### Provenance for Explaining Taxonomy Alignments [chapter]

Mingmin Chen, Shizhuo Yu, Parisa Kianmajd, Nico Franz, Shawn Bowers, Bertram Ludäscher
2015 Lecture Notes in Computer Science
Derivations and proofs are a form of provenance in automated deduction that can assist users in understanding how reasoners derive logical consequences from premises. However, system-generated proofs are often overly complex or detailed, and making sense of them is non-trivial. Conversely, without any form of provenance, it is just as hard to know why a certain fact was derived. We study provenance in the application of Euler/X [1], a logic-based toolkit for aligning multiple biological
more » ... es. We propose a combination of approaches to explain both, logical inconsistencies in the input alignment, and the derivation of new facts in the output taxonomies. Taxonomy Alignment. Given taxonomies T 1 , T 2 and a set of articulations A, all modeled as monadic, first-order constraints, the taxonomy alignment problem is to find "merged" taxonomies that satisfy Φ = T 1 ∪ T 2 ∪ A. An alignment can be inconsistent (Φ is unsatisfiable), unique (Φ has exactly one minimal model), or ambiguous (Φ has more than one minimal model). For example, let T 1 be given by isa (subset) constraints b ⊆ a, c ⊆ a, coverage constraint a = b ∪ c, and sibling disjointness b ∩ c = ∅. Similarly, T 2 is given by isa constraints e ⊆ d, f ⊆ d, coverage d = e ∪ f, and sibling disjointness e ∩ f = ∅. An expert aligns T 1 and T 2 using articulations a = d, b e, c f, and b d; see Fig. 1 . We would like to "apply" all of these relations between the two taxonomies, and output a merged taxonomy.