LGNov 27, 2021
Learning from learning machines: a new generation of AI technology to meet the needs of scienceLuca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin et al.
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself.
DBJul 25, 2014
Aber-OWL: a framework for ontology-based data access in biologyRobert Hoehndorf, Luke Slater, Paul N. Schofield et al.
Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning. We have developed the Aber-OWL infrastructure that provides reasoning services for bio-ontologies. Aber-OWL consists of an ontology repository, a set of web services and web interfaces that enable ontology-based semantic access to biological data and literature. Aber-OWL is freely available at http://aber-owl.net.
AIFeb 16, 2012
Towards quantitative measures in applied ontologyRobert Hoehndorf, Michel Dumontier, Georgios V. Gkoutos
Applied ontology is a relatively new field which aims to apply theories and methods from diverse disciplines such as philosophy, cognitive science, linguistics and formal logics to perform or improve domain-specific tasks. To support the development of effective research methodologies for applied ontology, we critically discuss the question how its research results should be evaluated. We propose that results in applied ontology must be evaluated within their domain of application, based on some ontology-based task within the domain, and discuss quantitative measures which would facilitate the objective evaluation and comparison of research results in applied ontology.