# Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
## 2 Related Work
### Comparison and Discussion  
Based on the aforementioned criteria, we classified the ontology learning approaches as shown in Table 1.  
The comparative table shows that a large number of approaches, such as those in [4; 5; 10; 11; 12; 13; 14; 15; 16], have a single input format. For example, [PERSON] and [PERSON] [11] built an ontology from LOD (DBpedia). However, our approach is capable of handling a semi-structured input with varying formats, including CSV, JSON, and XML. In addition, almost all approaches aimed to return one or a few specific learning elements. For example, [PERSON] and [PERSON] [5] extracted only non-taxonomic relations, while [PERSON] [15] included four learning elements such as concepts, non-taxonomic relations, datatype properties, and individuals. In contrast to these common approaches, we cover the six different kinds of learning elements that can be appended to a learned ontology. Moreover, apart from the works of [PERSON] et al. [10] and [PERSON] et al. [3], where an input is transformed to an ontology on two levels using an intermediate XML data and RDB schema, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Input** & \begin{tabular}{c} **Learning** \\ **Element** \\ \end{tabular} & \begin{tabular}{c} **Pivot** \\ **Model’s** \\ **Hierarchy** \\ \end{tabular} & \begin{tabular}{c} **Ontology** \\ **Refinement** \\ \end{tabular} & \begin{tabular}{c} **Ontology** \\ **Align-** \\ **ment** \\ \end{tabular} & \
\begin{tabular}{c} **Ontology** \\ **Merging** \\ \end{tabular} &
\begin{tabular}{c} **Ontology** \\ **Degree** \\ \end{tabular} \\ \hline [PERSON] et al. [10] & Semi-Structured: & I & XML data & x & x & x & x & Automatic \\ Volker and Niepert [11] & Structured: & C + NTR + I & x & x & x & x & Automatic \\ Dpedia & C + NTR + D + I & x & x & x & x & x & Automatic \\ Bohring and Auer [12] & Semi-Structured: & C + NTR + D + I & x & x & x & x & x & Automatic \\ [PERSON] et al. [3] & Semi-Structured: & C + NTR + D & RDB & & & & & \\ CSV or XSL & C + NTR + D & schema & x & x & x & x & Semi-Automatic \\ [PERSON] and Shamsafard [13] & Structured: RBD & C + NTR + & x & x & x & x & x & Automatic \\  & D + I + A & x & x & x & x & x & Semi-Automatic \\ [PERSON] et al. [14] & Structured: & C + TR & x & x & x & x & x & Semi-Automatic \\  & RBD & C + NTR + D + I & x & x & Predefined & & & \\ [PERSON] et al. [4] & Semi-Structured: & C + NTR + D + I & x & x & Rules & \(\surd\) & \(\surd\) & Semi-Automatic \\ Booshehri and Luksch [5] & Unstructured: & & & & & LOD with & & & \\ Text & NTR & x & x & DBpedia & x & \(\surd\) & Semi-Automatic \\  & Structured: & C + NTR + D + I & x & x & x & x & \(\surd\) & Automatic \\  & RBD & C + TR + NTR + & x & x & x & x & \(\surd\) & Automatic \\  & Unstructured: & C + TR + NTR + & x & x & x & x & \(\surd\) & Automatic \\  & Text & D + I + A & & & & LOD with & & & \\  & & & & & & & & \\  & & & & & & & & \\ \multirow{2}{*}{Our approach} & Semi-Structured: & C + TR + NTR + & x & XIL & DBpedia + & & & \\  & CSV, JSON or & C + NTR + & schema++ & \(\surd\) & WordNet & \(\surd\) & \(\surd\) & Automatic \\  & XML & & data & & & & & \\  & & & & & Metadata & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Tabular comparison of different ontology learning approaches based on main distinguishing criteria. respectively, a one-level transformation was applied in the remaining approaches. In this work, similar to [10], we propose a two-level transformation, since we support heterogeneous formats of input. Therefore, a semi-structured input is transformed to an ontology on two levels, going through a pivot used as an intermediate model in order to represent all input formats in the same formalism. Transformation on the first level refers to the transformation of a semi-structured input to a pivot model in XML schema. On the second level, it refers to the transformation of the pivot model itself to an ontology. Moreover, the XML hierarchy was overlooked in the reviewed approaches that used XML as an input [12] or as a pivot model [10].