KaBOB: ontology-based semantic integration of biomedical databases

Abstract

Background: The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources.

Results: We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license.

Conclusions: KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

Figure 1

KaBOB Construction. Depicts the incremental construction of KaBOB. Labeled arrows represent processes that flow from inputs to outputs. Construction starts with downloading files and flows through translating them into RDF and then iteratively querying and producing more RDF. Steps marked with ** involve multiple sets of rules being run and their output loaded in sequence.

Figure 2

Example ICE Records and corresponding BIO Concepts. Depicts an excerpt of the knowledge representation in KaBOB. Ovals are used to depict instances, and rectangles classes. Single line arrows represent triples and point from their subject to their object and are labeled with their property. The iao:denotes links that cross from the ICE to the BIO side are emphasized with dashed arrows. The double arrows are shorthand for representing an owl:Restriction on the given property with some values from the object value. This figure depicts two GO annotation records that are then converted to biomedical concepts using the same rule (rule not depicted). Additionally sets of gene identifiers are also depicted that denote their corresponding gene concept. On the BIO side the relations between genes, proteins, and gene or gene product aggregate classes are also shown. Other than the records and their field values, generated by the file parsers, all other links are the output of applying rules.