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Ontology development includes various approaches like Formal Concept Analysis (FCA) or Ontology Learning. FCA applies a user-driven step-by-step methodology for creating domain models, whereas Ontology learning refers to the task of automatically creating domain Ontology by extracting concepts and relations for the given data set [27]. This chapter focuses on building an automatic semantic indexer for online product/service reviews using Ontology. The representation of documents is semantically and contextually enriched by using the Context Feature Selection LDA (CFS_LDA) topic modeling technique. Search query yields improved relevant results thereby increases the recall value [26, 27].

The problem statement is simply stated as “to extract all probable relevant features from the Corpus.” Given a set of terms for each feature (or topic), the objective is to construct index, which is embedded in the Ontology so as to reduce query processing time. Ontology-based Semantic Indexing (OnSI) model includes three main processes like semantic indexing, Ontology development, and evaluation, as shown in Figure 1.8.

Figure 1.8 Ontology-based semantic indexing (OnSI) model.

The semantic indexing module includes topic mapping, and term indexing. Ontology development module populates Ontology with these terms and their weights (LDA weights). OnSI evaluation module evaluates the built Ontology through query processing.