Concordance matrices play a crucial role in input-output analysis, for translating between databases expressed in different sector classifications, or translating many misaligned databases into a common format. These matrices are critical tools in enabling the utilisation of all possible primary data sources for compiling input-output tables, even if those data sources are completely misaligned. Until this date, concordance matrices are constructed manually by interpreting pairwise sector labels, resulting in an often labour-intensive process. In this work, we use artificial intelligence (AI) approaches for the first time to estimate concordance matrices for input-output analysis, offering to significantly reduce the time and labour involved in primary data processing. We show that, when applying deep learning techniques to textual sector labels, AI algorithms are able to grasp intricate linguistic relationships and capture semantic nuances, thus bridging the gap between human language and numerical binary relationships. We use a range of performance evaluation measures and demonstrate the ability to predict a wide range of concordance matrices with up to 85% accuracy.
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