Harnessing the Web and Knowledge Graphs for Automated Impact Investing
  Scoring
Qingzhi Hu Daniel Daza Laurens Swinkels Kristina Ūsaitė Robbert-Jan 't Hoen Paul Groth
The Sustainable Development Goals (SDGs) were introduced by the United
Nations in order to encourage policies and activities that help guarantee human
prosperity and sustainability. SDG frameworks produced in the finance industry
are designed to provide scores that indicate how well a company aligns with
each of the 17 SDGs. This scoring enables a consistent assessment of
investments that have the potential of building an inclusive and sustainable
economy. As a result of the high quality and reliability required by such
frameworks, the process of creating and maintaining them is time-consuming and
requires extensive domain expertise. In this work, we describe a data-driven
system that seeks to automate the process of creating an SDG framework. First,
we propose a novel method for collecting and filtering a dataset of texts from
different web sources and a knowledge graph relevant to a set of companies. We
then implement and deploy classifiers trained with this data for predicting
scores of alignment with SDGs for a given company. Our results indicate that
our best performing model can accurately predict SDG scores with a micro
average F1 score of 0.89, demonstrating the effectiveness of the proposed
solution. We further describe how the integration of the models for its use by
humans can be facilitated by providing explanations in the form of data
relevant to a predicted score. We find that our proposed solution enables
access to a large amount of information that analysts would normally not be
able to process, resulting in an accurate prediction of SDG scores at a
fraction of the cost.