Abstract

Automated discovery systems can formulate and revise hypotheses by gathering and analyzing data. In order to generate new hypotheses and provide explanations of their new findings, these systems need a language to represent hypotheses, their revisions, and their provenance. This paper describes the DISK hypothesis ontology which fulfills these requirements. The paper then presents a survey of existing models for representing hypotheses along with their features and tradeoffs. We compare these hypothesis models in the context of automated discovery and hypothesis evolution.

Date

2017

Authors

Daniel Garijo, Yolanda Gil, Varun Ratnakar

Conference

K-CAP Workshops

Pages

40-46