The accumulated literature base in the behavioral sciences represents the IS discipline’s greatest source of knowledge, and yet the same literature has grown beyond human comprehension. An experiment is conducted showing the inability of experts to retrieve relevant constructs using full-text search. To address this inability to access the body of theoretical behavioral science research we propose a novel IT artifact built on an information extraction approach to nomological network discovery. Based on the design science paradigm we develop a three-step process for extraction and assembly of nomological networks proceeding through article download, hypothesis extraction, variable extraction, and finally to variable integration. Rule-based vs. machine learning algorithms are evaluated and compared to determine the best approach for the extraction steps. A dataset of all the relevant behavioral studies from two top journals in Information Systems and Psychology is used to evaluate the approach in comparison to expert decisions, leading into a discussion of limitations and possible extensions.