Unlike numerical preferences, preferences on at-tribute values do not show an inherent total order, but skyline computation has to rely on partial orderings explicitly stated by the user. In such orders many ob-ject values are incomparable, hence skylines sizes become unpractical. However, the Pareto semantics can be modified to benefit from indifferences: skyline result sizes can be essentially reduced by allowing the user to declare some incomparable values as equally desirable. A major problem of adding such equiva-lences is that they may result in intransitivity of the aggregated Pareto order and thus efficient query proc-essing is hampered. In this paper we analyze how far the strict Pareto semantics can be relaxed while al-ways retaining transitivity of the induced Pareto ag-gregation. Extensive practical tests show that skyline sizes can indeed be reduced about two orders of mag-nitude when using the maximum possible relaxation still guaranteeing the consistency with all user prefer-ences.