One of the big canyons in the Semantic Abyss is how to compare concepts and sense their similarity or differences as well as their relations to other concepts. Sometimes a user can be laser-precise as to what concept is desired, but even then the user may not be aware that other concepts may be quite similar or related in some way. Sometimes it is desirable to treat very similar concepts as virtually identical, while other times it may be desirable merely to offer the user alternatives that might meet the desired objective. In any case, the starting point is to quantify the conceptual distance between concepts. As might be expected, that is likely to be much easier said than done.
Much of the existing research relates to determining conceptual distance of document from query terms, also known as document relevance. Here, the objective is to compare the terms or concepts themselves to determine how close they are and which are closest.
It is not clear if any absolute conceptual distance can be determined. Usually, a relative conceptual distance for a set of concepts is all that is needed, or maybe all that is possible.
Some of the reasons for comparing conceptual distances are to determine:
- equality (say, in a social sense)
- same as
It may be true that any given application or even a given user of an application may have different criteria for how close the conceptual distance must be to satisfy their needs. Control over the looseness or tightness of the fit is probably also desirable.
A big challenge of the Semantic Web is that different developers and communities have different conceptions of the meanings of concepts. Sometimes seemingly different terms are used to refer to what are logically similar or even identical concepts. This means that we need a sophisticated level of concept matching that can transparently handle the bridging of superficial semantic gaps, as well as to alert the user were semantic gaps exist that cannot be automatically bridged but maybe the user can manually accept them as if they were automatically bridged.
Another problem is that superficially identical concepts may in fact be quite distinct at a deeper semantic level so that the concept matching should reject them as matches. In the alternative, the user can be alerted to these false concept matches and maybe redefine a new set of concepts to effectively bridge the perceived semantic gaps so that matching is more semantically correct.
In any case, the ability of the software to give the user excellent feedback on conceptual distance is a very important tool.