# Text Semantic Annotation: A Distributed Methodology Based on Community Coherence
## 5 Conclusions and Future Work  
In this work we proposed a novel distributed methodology exploring the value of community detection algorithms for the text annotation problem, introducing a novel metric referred to as community coherence. An extensive experimental evaluation outlined impressive precision and computational performance. To the best of our knowledge, the current work is the first approximation of the Wikification problem employing Gaussian Process Classification and leveraging the value of community detection algorithms via the community coherence concept. As Bayesian approaches are established in text classification tasks, due to their inherent capabilities of probability distribution inference, our intuition in the successful application of the Gaussian Process Classification was validated by our experimental evaluation, yielding outstanding results. As the method is nonparametric, the training dataset observations are interpolated efficiently, as the problem is formalized effectively using a limited set of well-established features from previous works, mitigating the inherent drawbacks of the classification approximation, such as high dimensional space performance challenges.  
Despite the promising results, there is a great room for improvement. Specifically:  
* Increased interpretability was apparently required to assess the value and benefits of this novel methodology, however, the adoption of less interpretable but more accurate classifiers is to be considered a future step.
* The introduction of well-established features in a classifier-based entity linking methodology combined with community coherence resulted in impressive results, however, our low dimensionality approach could be leveraged by some deep neural network architecture models, as the next focus on further improvements to accuracy performance.
* The classic sequential community detection algorithms, such as the Louvain [2] and Clauset-Newman-Moore [3] algorithms, are doubtlessly considered unscalable and thus the experimentation with different community extraction techniques, such as the community prediction introduced in [27], needs also to be evaluated in terms of performance improvement and overall efficiency.
* The underlying challenge of the text annotation tasks, in conjunction with the Entity Linking tasks in general, would be the Knowledge Acquisition Bottleneck, namely the lack of semantically annotated corpora of the underlying knowledge ontology (i.e., Wikipedia). Addressing this challenge via an unsupervised machine learning approximation is among our future plans, aiming at improving knowledge acquisition closure from all available corpora resources.  
Our introduced methodology is promising for large scale adoption. In this article, we primarily focused on the feasibility assessment of leveraging the value of Community Detection algorithms for the Text Annotation and Entity Linking task. Our exhaustive experimentation revealed auspicious results.  
Conceptualization, C.M., G.P. and M.A.S.; methodology, M.A.S. and G.P.; software, M.A.S.; validation, M.A.S. and G.P.; formal analysis, M.A.S. and G.P.; investigation, M.A.S.; resources, C.M.; data curation, M.A.S.; writing--original draft preparation, M.A.S. and G.P.; writing--review and editing, C.M., M.A.S. and G.P.; visualization, M.A.S.; supervision, M.A.S. and G.P.; project administration, C.M.; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.  
[PERSON] has been co-financed by the European Union and the Greek national funds through the Regional Operational Program \"Western Greece 2014-2020\", under the Call \"Regional Research and Innovation Strategies for Smart Specialization--RIS3 in Information and Communication Technologies\" (project: 5038701 entitled \"Reviews Manager: Hotel Reviews Intelligent Impact Assessment Platform\").  
The authors declare no conflict of interest.