## Appendix A Additional Experiments
### Lower Back Pain Management Guidelines  
In this experiment we used the summary tables on \"clinical practice guidelines for the management of non-specific low back pain in primary care\" from [13]. In the cited paper, several comparisons are made between 15 clinical practice guidelines from multiple continents and countries (Africa (multinational), Australia, Brazil, Belgium, Canada, Denmark, Finland, Germany, Malaysia, Mexico, the Netherlands, Philippine, Spain, the USA and the UK). In our experiments we used all of those for which an English text was available: (GER) [61], (MAL) [62], (SPA) [63], (UK) [64], (AUS) [65], (USA) [66], (CAN) [67], (DEN) [68], and (BEL) [69]. For this total of nine guideline texts, we experimented with Table 1 (describing methodologies for diagnosis) and Table 2 (treatment recommendations) from the article [13] containing, respectively, 12 and 60 features; in addition, we created a super-table combining the two tables, and applied our method to it as well. With the same process as described in Section 3 we converted the Tables 1 and 2 of [13] into Jaccard distances. Then, as before, we computed the distortion between the graphs of the full text and the graphs of distances between the extracted features; and, as before, we established that the probability of obtaining high similarity by chance is extremely small. For Table 1 of [13] our best model BioAsq_WMD produced about 28% distortion (or 72% similarity). Similar results hold for Table 2 of and for the combined table, although the actual distortion numbers differ. In all cases, for the model BioAsq_WMD we found about 10-fold standard deviation, with distortion of about 16% for Table 2 and about 14% for the aggregated tables combining Tables 1 and 2 of [13]. All other models used in Table 6 performed in line with the previous results, with the only exception being the conceptualized models for Table 1 of [13], where for Conceptualized_CosD and Conceptualized_CosD' the distortion was slightly worse than random. We do not have an explanation for this sub-par performance, but we have seen a relatively weak performance of this model in Table A1. Table A3 shows the Jaccard distances and Table A4 shows the performance of all models on the combined table. Thus the performance of the model does not seem to degrade with a large number of comparisons. and Irish guidelines; CHEP2009 [77], the 2009 Canadian Hypertension Education Program; AAP [75], 2017 guidelines focusing on children and adolescents; and JNC [76], the 2014 evidence-based guidelines focusing on adults. Because we are not aware of any tabular summary of differences between hypertension guidelines, similar to the one shown earlier in Figure 1, we made the comparisons between full texts of the guidelines and their abstracts. That is, we created two graphs of embeddings, as shown in Figure A1, and measured their similarity, as well as the probability of the similarity arising by chance, as shown in Table A5. The experiment shows that the concepts appearing in the abstracts of the guidelines strongly correlate with the concepts used the full texts of the guidelines. Moreover, the method, described earlier in Section 5, which we used to find this correspondence, was very good at picking up this similarity; and, as before, a very good model was obtained by using BioASQ embeddings with the Word Mover Distance (WMD).