## Appendix A Additional Experiments
### Experimenting with the Full Texts of the Guidelines  
We performed additional experiments with _modified_ views of the full guideline documents, as enumerated below. This was driven by the fact that the levels of distances between full documents may change if we compute the similarities/distances between selected sentences, which are explicitly related to the statements from the CDC table in Figure 1. For these additional experiments we split each full text guideline document into two different subsets:  
1. Related: containing sentences that are related to the CDC table, by having common concepts, as represented by UMLS concepts. This was done in multiple ways, giving us six possible experiments: 1. The CDC recommendations table was considered as a single bag of concepts. If a sentence in the full text had a _minimum_ number of mutual concepts with this bag, that sentence was considered a related sentence. 2. If a sentence in the full text had a _minimum_ number of mutual concepts with at least one statement from the CDC table (again, viewed as a bag of concepts), that sentence was considered a related sentence. Different minimum numbers of mutual concept(s) were examined in our experiment, that is the _minimum_ was set to 1, 2 and 3. 2. Unrelated: the other sentences. Unrelated sentences were not used for these additional experiments. Concept extraction: For all experiments, we used MetaMap ([[https://metamap.nlm.nih.gov/](https://metamap.nlm.nih.gov/)]([https://metamap.nlm.nih.gov/](https://metamap.nlm.nih.gov/)) (accessed on 24 February 2021)) to extract UMLS concepts ([[https://www.nlm.nih.gov/research/umls/index.html](https://www.nlm.nih.gov/research/umls/index.html)]([https://www.nlm.nih.gov/research/umls/index.html](https://www.nlm.nih.gov/research/umls/index.html)) (accessed on 24 February 2021)) and semantic types ([https://www.nlm.nih.gov/research/umls/META3_current_semantic_types.html](https://www.nlm.nih.gov/research/umls/META3_current_semantic_types.html) (accessed on 24 February 2021)) in sentences. We only considered concepts with informative (in our opinion) semantic types. This meant using concepts related to diagnosis and prevention, for example \"findings\", and not using ones related, e.g., to genomics. Our final list had the following: [[diap], [hcla], [dsyn], [neop], [qnco], [qloc], [tmco], [fndg], [geoa], [topp], [lbpr]].  
For full text guidelines (as per Table 4), the results of the experiments are shown in Table 6, and discussed in Sections 6 and 8. Tables A1 and A2 are based on the same type of comparisons as discussed in Section 6, except that we subtract the Unrelated sentences from the full guidelines. Again, we observed that the similarity is not accidental, and that BioASQ embeddings with WM distance seem on average to give the best performance.  
Note the potentially important observation about Tables 6, A1 and A2: They jointly show that the property we investigate, i.e., the conceptual distances between guidelines, is indeed geometric, and therefore the word \"distances\" is not merely a metaphor. The correspondence between the two graphs is preserved no matter how we set up the experiments. That is, as with geometric properties such as being collinear or parallel, the structure remains the same when a transformation (such as a projection) is applied to the points, even though some of the measurements might change (e.g., measured distances, or the area of a parallelogram). The same happens when we transform the documents by removing Unrelated sentences: the values of distortions change, but the non-accidental correspondence with the summary graph (Figure 5) remains invariant.