# An Embedding-Based Approach to Repairing OWL Ontologies
## 5 Experiments
### Results of Comparing Repair Algorithms
#### 5.4.2 Results about Efficiency  
Figure 4 presents the time to compute a diagnosis for each selected ontology. For the ontology km1500-3500, LogicAlg cannot finish the repair process within the limited time (i.e., 1000 s).  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Ontology** & **BaseAlg** & **LogAlg** & **ScoreAlg** & **ShapAlg** & **SigAlg** & **SigCosAlg** & **SigEucAlg** & **ThrCosAlg** & **ThrEucAlg** \\ \hline ALOD2 Vec-confof-edas & 1 & 1 & 1 & 1 & 2 & 4 & 4 & 5 & 1 \\ ATRown-cnt-confof & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 1 \\ CHEM-A & 1 & 1 & 1 & 1 & 1 & 1 & 3 & 1 \\ dbpedia\_2014 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ Economy & 8 & 34 & 8 & 8 & 47 & 22 & 23 & 36 & 20 \\ Geography & 9 & 11 & 9 & 10 & 11 & 12 & 13 & 11 & 13 \\ km1500-3500 & 19 & n.a. & 26 & 26 & 39 & 38 & 38 & 38 & 30 \\ kola & 1 & 2 & 1 & 1 & 2 & 3 & 3 & 3 & 3 \\ Ly-cnt-conference & 1 & 1 & 1 & 1 & 2 & 1 & 1 & 2 & 2 \\ Lily-edas-ekaw & 4 & 4 & 5 & 5 & 6 & 5 & 5 & 8 & 7 \\ MGED & 3 & 7 & 3 & 4 & 9 & 21 & 20 & 7 & 7 \\ minIambius & 3 & 3 & 3 & 3 & 3 & 3 & 3 & 3 \\ pizza & 2 & 2 & 2 & 2 & 3 & 3 & 3 & 3 \\ proton & 8 & 12 & 8 & 9 & 15 & 12 & 12 & 12 & 11 \\ Terrorism & 1 & 5 & 1 & 1 & 5 & 5 & 5 & 1 & 4 \\ Transportation & 13 & 15 & 13 & 14 & 24 & 23 & 23 & 19 & 20 \\ University & 3 & 3 & 3 & 3 & 3 & 3 & 3 & 4 \\ Ve.Align-edas-lasted & 2 & 2 & 2 & 2 & 12 & 5 & 5 & 2 & 3 \\ Wiktionary-cnt-confof & 3 & 3 & 3 & 3 & 5 & 6 & 6 & 4 & 8 \\ Wiktionary-confof-edas & 1 & 1 & 1 & 1 & 3 & 5 & 5 & 5 & 1 \\ \hline Total & 85 & 109 & 93 & 97 & 193 & 175 & 176 & 168 & 143 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Repair results about the number of the axioms removed by each repair algorithm (using the model m1 and the threshold 0.5), where “n.a.” indicates “not available”.  
Figure 4: Time in milliseconds (y-axis) for each incoherent ontology to compute diagnoses using the model m1 and the threshold 0.5.  
From the figure, we observe that ScoreAlg performs best and is able to finish most of the processes within 10 milliseconds. BaseAlg performed slightly worse than ScoreAlg, and it spent less than 100 milliseconds for most of the ontologies. This can be explained by the fact that BaseAlg applies the ILP-based approach to all MIPS directly while ScoreAlg is based on those subsets extracted from all MIPS. As the similarities of ScoreAlg and ShapAlg are similar, their performances are also close. LogicAlg is more time-consuming than all other algorithms except the two threshold-based ones in most cases. It is mainly due to the usage of logical reasoning. Take the ontology VeeAlign-edas-iasted as an example. LogicAlg took about 6 s while others took no more than 4 s. Three signature-based algorithms performed similarly, and the original one performed slightly better. Two threshold-based algorithms spent much more time than others since they need to spend time computing a degree for an axiom over all axioms in the corresponding ontology. The degree is summing the similarity values between the axiom and others if the values are larger than the given threshold (i.e., 0.5).