# Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
## 2 Related Work
### Ontology Learning Approaches  
For dynamic ontology-based model evolution, ontology learning approaches can offer evolution support. Ontology learning is defined as the automatic or semi-automatic process for building an ontology from scratch, enriching or adapting a preexisting ontology to the arisen changes, and the consistent management of these changes [6]. By going through the literature and several surveys in this area [7, 8, 9], numerous approaches have been proposed to support ontology learning in the scope of ontology building or preexisting ontology evolution. A couple of approaches for ontology learning have been proposed in the scope of building ontologies from scratch. In this sense, [PERSON] et al. [10] introduced an approach that deals with learning ontologies from tabular data (TD) in CSV format. The proposed approach relies on two-level data transformation from the CSV data to a \"pivot\" model and then to an ontology. To this end, a mapping process is introduced to first transform a CSV document into an XML format. Then, the transformation from the XML format to the ontology is performed using Extensible Stylesheet Language Transformation (XSLT). Wealso cite the work of [PERSON] and [PERSON] [11], which described an approach for ontology building directly from DBpedia. The presented approach uses statistical methods and association rules for finding alignments. The novelty of this approach comes from the fact that it is one of the early works which creates ontologies from a Linked Open Data (LOD) dataset. Another work that has considered ontology building based on ontology learning is that of [PERSON] and [PERSON] [12]. This work proposed a tool called xml2 owl, which covers both the schema and data levels. Xml2 owl offers the possibility to create an OWL ontological model from an XML schema and to convert XML data to OWL instances. This conversion is performed using XSLT. Moreover, [PERSON] et al. [3] proposed a semi-automatic framework that provides a support system for users in transforming TD in spreadsheet format (i.e., CSV or XSL to RDF format). This framework focused on the need for automatic schema detection before starting ontology learning from TD. For that, it provided techniques for data type and nominal type detection for the column fields in the datasets. Therefore, the proposed approach consists of three processes in which users are involved: dataset preparation and import, schema detection and verification, and OWL and RDF data generation. The last process relies on a pivot model that translates TD into a Relational Database (RDB) and mapping rules for database schema to ontology transformation. Aside from this, the authors in [13] proposed an approach to create an ontology from an RDB automatically. First, the presented approach performs relational schema pre-processing on the input data to determine its components. Then, it analyzes these components using a set of mapping rules to convert them to corresponding ontology components. Finally, [PERSON] et al. [14] proposed a semi-automatic approach to learn OWL ontologies from RBD by using classification techniques or, more specifically, decision trees. They introduced an algorithm based on the use of decision trees to automatically discover the taxonomic relationships between concepts. Then, they invited a domain expert to semantically validate the generated schema and add a meaningful name to the ontology concepts. Apart from these approaches, other approaches in the scope of preexisting ontology evolution are emerging. For example, [PERSON] et al. [4] proposed a mechanism to transform semi-structured data--specifically a set of JSON documents provided by Web services--into a unified ontology. This mechanism extracts JSON data automatically, including concepts, properties, constraints, and values and builds the ontology. The resulting ontology must be validated by domain experts. Additionally, [PERSON] and [PERSON] [5] provided an ontology enrichment approach from text, in which the Web of linked data, particularly DBpedia, is used.