# 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
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**Algorithm 1** The Proposed Missing Concepts Refinement Algorithm  
Indeed, the most novel idea in this method is to compose several steps that maximize the performance of the concept hierarchy refinement by taking into consideration (1) the verb hyponym and hypernym relationships contained in WordNet, (2) the behavior of different linguistic patterns by extracting hyponym-hypernym pairs from the metadata accompanied by the candidate data source, and (3) the concept hierarchy retrieved from DBpedia predicates, such as rdfs:subClassOf, umbel:superClassOf, and geo-ont:parentFeature. Another possibility to improve the taxonomic relation refinement from the metadata is to combine the Hearst [22,23] and Aguado de Cea [19] patterns for hyponym and hypernym extraction, given that this combination has been proven to improve the precision and recall, since Hearst patterns allow for finding all possible taxonomic relationships with high precision but low recall, and in contrast, Aguado de Cea patterns produce high recall but low precision [24]. Table 5 shows a part of Hearst and Cea's patterns that are used to acquire new taxonomic relations in the partially refined local ontology. The \"taxonomic relation refinement\" method is outlined in Figure 3, where the basic steps are displayed. At the initial step of this method, the candidate metadata is scanned for instances of distinguished Hearst and Cea patterns that are useful for detecting hyponym and hypernym relations for each concept included in the partially refined local ontology to identify new assumption relations. These patterns occurred frequently across the textual metadata and summarized the most common ways of expressing hyponyms and hypernyms. An example of these patterns that could be detected in a sentence like \"Activities such as changing clothes, having guests, or cleaning are considered\" is \"NP \(\lfloor\) such as [NP] * {and | or ] NP\", where the first NP denotes a super concept (e.g., \"activity\") of the next NPs (e.g., \"changing clothes\", \"having guests\", and \"cleaning\"). Then, a set of hyponyms \begin{table}
\begin{tabular}{c c c} \hline \hline
**LSP** & **Example** & **Relation** \\ \hline Property\(\mid\)ies \(\mid\) characteristic\(\mid\)s \(\mid\) & & Datatype properties: X, Y, Z \\ attribute \(\mid\)s of NP\(<\)class\(>\) be [PARA] & Attributes of an accelerometer are X, Y, and Z & Class or domain: accelerometer \\ [(NP\(<\)property\(>\)) * and] NP\(<\)property\(>\) & & Metals are lustrous, malleable, and good & Datatype property: lustrous \\ NP\(<\)class\(>\) be [(AP\(<\)property\(>\)) *] and & & & Class or domain: metal \\ AP\(<\)property\(>\) & & & Datatype property: color \\ NP\(<\)class\(>\) have NP\(<\)class\(>\) & & & Class or domain: car \\ NN with \(\mid\) without DT? RB? J!? ATTR & & & Datatype property: cheese \\  & & & Class or domain: pizza \\ DT ATTR of DT? RB? J!? NN & & & Datatype property: color \\  & & & Class or domain: car \\ \hline \hline NP: noun phrase; AP: adjectival phrase; NN: noun; DT: determiner; RB: adverb; J!: adjective; ATTR: attribute; PARA: paralinguistic symbols, & & \\ like colors;*: repetition. & & & Class or domain: car \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LSPs for missing concept acquisition. and hypernyms covered by WordNet is gathered for each concept. Finally, this method finishes by checking each concept for the DBpedia predicates, such as \"rdfs:subClassOf\", \"umbel:superClassOf\", or \"geo-ont:parentFeature\", to discover unrecognized assumption relations among concepts using DBpedia. The third and last refinement method, \"non-taxonomic relation refinement\", is based on associating concepts by representing hidden connections between them and identifying their non-taxonomic relations, specifically the object properties.