The vertebrate taxonomy ontology: a framework for reasoning across model organism and species phenotypes

Midford, Peter E.; Dececchi, T. Alexander; Balhoff, James P.; Dahdul, Wasila; Ibrahim, Nizar; Lapp, Hilmar; Lundberg, John G.; Mabee, Paula M.; Sereno, Paul C.; Westerfield, Monte; Blackburn, David C.

doi:10.1186/2041-1480-4-34

Cited by 43 publications

(49 citation statements)

References 11 publications

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“…One option is to assemble single, comprehensive taxonomies for particular groups, with periodically updated versions [10,66,79]. This approach offers an immediate and valuable service to users.…”

Section: Names As Identifiers Of Taxonomic Meaningschallenges and Solmentioning

confidence: 99%

“…The logic foundations for this particular approach were developed in [16,43,86]. The step of modeling an input taxonomy as an is_a hierarchy is well established [37,66,89]. However, the remaining steps in our toolkit workflow diverge from existing ontology matching or provenance-tracking applications [18,21,24,52,80,89,92,97].…”

Section: Relationship Of the Rcc-5 Multi-taxonomy Alignment Approach mentioning

confidence: 99%

“…Other taxonomically focused integration projects that do not utilize RCC-5 include [10,13,66,73,88]. The degree to which the RCC-5 alignment approach is relevant to 3 A MIR is the unique node in a given R 32 lattice that implies all other true articulations in the lattice.…”

Section: Relationship Of the Rcc-5 Multi-taxonomy Alignment Approach mentioning

confidence: 99%

“…We adopt the view that taxonomic names and nomenclatural relationships are necessary but not sufficient for integrating biodiversity data for semantic information environments Web [5,35,58,73]. The reasons for this insufficiency are systemic and well known to taxonomy contributors and users [3,10,66,74]. Ultimately they are rooted in the way in which identity is established according to the rules of nomenclature that guide the application of names to perceived taxonomic groups [29,51,64,96].…”

Section: Names As Identifiers Of Taxonomic Meaningschallenges and Solmentioning

confidence: 99%

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Names are not good enough: Reasoning over taxonomic change in the Andropogon complex1

Franz

Chen

Kianmajd

et al. 2016

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Abstract. We present a novel, logic-based solution to the challenge of reconciling the meanings of taxonomic names across multiple biological taxonomies. The challenge arises due to limitations inherent in using type-anchored taxonomic names as identifiers of granular semantic similarities and differences being expressed in original and revised taxonomic classifications. We address this challenge through: (1) the use of taxonomic concept labels -thereby individuating name usages according to particular sources and allowing each taxonomy to be recognized separately; (2) sets of user-provided Region Connection Calculus articulations among concepts (RCC-5: congruence, proper inclusion, inverse proper inclusion, overlap, exclusion); and (3) the use of an Answer Set Programming-based reasoning toolkit that ingests these constraints to infer and visualize consistent multi-taxonomy alignments. The feasibility of this approach is demonstrated with a use case involving pairwise alignments of 11 non-congruent classifications of Eastern United States grass entities variously assigned to the Andropogon glomeratusvirginicus 'complex' over an interval of 126 years. Analyses of name:meaning identity reveal that, on average, taxonomic names are reliable identifiers of taxonomic non-/congruence for approximately 60% of the 127 merge regions obtained in 12 pairwise alignments. The name:meaning cardinality over the entire time interval ranges from 1:6 to 4:1, with only 1:36 names attaining the semantically ideal 1:1 ratio. We discuss the applicability of the RCC-5 alignment approach in the context of achieving logic-based integration of non-/congruent taxonomic concept hierarchies in dynamic biodiversity data environments.

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Section: Names As Identifiers Of Taxonomic Meaningschallenges and Solmentioning

confidence: 99%

Section: Relationship Of the Rcc-5 Multi-taxonomy Alignment Approach mentioning

confidence: 99%

Section: Relationship Of the Rcc-5 Multi-taxonomy Alignment Approach mentioning

confidence: 99%

Section: Names As Identifiers Of Taxonomic Meaningschallenges and Solmentioning

confidence: 99%

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Names are not good enough: Reasoning over taxonomic change in the Andropogon complex1

Franz

Chen

Kianmajd

et al. 2016

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“…So far, the community has mostly considered the SW standards through their most salient manifestation: the Web of Linked Data (Heath and Bizer 2011). Indeed, the 5-star Linked Data principles are geared towards the building of a large, distributed knowledge graph that may successfully fulfill biodiversity's need for interoperability and data integration.…”

mentioning

confidence: 99%

Modelling Biodiversity Linked Data: Pragmatism May Narrow Future Opportunities

Michel¹,

Faron-Zucker²,

Tercerie³

et al. 2018

BISS

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As the biodiversity community increasingly adopts Semantic Web (SW) standards to represent taxonomic registers, trait banks or museum collections, some questions come up relentlessly: How to model the data? For what goals? Can the same model fulfill different goals? So far, the community has mostly considered the SW standards through their most salient manifestation: the Web of Linked Data (Heath and Bizer 2011). Indeed, the 5-star Linked Data principles are geared towards the building of a large, distributed knowledge graph that may successfully fulfill biodiversity’s need for interoperability and data integration. However, the SW addresses a much broader set of problems involving automatic reasoning. For instance, reasoners can exploit ontological knowledge to improve query answering, leverage class definitions to infer class subsumption relationships, or classify individuals i.e. compute instance relationships between individuals and classes by applying reasoning techniques on class definitions and instance descriptions (Shearer et al. 2008). Whether a "thing" should be modelled as a class or a class instance has been debated at length in the SW community, and the answer is often a matter of perspective. In the context of taxonomic registers for example, the NCBI Organismal Classification (Federhen 2012) and Vertebrate Taxonomy Ontology (Midford et al. 2013) represent taxa as classes in the Ontology Web Language (OWL). By contrast, other initiatives represent taxa as instances of various classes, e.g. the SKOS Concept class (skos:Concept) in the AGROVOC thesaurus (Caracciolo et al. 2013) (we speak of the instances as SKOS concepts), the Darwin Core taxon class (dwc:Taxon) in Encyclopedia of Life (Parr et al. 2016), or classes depicting taxonomic ranks in GeoSpecies, DBpedia and the BBC Wildlife Ontology. Such modelling discrepancies impede linking congruent taxa throughout taxonomic registers. Indeed, one can state the equivalence between two classes (with owl:equivalentClass) or two class instances (with owl:sameAs, skos:exactMatch, etc.), but good practices discourage the alignment of classes with class instances (Baader et al. 2003). Recently, Darwin Core's popularity has fostered the modeling of taxa as instances of class dwc:Taxon (Senderov et al. 2018, Parr et al. 2016). In this context, pragmatism may incline a Linked Data provider to comply with this majority trend to ensure maximum interlinking. Although technically and conceptually valid, this choice entails certain drawbacks. First, considering a taxon only as a an instance misses the fact that it is a set of biological individuals with common characteristics. An OWL class exactly captures this semantics through the set of necessary and sufficient conditions that an individual must meet to be a class member. In turn, an OWL reasoner can leverage this knowledge to perform query answering, compute subsumption or instance relationships. By contrast, taxa depicted by class instances are not defined but described by stating their properties. Hence the second drawback: unless we develop bespoke reasoners, there is not much a standard OWL reasoner can deduce from instances. Yet, some works have demonstrated the effectiveness of logic representation and reasoning capabilities, e.g. computing the alignments of two primate classifications (Franz et al. 2016) using generic reasoners that nevertheless require proprietary input formats. OWL reasoners are typically designed to solve such classification problems. They may leverage taxonomic ontologies to compute alignments with other ontologies or apply reasoning to individuals' properties to infer their species. Hence, pragmatically following the instance-based approach may indeed maximize interlinking in the short term, but bears the risk of denying ourselves potentially desirable use cases in the longer term. We believe that developing class-based ontologies for biodiversity should help leverage the SW’s extensive theoretical and practical works to tackle a variety of use cases that so far have been addressed with bespoke solutions.

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Using the phenoscape knowledgebase to relate genetic perturbations to phenotypic evolution

Manda

Balhoff

Lapp

et al. 2015

Genesis

Self Cite

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Summary: The abundance of phenotypic diversity among species can enrich our knowledge of development and genetics beyond the limits of variation that can be observed in model organisms. The Phenoscape Knowledgebase (KB) is designed to enable exploration and discovery of phenotypic variation among species. Because phenotypes in the KB are annotated using standard ontologies, evolutionary phenotypes can be compared with phenotypes from genetic perturbations in model organisms. To illustrate the power of this approach, we review the use of the KB to find taxa showing evolutionary variation similar to that of a query gene. Matches are made between the full set of phenotypes described for a gene and an evolutionary profile, the latter of which is defined as the set of phenotypes that are variable among the daughters of any node on the taxonomic tree. Phenoscape's semantic similarity interface allows the user to assess the statistical significance of each match and flags matches that may only result from differences in annotation coverage between genetic and evolutionary studies. Tools such as this will help meet the challenge of relating the growing volume of genetic knowledge in model organisms to the diversity of phenotypes in nature. The Phenoscape KB is available at http://kb.phenoscape. org. genesis 53:561-571, 2015. V C 2015 Wiley Periodicals, Inc.

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The vertebrate taxonomy ontology: a framework for reasoning across model organism and species phenotypes

Cited by 43 publications

References 11 publications

Names are not good enough: Reasoning over taxonomic change in the Andropogon complex1

Names are not good enough: Reasoning over taxonomic change in the Andropogon complex1

Modelling Biodiversity Linked Data: Pragmatism May Narrow Future Opportunities

Using the phenoscape knowledgebase to relate genetic perturbations to phenotypic evolution

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