**4. Design decisions in the implementation of ontology**

The description logic (DL) is used in reasoning the instances of ontology. DL is the math behind the constructs of the ontology. The engineered PROO ontology has DL expressivity level *ALCIN(D)*. ALCIN(D) is attribute logic with complement, role inverse, unqualified number restriction and datatype. This ontology is robustly scalable and the rules learned from it are computationally solvable in polynomial running time, i.e., PTIME. The target sentiment which is learned as the rule consequent on the object properties of PROO ontology is decidable as the rules are deductible in the PTIME. Also the learned rules are DL-safe as these rules are restricted to known instances of the ontology.

collected product features and opinions. Only one product type for the rule-based sentiments analysis as the PROO ontology is developed for a class of mobile phones of different

Sentiment-Based Semantic Rule Learning for Improved Product Recommendations

http://dx.doi.org/10.5772/intechopen.72514

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ILP rules are also extracted from PROO ontology. The rule predecessor is learned by forming a conjunction of PROO ontology classes and the relevant properties which relate to these classes. The class instances and the property values are reasoned for extracting the target sentiment class instance which is the rule consequent. The generated rules cover the positive instances of the product feature. The assessment of the generated rules is envisioned with

The AUC is a measure to showcase the reviews covered in either of the two sentiment groups (good/bad) available from the dataset. The parameters of the receiver operating characteristic (ROC) curve are the target class label and the ranking attribute. The target instance considered is good for the sentiment class and the ranking attribute is considered as opinion strength. An accuracy of 86.7% of ROC area coverage is obtained. The k-common features identified after the customer searched for Iphone 6 s plus are tabulated in **Table 2**. The value of k found is 17.

area under receiver operating characteristic curve (AUC).

The similar products are Oppo f1 plus and Samsung galaxy j7 prime.

manufacturers.

**k-Common features**

Network connectivity

**Table 2.** List of k-common features.

Phone ROM Battery Performance

OS Brand

Camera Price

Build quality Touch Screen Battery life Camera quality Appearance Display RAM

There were some issues encountered at the time of PROO ontology development. This PROO ontology development was based on design decisions taken at two stages. The two stages were namely the design decisions made before the ontology development and, the decisions made at the time of ontology development.

The first design decision before the development of ontology was on the scope of the ontology to represent the appropriate knowledge for conceptualization. In the product reviews domain, the PROO ontology was intended to support the new customers in retrieving the object information from a large number of reviews by reasoning on object property ontology path. The second design decision was on adhering to the development of a formal ontology so as to reason the ontology for making meaningful conclusions. The PROO ontology was developed using the formal Web Ontology Language (OWL) constructs. The third design decision was whether to annotate the product features and opinions extracted from the reviews as instances to the concepts of the ontology or not.

The design decision taken during the development of ontology was to choose the required superclass-subclass taxonomies in the ontology. The taxonomies created in the development of PROO ontology were the hierarchy of the product features and the PoS word class tags. For some queries on PROO ontology, it was observed that the information retrieved is incorrect. The same instance that was used in analyzing the different product reviews has led to the former mentioned problem.
