing description, attributes and properties. Or perhaps description and naming strategies in subject identification can coexist and complement each other.

Interoperability – pragmatic

While syntactic interoperability is a prerequisite for effective data interchange, a whole host of other factors such as the source of the data, the target group and use cases for which it was produced have to be taken into account when re-using pieces of data. Context influences the semantics of data elements and of their content (a certain concept can have different meanings in different knowledge communities). I’d call this level of interoperability “pragmatic”, drawing on a definition of pragmatics as a subfield of linguistics: it “studies the ways in which context contributes to meaning.”

How are data and data elements actually used in context, and used differently in different contexts? Identifiers and identity conditions may vary depending on context. What about incorrect or inconsistent use of data elements? And what is/are the underlying model(s) that enable applications to share data and interpret them correctly in the given domain? If, for example, we don’t agree on the concept “book” and its properties, how can we effectively share and make sense of data about it? Is data still as valuable when torn out of its original context (Linked Data)?

I suggest that data pragmatics could look at the way people use formats, vocabularies etc. in practice as opposed to a theoretical, top-down, prescriptive view. With the number of people creating and publishing data on the web growing at an unprecedented rate, in this “open world”, we are bound to end up with data of varying quality that may or may not be interoperable on all levels.