document: On the potential of domain literature for clustering and Bayesian network learning Thanks to its increasing availability , electronic literature can now be a major source of information when developing complex statistical models where data is scarce or contains much noise . This raises the question of how to integrate information from domain literature with statistical data . Because quantifying similarities or dependencies between variables is a basic building block in knowledge discovery , we consider here the following question . Which vector representations of text and which statistical scores of similarity or dependency support best the use of literature in statistical models ? For the text source , we assume to have annotations for the domain variables as short free-text descriptions and optionally to have a large literature repository from which we can further expand the annotations . For evaluation , we contrast the variables similarities or dependencies obtained from text using different annotation sources and vector representations with those obtained from measurement data or expert assessments . Specifically , we consider two learning problems : clustering and Bayesian network learning . Firstly , we report performance ( against an expert reference ) for clustering yeast genes from textual annotations . Secondly , we assess the agreement between text-based and data-based scores of variable dependencies when learning Bayesian network substructures for the task of modeling the joint distribution of clinical measurements of ovarian tumors .
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