Situations and relations can be interpreted very differently by those involved and things can escalate easily.
The approximate relations can be interpreted from two aspects: the lower part (only including crisp relations about uncertain region) and the uncertain part (the uncertain directions about uncertain regions).
We develop a topic-driven initialization scheme, so that each learned relation type can be interpreted as a representative of semantic topics of the objects.
In this system, the types do not have orders, but Russell's ramified type theory with orders (complete with relation types) can be interpreted in it (a technical result of which we do not give an account here).
If states in a State Machine are interpreted by State Invariants, then the topological relations between its states can be interpreted as logical relations between the corresponding formulas.
In the situation of statistically non-significant study results, just the absolute and relative frequencies should be presented complemented by point and interval estimates of a relation effect measure, which can be interpreted easily in all situations, e.g. the risk ratio.
The relation of making probable, itself, can be interpreted in many different ways.
For example, the interpretation of the relation between education and hypoglycaemic events can be interpreted as following for the linear mixed model (Table 2): Higher educated diabetic patients have 1.14 more events than (on average over time) compared to lower educated diabetic patients.
These data can be interpreted in relation to current theories of hippocampal function, which emphasize the critical role played by the hippocampus in relational memory formation and retrieval.
