We present OpenBiodiv - an implementation of the Open Biodiversity Knowledge Management System.

The need for an integrated information system serving the needs of the biodiversity community can be dated at least as far back as the sanctioning of the Bouchout declaration in 2007. The Bouchout declaration proposes to make biodiversity knowledge freely available as Linked Open Data (LOD)*1. At TDWG 2016 (Fig. 1) we presented the prototype of the system - then called Open Biodiversity Knolwedge Management System (OBKMS) (Senderov et al. 2016). The specification and design of OpenBiodiv was then outlined in more detail by Senderov and Penev (2016). In this poster, we describe the pilot implementation. We believe OpenBiodiv is possibly the first pilot-stage implementation of a semantic system running on top of a biodiversity knowledge graph.

Figure 1.

High-level Architecture of OpenBiodiv.

OpenBiodiv has several components:

1. OpenBiodiv ontology: A general data model supporting the extraction of biodiversity knowledge from taxonomic articles or from databases such as GBIF. The ontology (in preparation, Journal of Biomedical Semantics, available on GitHub) incorporates several pre-existing models: Darwin-SW (Baskauf and Webb 2016), SPAR (Peroni 2014), Treatment Ontology, and several others. It defines classes, properties, and rules supporting the interlinking of these disparate ontologies to create a LOD biodiversity knowledge graph. A new addition is the Taxonomic Name Usage class, accompanied by a Vocabulary of Taxonomic Statuses (created via an analysis of 4,000 Pensoft articles) enabling for the automated inference of the taxonomic status of Latinized scientific names. The ontology supports multiple backbone taxonomies via the introduction of a Taxon Concept class (equivalent to DarwinCore Taxon) and Taxon Concept Labels as a subclass of biological name.
2. The Biodiversity Knowledge Graph: A LOD dataset of information extracted from taxonomic literature and databases. To date, this resource has realized part of what was proposed during the pro-iBiosphere project and later discussed by Page (2016). Its main resources are articles, sub-article componets (tables, figures, treatents, references), author names, institution names, geographical locations, biological names, taxon concepts, and occurrences. Authors have been disambiguated via their affiliation with the use of fuzzy-logic based on the GraphDB Lucene connector. The graph interlinks: (1) Prospectively published literature via Pensoft Publishers. (2) Legacy literature via Plazi. (3) Well-known resources such as geographical places or institutions via DBPedia. (4) GBIF's backbone taxonomy as a default but not the preferential hierarchy of taxon concepts. (5) OpenBiodiv id's with nomenclator id's (e.g. ZooBank) whenever possible. Names form two networks in the graph: (1) A directed-acyclical graph (DAG) of supercedence that can be followed to the corresponding sinks to infer the currently applicable scientific name for a given taxon. (2) A network of bi-directional relations indicating the relatedness of names. These names may be compared to the related names inferred on the basis of distributional semantics (Nguyen et al. 2017).
3. ropenbio: An R package for RDF*2-ization of biodiversity information resources according to the OpenBiodiv ontology. We intend to submit this to the rOpenSci project. While many of its high-level functions are specific to OpenBiodiv, the low-level functions, and its RDF-ization framework can be used for any R-based RDF-ization effort.
4. OpenBiodiv.net: A front-end of the system allowing users to run low-level SPARQL queries as well to use an extensible set of semantic apps running on top of a biodiversity knowledge graph.

Linked Open Data, R package, RDF, SPARQL, Biodiversity Knowledge Graph, Semantic web, Semantic publishing, inference, Artificial Intelligence, Text Mining

Viktor Senderov and Teodor Georgiev