# DAEOM: A Deep Attentional Embedding Approach for Biomedical Ontology Matching
## 3 Experiments and Discussion
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**MA-NCI**} & \multicolumn{3}{c}{**FMA-NCI (Small)**} & \multicolumn{3}{c}{**FMA-SNOMED (Small)**} \\ \cline{2-10}  & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline OM-TD (TF-IDF) & 0.900 & 0.648 & 0.785 & 0.969 & 0.734 & 0.835 & 0.941 & 0.613 & 0.742 \\ OM-TD (LSTM) & 0.968 & 0.704 & 0.815 & 0.958 & 0.871 & 0.912 & 0.972 & 0.687 & 0.805 \\ OM-TD (TBERT) & 0.977 & 0.702 & 0.817 & 0.966 & 0.878 & 0.920 & 0.977 & 0.715 & 0.826 \\ OM (LSTM + SGAT) & 0.975 & 0.717 & 0.826 & 0.971 & 0.879 & 0.923 & 0.981 & 0.732 & 0.838 \\ OM (TBERT + GraphSAGE) & 0.954 & 0.529 & 0.681 & 0.981 & 0.738 & 0.843 & 0.913 & 0.677 & 0.777 \\ OM (TBERT + TransE) & 0.890 & 0.502 & 0.642 & 0.961 & 0.558 & 0.706 & 0.722 & 0.506 & 0.595 \\ OM (DAEOM-) & 0.977 & 0.736 & 0.840 & 0.984 & 0.871 & 0.924 & 0.987 & 0.783 & 0.873 \\ OM (DAEOM) & **0.981** & **0.748** & **0.849** & **0.989** & **0.888** & **0.936** & **0.990** & **0.791** & **0.879** \\ \hline \hline \end{tabular} Note: “OM-TD” means only using terminological description for ontology matching. “OM” means using both network structure and terminological description. Bold numbers indicate the best performance on each matching task, respectively.  
\end{table} 
Table 2: The comparison of DAEOM with baseline methods. Ontology (DOID), and the Medical Subject Headings (MeSH) [12]. Hence, our reported recall can be explained due to the lower coverage of biomedical terminology in the semantic lexicons that we have used. Nevertheless, there still exists a gap compared with the best systems (e.g., AML, LogMap, POMAP++) in MA-NCI. We analyze that only considering the SUBCLASS-OF relationship may be the main reason. In MA-NCI, the ontology \(O_{s}\) contains a large number of property relationships, which we did not use. This is also the reason why the relations of ontology \(O_{s}\) of this task in Table 1 is sparser than that of other ontology. We leave this issue in our future work. In Table 4 we demonstrate a sample of results produced by aligning ontologies using improved Stable Marriage's solution based on a similarity matrix computed either on DAEOM or OM-TD(LSTM) in FMA-NCI task. The automatically calculated threshold of DAEOM is 0.046, and the other is 0.097. These results are divided into a true and false alignment. As we know, there are two types of ontological heterogeneity. The first one is that different concepts from the two ontologies contain the same semantics, but are defined as different name labels. The other is that two concepts are defined as the same name labels but their semantemes are very different. From those results in Table 4, it can be seen that DAEOM can effectively distinguish two concepts that have name or semantic heterogeneity by jointly encoding ontology terminological description and network structure. In particular, our method is more effective than the term-only method to match concepts that have numbers and abbreviations in their name labels.