# Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
## 3 Ontology-Based Model Evolution Approach
### Ontology-Based Model's Learning Module
#### 3.3.2 Refinement Phase  
After attaining the local ontology, it is necessary to expand its quality borders through the accreditation of a refinement phase. Therefore, uncovering missing concepts as well as relations and identifying a concept hierarchy are considered ontology refinement methods. For this, DBpedia, WordNet, and metadata knowledge bases are considered references for refining the local ontology, since they are widely used and cover broad resources from different domains. Indeed, coupling several knowledge bases could answer missing knowledge during ontology refinement and improve the inexpressive local ontology through exploiting the different linguistic patterns, hyponym-hypernym relationships contained in WordNet, and concept hierarchy retrieved from DBpedia. The first refinement method, \"missing concepts refinement\", is used to compensate for the missing concepts from the learning phase. More specifically, this method follows the main steps outlined in Algorithm 1. In this algorithm, the beginning step is intended to collect all datatype properties included in the local ontology. Next, the retrieved datatype properties are traversed to extract their corresponding domains. Then, the candidate metadata are processed for each datatype property sequentially in three steps. First, each sentence that contains the datatype property is extracted. Second, Parts of Speech (POS) tags are induced over each extracted sentence to prepare them for analysis. Third, a collection of lexico-syntactic patterns (LSPs), presented in Table 4, is exploited over the POS tags to extract the related domain to the datatype property. These LSPs are issued from different works published in the literature, such as [19; 20; 21], to perform a semantic analysis on any textual metadata and to identify missing concepts through the datatype properties. Once these steps are established, the domain of the datatype property is updated to represent the missing concepts. The second refinement method, \"taxonomic relation refinement\", identifies the concept hierarchy to organize learned and new covered concepts into the local ontology through subsumption relations. Therefore, this method focuses on exploring the benefits from coupling WordNet, metadata, and DBpedia to learn new taxonomic relations among the concepts represented in the partially refined local ontology. \begin{table}
\begin{tabular}{c c} \hline \hline
**XML Schema** & **OWL Ontology** \\ \hline \
\begin{tabular}{c} xsd:complexType: xsd:element, containing other elements or \\ having at least one attribute \\ xsd:attribute \\ xsd:element, inside an anonymous complex type \\ xsd:complexType, which uses extensions \\ \end{tabular} &
\begin{tabular}{c} owl:Class, coupled with owl:ObjectProperty \\ owl:DataProperty with a range depending on the attribute type \\ owl:ObjectProperty \\ owl:Class as an owl:subClassOf “base type” \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: XML schema to OWL ontology mapping rules. ```
Input: LocalOntology, Metadata, LSPs Output: PartiallyRefined_LocalOntology
1 DataTypePropertiesSet = GetAllDatatypeProperties (LocalOntology)
2 For each DatatypeProperty \(\in\) DataTypePropertiesSet do
3 Domain = GetDomain (DatatypeProperty)
4 SentencesSet = FindSentences (DatatypeProperty, Metadata)
5 For each Sentence \(\in\) SentencesSet do
6 Sentence = PartsOfSpeechTagging(Sentence, POS)
7 NewDomain = matches (Sentence, LSPs)
8 If NewDomain \(\
eq\emptyset\)then
9 PartiallyRefined_LocalOntology = UpdateMissingConcept (DatatypeProperty,Domain,NewDomain)
10 End If
11 End For
12 End For
13 End.