## 4 Experiments
### A Case Study  
To further analyze the contribution of ICRT, a case study is conducted on an article with PubMed ID 17785587. As illustrated in Figure 7, based on the curation by CTD experts, the article belongs to two target clusters that respectively correspond to two issues about two associations: (1) Target cluster I: <TERT, DC>, and (2) Target cluster II: <DKCL, DC>, where TERT is \"telomerase reverse transcriptase,\" DC is \"dyskeratosis congenita,\" and DKCl is \"dyskerin pseudouridine synthase 1\". With the help of ICRT, all systems correctly identify Target cluster II, but not Target cluster I, as they make an error in identifying a large cluster with an additional article (ID = 17460043, see the cluster marked with a dash line box in Figure 7). This article focuses on the association <TERT, IPF>, where IPF is \"Idiopathic Pulmonary Fibrosis\" (see Target cluster III in Figure 7). That is, both Target clusters I and III are about a gene TERT, but they are about different _issues_ of entity associations (i.e., associations between TERT and two different diseases DC and IPF, respectively). Detailed analysis shows that the error is made (i.e., grouping article 17460043 into Target cluster I) for two reasons: (1) when discussing the target issue <TERT, IPF>, article 17460043 also discusses <TERT, DC> and cites references about the association, and (2) the article also notes the correlation of DC and IPF, and cites references about the correlation. That is, the article discusses correlations among TERT, DC and IPF, although it manly focuses on TERT and IPF only. It is interesting to note that, without the help of ICRT, all the underlying systems make more errors in clustering the articles. Table 3 summarizes the errors made by the systems. _OK-TA_ identifies none of the article pairs in Target clusters I (ref. the 1 st row of Table 3), and all the other underlying systems make the same error noted above (i.e., grouping article 17460043 with certain articles in Target  
Figure 6: Test results on the gene-disease dataset. Figure 7: A case study to analyze how ICRT successfully improves clustering systems that work on various inter-article similarity measures: Without ICRT, all the underlying systems make more errors (see the explanation in the main text). cluster I, ref. the 2 nd row of Table 3). Moreover, both _BC_ and _DescriptiveBC_ make an additional error on article 23583980 (ref. the 3 rd row of Table 3), and all the other underlying systems make an additional error on article 22299032 (ref. the 4 th row of Table 3). With the help of ICRT, these errors are avoided. More specifically, for article 23583980, _BC_ and _DescriptiveBC_ find that it is quite similar to article 17460043, because they cite several references in common. The similarity estimation is good, as both articles belong to Target cluster III in Figure 7; however, it also leads the clustering systems to make an error of grouping them with articles in Target cluster I, because article 17460043 is similar to articles in Target cluster I. With the help of ICRT, the error is avoided by excluding article 23583980 from the identified cluster (i.e., the cluster marked with a dash line box in Figure 7), because IIT of 17460043 and 23583980 is quite different from IIT of the identified cluster. For article 22299032, all the other systems make at least one of two errors: (1) without grouping it into Target cluster II, and (2) grouping it into Target cluster I (recall that the article belongs to Target cluster II but _not_ Target cluster I, see Figure 7). The first error is made by those systems (e.g., _PMS_ and _OK-TA_) that think the two articles in Target cluster II (i.e., 22299032 and 17785587) are not similar enough. With the help of ICRT, the error is avoided, as many references in the two articles are similar, meaning that issue-based similarity (IBS, ref. Equation (8)) between the two articles is high.