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Syst Biol. 2016 Jul;65(4):561-82. doi: 10.1093/sysbio/syw023. Epub 2016 Mar 22.

Two Influential Primate Classifications Logically Aligned.

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School of Life Sciences, PO Box 874501, Arizona State University, Tempe, AZ 85287, USA;
School of Life Sciences, PO Box 874501, Arizona State University, Tempe, AZ 85287, USA;
Department of Biology, Bucknell University, 1 Dent Drive, Lewisburg, PA 17837, USA;
Department of Computer Science, 2063 Kemper Hall, 1 Shields Avenue, University of California at Davis, CA 95616, USA;
Department of Computer Science, 502 East Boone Avenue, AD Box 26, Gonzaga University, Spokane, WA 99258, USA;
Gradate School of Library and Information Science, 510 East Daniel Street, University of Illinois at Urbana-Champaign, Champaign, IL 61820.


Classifications and phylogenies of perceived natural entities change in the light of new evidence. Taxonomic changes, translated into Code-compliant names, frequently lead to name:meaning dissociations across succeeding treatments. Classification standards such as the Mammal Species of the World (MSW) may experience significant levels of taxonomic change from one edition to the next, with potential costs to long-term, large-scale information integration. This circumstance challenges the biodiversity and phylogenetic data communities to express taxonomic congruence and incongruence in ways that both humans and machines can process, that is, to logically represent taxonomic alignments across multiple classifications. We demonstrate that such alignments are feasible for two classifications of primates corresponding to the second and third MSW editions. Our approach has three main components: (i) use of taxonomic concept labels, that is name sec. author (where sec. means according to), to assemble each concept hierarchy separately via parent/child relationships; (ii) articulation of select concepts across the two hierarchies with user-provided Region Connection Calculus (RCC-5) relationships; and (iii) the use of an Answer Set Programming toolkit to infer and visualize logically consistent alignments of these input constraints. Our use case entails the Primates sec. Groves (1993; MSW2-317 taxonomic concepts; 233 at the species level) and Primates sec. Groves (2005; MSW3-483 taxonomic concepts; 376 at the species level). Using 402 RCC-5 input articulations, the reasoning process yields a single, consistent alignment and 153,111 Maximally Informative Relations that constitute a comprehensive meaning resolution map for every concept pair in the Primates sec. MSW2/MSW3. The complete alignment, and various partitions thereof, facilitate quantitative analyses of name:meaning dissociation, revealing that nearly one in three taxonomic names are not reliable across treatments-in the sense of the same name identifying congruent taxonomic meanings. The RCC-5 alignment approach is potentially widely applicable in systematics and can achieve scalable, precise resolution of semantically evolving name usages in synthetic, next-generation biodiversity, and phylogeny data platforms.


Alignment; Primates; Region Connection Calculus; classification; concept taxonomy; logic; ontology; reasoning

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