# Ontology-Based Feature Selection: A Survey
## 6 Open Issues and Challenges  
Features show dependencies among each other and, therefore, they can be structured as trees or graphs. Ontology-based feature selection in the era of knowledge graphs such as Wikidata, DBpedia, Freebase, and YAGO, can be influenced by two issues [103]:  
* The large expansion of knowledge recorded in Wikipedia, from which DBpedia and YAGO have been created as reference sources for general domain knowledge, is needed to assist information disambiguation and extraction. * Advancements in statistical NLP techniques, and the appearance of new techniques that combine statistical and linguistic ones. An important and open issue in this domain is the linking of one document-mentioned entity to a particular KG's entity and the way it affects how other surrounding document entities are linked. Furthermore, it is more and more common nowadays to see an increasing number of inter-task dependencies being modeled, where pairs of tasks such as Named-Entity Recognition (NER) and Entity Extraction and Linking (EEL), Word Sense Disambiguation (WSD) and EEL, or EEL and Relation Extraction and Linking (REL), are seen as interdependent. The combinatorial approach of those tasks will continue to exist and advance since it has been proven highly effective to the precision of the overall information/knowledge extraction process. Regarding the contributed communities in this area of research, related works have been conducted by the Semantic Web community as well as from others such as the NLP, AI, and DB communities. Works conducted by the NLP community focus more on unstructured input, while database and data-mining-related works target more to semi-structured input [103]. As mentioned above, ontologies play a key role in feature selection. However, the engineering of ontologies, despite advancing quickly over the last decade, has not yet reached the status were consensus in domain-specific communities will deliver gold-standard ontologies for each case and application area. On the other hand, several issues and challenges related to the collaborative engineering of reused and live ontologies have been recently reported [50], indicating that this topic is still active and emerging. For instance, as far as concerns feature selection, different ontologies of the same domain used in the same knowledge extraction tasks will most probably result in a different set of features selected (schema-bias). Furthermore, human bias in conceptualizing context during the process of engineering ontologies (in a top-down collaborative ontology engineering approach) will inevitably influence the feature selection tasks. Specifically, in the cases where large KGs (e.g., DBpedia) are used for knowledge extraction, such a bias is present in both conceptual/schema (ontology) and data (entities) levels. Debiasing KGs is a key challenge in the Semantic Web and KG community itself [104], and consequently in the domain of KG-based feature selection. Important challenges arise when ontology-based feature selection is applied to Linked Data (LD). LD appears to be one of the main structural elements of Big Data. For example, data created in social media platforms are mainly LD. LD appear to have significant correlations regarding various types of links and therefore, they possess more complex structure than the traditional attribute-valued data. However, they provide extra, yet valuable, information [105]. The challenges of using ontology-based feature selection in LD concern the development of ontology-based frameworks to exploit complex relation between data samples and features, and how to use them in performing effective feature selection, and to evaluate the relevance of features without the guide of label information. Another interesting research area is the real-time feature selection. The main difficulty in dealing with real-time feature selection is that both data samples and new features must be taken into account simultaneously.