SOFIA is an Information Extraction system whose main goal is to extract Causal Relations that are explicitly mentioned in text, in order to facilitate automatic causal analysis graph construction.
SOFIA extracts three major types of information: Entities, Events, and Causal-type Relations. All semantic units are important in order to build a coherent model useful for CAG construction. Entities include physical objects, people, organizations, etc., while events denote some actions, processes or changes of state. Entities are participants in events (e.g., the car moves), while events are arguments to relations like Causality (e.g., Reading causes Thinking).
The detected Events and Entities are currently grounded to SOFIA’s internal Ontology, We note that although the Ontology is subject to minor changes, we do not plan to change the Upper Level structure. Additional information provided by SOFIA includes Time, Location, Confidence Scores and Quantitative/Qualitative metrics of Entities.
Development of the
SOFIA Reader ended before a few key features were introduced to the World Modelers Machine Reading Pipeline. As a result, there are a number of caveats that must be taken into account in order to use
SOFIA to produce reader outputs. For more details, see the Sofia Workflows page.