# An Embedding-Based Approach to Repairing OWL Ontologies
## 5 Experiments
### Results of Comparing Different Models  
To observe the influence of different pre-trained models on the results of repairing ontologies, the algorithms ThrCosAlg and ThrEucAlg were evaluated with the four models (i.e., m1, m2, m3 and m4). The threshold was set to 0.5. Figure 5 illustrates the time to compute a diagnosis for each ontology by applying the threshold-based algorithm with a specific model and a given distance metric. In this figure, cos-m1 indicates the threshold-based algorithm with Cosine Distance and model m1, and euc-m1 indicates the threshold-based algorithm with Euclidean Distance and model m1. It is similar to other algorithms. In Figure 5, we observe that the algorithm with cos-m4 is slightly more efficient than the algorithm with other configurations. The algorithms with cos-m1, euc-m1, euc-m3 or euc-m4 spent relatively more time. Generally speaking, the time difference between these algorithms is not significant. Table 6 presents the number of axioms removed by each algorithm with a specific model. The results reveal that both threshold-based algorithms with m4 remove fewer axioms than the two algorithms with the other models according to the total number of removed axioms. In particular, ThrCosAlg with m4 provides more promising results, and a total of 118 axioms were deleted. Compared with the baseline algorithm BaseAlg, this algorithm could find minimal diagnoses for 9 out of 20 ontologies. Although both  
algorithms remove the same number of axioms for these ontologies, the axioms removed may be different. Take the ontology Economy as an example. BaseAlg removed the following 8 axioms:  
a1: SubClassOf(HandwovenCarpet TextileProduct)  
a2: SubClassOf(Lumber ForestProduct)  
a3: SubClassOf(Ginger Vegetable)  
a4: DisjointClass(CapitalGood ManufacturedProduct)  
a5: DisjointClass(Fruit GroceryProduce)  
a6: DisjointClass(GroceryProduce RootVegetable)  
a7: DisjointClass(GroceryProduce Vegetable)  
a8: DisjointClass(AgriculturalProduct Fodder)  
ThrCosAlg with the model m4 removed the axioms from a4 to a8 together with the following three axioms:  
a9: DisjointClass(ForestProduct ManufacturedProduct)  
a10: DisjointClass(HandicraftProduct ManufacturedProduct)  
a11: DisjointClass(Spice Vegetable)  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ontology**} & \multirow{2}{*}{**0.4**} & \multirow{2}{*}{**0.45**} & \multirow{2}{*}{**0.5**} & \multirow{2}{*}{**0.55**} & \  
ThrCosAlg with the model m2 removed the same set of axioms as ThrCosAlg with the model m4. As we can see, axioms from ax1 to a3 represent knowledge in a correct way, while axioms from a9 to a11 are not very correct. For instance, axiom ax1 means that Spice is disjoint with Vegetable. However, in fact, those natural spaces such as Chinese prickly ash and coriander belong to vegetables. Therefore, ThrCosAlg with the models m2 and m4 could differentiate axioms with various degrees, and assign lower degrees to those problematic axioms. ThrCosAlg with the model m4 performed even better. Take the ontology Wiktionary-confof-edas as another example. ThrCosAlg with the model m4 only removed 1 axiom, which is the same as the baseline algorithm, but ThrCosAlg with other models removed more than 3 axioms.