Semantic Web: – Interoperability,Usability,Applicability

Encyclopedia of Life (EOL) has developed TraitBank (http://eol.org/traitbank), a new repository for organism attribute (trait) data. TraitBank aggregates, manages and serves attribute data for organisms across the tree of life, including life history characteristics, habitats, distributions, ecological relationships and other data types. We describe how TraitBank ingests and manages these data in a way that leverages EOL’s existing infrastructure and semantic annotations to facilitate reasoning across the TraitBank corpus and interoperability with other resources. We also discuss TraitBank’s impact on users and collaborators and the challenges and benefits of our lightweight, scalable approach to the integration of biodiversity data.

Taxonomic knowledge provides a scientific name to each organismal group and is thus indispensable information for understanding biodiversity. However, the various perspectives of classifying organisms and changes in taxonomic knowledge have led to inconsistent classification information among different databases and repositories. To have a precise understanding of taxonomy, one needs to integrate relevant data across taxonomic databases. This is difficult to establish due to the ambiguity in taxon interpretation. Most researchers in earlier stages employed the Linked Open Data (LOD) technique to establish links in taxonomy transition. However, they overlooked the temporal representation of taxa and underlying knowledge of the change in taxonomy, so it is difficult for learners to gain perspective on how some identifiers of taxa are linked. To this end, this research is aimed at developing a model for presenting and preserving the change in taxonomic knowledge in the Resource Description Framework (RDF). Specifically, the proposed model takes advantage of linking Internet resources representing taxa, presenting historical information of taxa, and preserving the background knowledge of the change in taxonomic knowledge in order to enable a better understanding of organisms. We implement a prototype to demonstrate the feasibility and the performance of our approach. The results show that the proposed model is able to handle various practical cases of changes in taxonomic works and provides open and accurate access to linked data for biodiversity.

The Darwin Core vocabulary is widely used to transmit biodiversity data in the form of simple text files. In order to support expression of biodiversity data in the Resource Description Framework (RDF), a guide was created as a non-normative addition to the Darwin Core standard. This paper describes the major issues that were addressed in the creation of the guide, particularly problems related to adapting terms designed to have literal values for use with IRI references. By making it possible to express millions of existing records as RDF, the guide is an important step towards enabling the biodiversity informatics community to participate in broader Linked Data and Semantic Web efforts.

Darwin-SW (DSW) is an RDF vocabulary designed to complement the Biodiversity Information Standards (TDWG) Darwin Core Standard. DSW is based on a model derived from a community discussion about the relationships among the main Darwin Core classes. DSW creates a new class to accommodate an important aspect of its model that is not currently part of Darwin Core: a class of Tokens, which are forms of evidence. DSW uses Web Ontology Language (OWL) to make assertions about the classes in its model and to define object properties that are used to link instances of those classes. A goal in the creation of DSW was to facilitate consistent markup of biodiversity data so that RDF graphs created by different providers could be easily merged. Accordingly, DSW provides a mechanism for testing whether its terms are being used in a manner consistent with its model. Two transitive object properties enable the creation of simple SPARQL queries that can be used to discover new information about linked resources whose metadata are generated by different providers. The Organism class enables semantic linking of biodiversity resources to vocabularies outside of TDWG that deal with observations and ecological phenomena.

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 glomeratus-virginicus ‘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.