# Text Semantic Annotation: A Distributed Methodology Based on Community Coherence
## 4 Results
The Mention Score--Confidence scatterplot (Figure 2), outlines the correlation of the confidence score with the probability of a valid mention annotation as intuitively anticipated. As we model confidence, based on the relative difference of the next highest positive linking probability estimated mention, we can observe a proportionately insignificant number of invalid mention annotation outliers for some relatively high confidence scores. The generated GPC model, which considers _community coherence_, _link probability_, _average Jaccard index_ of inter-wiki links, and _average relatedness_[9], yields impressive accuracy performance. Specifically, the proportion of the valid versus the invalid annotations appears highly inclined towards the valid annotations. As depicted in Figure 3 and Table 1, the precision-recall performance of both approximations is similar. Nonetheless, the Clauset-Newman-Moore algorithm practically proved slightly better when applied in the community detection step as also theoretically anticipated. Specifically, the Clauset-Newman-Moore algorithm is naturally expected to form few big communities and hence further retain the original ontologies' network structure. On the contrary, the Louvain algorithm's application predictably resulted in the generation of many smaller communities and hence further fragment of the initial network of ontologies. Despite the successful and consistent fragmentation of Louvain's community structure, the nature of the problem fits best in the formation of larger discrete unified communities. This requires further analysis in future experimentation, since other community detection algorithms might bear even better performance from the examined baseline algorithms. In any case, and for any community detection algorithm applied, the adoption of the community coherence feature is practically proven to be superior for high precision performance. Specifically, in the extreme case of absolute recall, the precision difference of the methodology introduced reaches 5% per each baseline case as depicted in Figure 3 and Table 1. As a result, it is safe to conclude that even with the baseline application of a GPC classifier, the value of community prediction application is justified. Figure 3: Precision-recall of the two community detection methodology variants, the Gaussian Process Classifier and TAGME [12] baseline. Figure 2: Mention Score—Confidence Scatter Plot. Furthermore, as the Gaussian Process Classification fits a generative probabilistic model, we observe a very gradual precision-recall curve slope. Overall, the contribution of community detection is remarkable as, even at very low confidence levels, i.e., when recall is over 0.9, the precision remains at exceptional levels, approximately 0.83. In Table 1, we see that even at a recall level of 0.8, both the Louvain and the Clauset-Newman-Moore algorithms attain impressive results for a dataset of this size and context variability, being highly propitious for real world applications. The performance divergence between the two examined algorithms is very low, outlining the fact that, despite community structure discrepancies, meaningful context information is contributing to the overall performance. In general, the precision-recall curve of similar works [9; 10; 11; 12] presents more aggressively steeper slopes in the (0.6, 1.0) range. Apparently, in the general case, the confidence level increase can be alternatively interpreted as requesting the absolute accuracy in the whole documents' dataset. On the contrary, in our methodology the respective precision-recall curve is far smoother, proving the superiority of the introduced technique. This related to the fact that the mention retrieval occurs in the community level where the mentions belonging in the same community tend to have much more contextual similarity. Hence, as shown in the above graph, the higher precision-recall reported values are due to our methodology's ensured community cohesion, which can be additionally translated to contextual resemblance.