Phenoscape KB

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The Phenoscape Knowledgebase (KB) is an integrated dataset, and software infrastructure and services, bringing together evolutionary data matrices annotated by Phenoscape curators, model organism phenotype and gene expression annotations, and a collection of ontologies providing a common semantic framework. The entire dataset is expressed in the OWL Web Ontology Language, and, when used as a database, stored in an RDF triplestore.

This page describes the in-development version of the KB built on OWL and RDF technologies, not the legacy Phenoscape Knowledgebase built on OBD.

Data sources

Comparative data


Model organism data


Gene expression and genetic phenotype annotations for zebrafish (Danio rerio). ZFIN data downloads can be found at


Gene expression and genetic phenotype annotations for mouse (Mus musculus). MGI data downloads can be found at

  • Gene identifiers and names
  • Gene expression annotations
    • Custom report provided by Terry Hayamizu. We are working with MGI to establish continually updated downloadable reports.
  • Genetic phenotype annotations
    • Custom report provided by Terry Hayamizu. We are working with MGI to establish continually updated downloadable reports.


Gene expression and genetic phenotype annotations for frog (Xenopus laevis and X. tropicalis). Xenbase data downloads can be found at


Genetic phenotype annotations for human (Homo sapiens). HPO data downloads can be found at


Production tools

All of the inputs to the Phenoscape KB are processed to create a coherent semantic model in OWL. For OWL-based ontology files, processing may be minimal. For input datasets, we have written conversion tools which generate OWL-formatted data from input tables.

Phenoscape OWL tools

This project provides a collection of Scala classes which implement various ontology manipulations, as well as OWL converters for the various data formats used by our data providers.

KB build process

The Knowledgebase is built using a Scala script within the Phenoscape OWL tools codebase. It orchestrates coordinates OWL conversions and reasoning tasks. The result is a set of OWL files, constituting the Phenoscape KB, which can be loaded together into an RDF triplestore.

An overview of specific components of the KB build process is available.

Reasoning process

The OWL ontologies and data files constituting the Phenoscape KB are ultimately loaded into an RDF triplestore for use as a database back-end for the KB web application. The SPARQL query language provides a natural interface to semantic web data; however, support for query-time reasoning within RDF triplestores is severely limited in both performance (in the face of large, complex ontologies) and OWL expressivity. Further, while SPARQL and RDF are perfectly "aligned" with OWL instance data and property assertions, queries that require direct manipulation of the RDF serialization implementation details of OWL class expressions and complex axioms are over-complicated, error-prone, and bound to result in possibly faulty attempts to embed OWL reasoning logic into the structure of the query.

These considerations have contributed to a number of guiding principles for the production of OWL data for the KB:

  • Impedance mismatch between SPARQL and OWL must be considered when modeling data for SPARQL query.
    • Any logical inferences which we wish to make use of in the web application interface must be precomputed and asserted into the triplestore knowledgebase.
    • SPARQL queries should be able to obtain the desired results by making use of only named classes; it should not be necessary to describe OWL class expressions via SPARQL triple patterns.
    • Owlet is available as a workaround for describing anonymous classes to be used in a SPARQL query.
  • Currently available OWL reasoners provide dramatically better performance when computing an inferred class hierarchy vs. making inferences about OWL individuals. This is evident even with the very high performance ELK reasoner for the OWL EL profile. For this reason data are modeled in such a way that class axioms can be extracted from datasets, combined with ontologies and reasoned over separately, then merged back with datasets containing OWL individuals.
  • At the scale of the Phenoscape KB, the ELK reasoner is nearly always the only reasoner capable of producing a timely result (a few minutes vs. an uncounted number of days) for reasoning use cases. Even dividing up the dataset into smaller pieces that can be reasoned independently does not seem to be worth it; the size of the terminology is still quite large and it is difficult to separate pieces that are known to not logically interact.
    • For this reason most modeling/reasoning expectations must fall within the OWL EL profile. We have developed some programmatic workarounds to compute a hierarchy of absence phenotypes. TODO—describe
  • TODO property normalization
  • TODO memory requirements

Database and web application

The RDF output of the production script is loaded into an RDF triplestore. We are currently using the Bigdata triplestore. In selecting an RDF triplestore for the KB, we have a few basic requirements:

  • SPARQL endpoint.
  • Support for SPARQL 1.1 query language. This is required for aggregates such as "COUNT". "Property paths" also provide basic transitivity reasoning which can be useful for ad hoc queries, even if not performant enough for use in queries from the web application.
  • Embedded full-text index available within SPARQL queries.

Nice features (not required but possibly needed for certain interface features):

  • Concise bounded description mode for SPARQL DESCRIBE queries. When blank nodes are included in a DESCRIBE result, this recursively describes them until the graph terminates in named nodes in all directions. This is useful for grabbing the necessary and sufficient RDF graph needed to reconstruct OWL class expressions.