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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.

      1.5.1 Ontology-Based Semantic Indexing (OnSI) Model

Semantic indexing module builds indexer for the review documents with the support of CFS_LDA model. The topic modeling technique CFS_LDA is used in the feature selection process to extract the features (or topics) and their related terms. Those terms that are grouped under the same topic are selected and tagged to represent the features of domain (or concept). Finally, the index is built using the keywords representing the features and feature terms with their context.

      Definition 1: Context is defined as a triplet < F, T, M >. F is the set of features, T is set of terms and their LDA scores, and M is the relationship between T and F, known as Ontology mapping. In other words, T is the set of terms used to describe F features of concept. Their contextual relationship is identified by M, a mapping function between T and F using the score.

      1.5.2 Ontology Development

      A topic (or feature) is a duplet <F, T>, F is the set of features of the domain and T is the candidate set of terms representing F. CFSLDA model selects T = {tc | tc is correlated with tp}, where tp is the higher probable term of F selected by the model. The contextual relationship between this duplet is represented by the triplet <F, T, M>, where M represents the mapping between F and T. The Ontology development or update procedure is shown:

Ontology tree is constructed with different levels for each <F, T> such that F forms the higher level, and T forms the next level for the corresponding F, as shown in Figure 1.9.

Figure 1.9 Domain ontology modeling for features.

      The LDA weight of each term is included in the Ontology representation. Ontology is used to construct the index through concept reasoning.

      1.5.3 OnSI Model Evaluation

OnSI evaluation module includes query processing, tagging, and Ontology mapping for feature scoring. It retrieves the relevant feature (or topic) and its score from the built domain Ontology for the set of query terms.

       (i) Query Processing

      Searching data is made easier and speed up when they are contextually grouped along with indexing. Else searching data in relational schema is quite expensive. Each query document is pre-processed and the PoS tagged nouns are sent to Ontology.

       (ii) Ontology Mapping

      The resultant features from the domain Ontology are mapped to the closest feature using their feature score. It enables to retrieve the relevant feature and its score for the query documents. Sparql, a XML-based query language is used to retrieve data from the built Ontology. It is described as follows:

Let Q be the set of query documents of product/service reviews written for F features using T terms. These documents are pre-processed and the nouns are extracted from them. Each query document is represented by q = {t₁, t₂, …, t_n}, where t is the term representing the feature f. The function M(t, f) maps each term t ϵ T with the feature f ϵ F and returns the fScore. This module addresses the four different types of queries, and it is shown in Figure 1.10.

Figure 1.10 Ontology mapping using OnSI model.

       (iii) Retrieval Process From Ontology

      When Ontology is queried using Sparql, the query language for Ontology, there could be four different types of queries. The procedure for retrieving data from Ontology is explained:

Type 1: Terms under only one feature

      If all terms t₁, t₂, …, t_n mapped to the same feature f, then f is returned as the result with the cumulative LDA scores of each term. For all i, 1 < i < n, there exists f such that

      For example, “treatment is good” is considered Type 1 query. Here, the word treatment is extracted. It comes under the feature (or topic) “Medicare,” and it is returned as the feature.

      Type 2: Terms under multiple features, each term under only one feature

      If terms { ti } mapped to feature +fa ϵ F, and terms {tj} mapped to feature fb ϵ F, then f whose fScore is higher is returned. In this case, the cumulative fScore is computed for each feature f and the feature with higher score is selected. Cumulative fScore of feature f is determined by the sum of LDA scores of terms corresponding to the feature f.

      For example, “the treatment is good but the rent is costly” is considered as Type 2 query. Here, the terms treatment and rent are extracted. The term treatment comes under the topic “Medicare,” while the terms rent comes under the topic “Cost.”

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