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MachineProse: an Ontological Framework for Scientific Assertions

Affiliation of the authors: Department of Computer Science and Electrical Engineering, School of Computing & Engineering, University of Missouri-Kansas City, Kansas City, MO.

Correspondence and reprints: Deendayal Dinakarpandian, MD, PhD, Department of Computer Science and Electrical Engineering, School of Computing & Engineering, University of Missouri-Kansas City, Kansas City, MO 64110; e-mail: <dinakard{at}umkc.edu>.

Received for publication: 07/14/05; accepted for publication: 10/16/05.

Objective: The idea of testing a hypothesis is central to the practice of biomedical research. However, the results of testing a hypothesis are published mainly in the form of prose articles. Encoding the results as scientific assertions that are both human and machine readable would greatly enhance the synergistic growth and dissemination of knowledge.

Design: We have developed MachineProse (MP), an ontological framework for the concise specification of scientific assertions. MP is based on the idea of an assertion constituting a fundamental unit of knowledge. This is in contrast to current approaches that use discrete concept terms from domain ontologies for annotation and assertions are only inferred heuristically.

Measurements: We use illustrative examples to highlight the advantages of MP over the use of the Medical Subject Headings (MeSH) system and keywords in indexing scientific articles.

Results: We show how MP makes it possible to carry out semantic annotation of publications that is machine readable and allows for precise search capabilities. In addition, when used by itself, MP serves as a knowledge repository for emerging discoveries. A prototype for proof of concept has been developed that demonstrates the feasibility and novel benefits of MP. As part of the MP framework, we have created an ontology of relationship types with about 100 terms optimized for the representation of scientific assertions.

Conclusion: MachineProse is a novel semantic framework that we believe may be used to summarize research findings, annotate biomedical publications, and support sophisticated searches.