# Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance Criteria
## 3 Methodology
### VF2E vs. Keyword Extraction with Chunking  
Furthermore, some other comparisons between VF2E and the proposed approach are in finding polarity of just keywords; we performed chunking in the reviews based on some grammar rules defined in our method. In this way, we calculated the polarity, but for that particular chunk from the review, either polarity of all the sentences where that chunk appears in a text or only of that chunk. To better clarify this point, we took some examples to get the polarity of the words. If the word'very delicious' was extracted as a keyword, the polarity of this would be positive. However, in reality, this word was used in a sentence like \"The food was not very delicious\". Thus, the polarity of the whole sentence would be negative. Moreover, when we passed this sentence to the chunking method, it gave us some essential chunks from the text like 'food not delicious'. Both methods aim to find out the most relevant services/restaurants and recommend them to users. Generally, this process does not follow the traditional machine learning techniques like splitting the dataset into train and test parts, and getting the model trained on the train set and then tested on some unseen test instance to evaluate the performance/learning using classifiers/algorithms. The basic concepts of both recommendation approaches are the same--we have not only training datasets like customer reviews. This is precisely why we prepare our data and construct a particular structure in which we can recommend items to users based on their reviews posted on a service. In our recommendation system, we attempt to recommend services to users without knowing what services they have visited before any step. The system is unaware of the services visited by the users; therefore, the proposed method blindly recommends services to customers in terms of their previously visited services. Although we have some models, structures, and environments in which we do some processing on the data provided by the customer and then come to a decision about what services should be recommended to which user. The foremost important step is keyword extraction from the reviews, which then become entities based on which relationships are created in the knowledge graph. When we have to recommend services to a user, the algorithm crawls on a graph to extract the best recommendations. At this step of extracting keywords, we compared the proposed method with the existing one. They use pruning rules based on which they reject the keywords; their main concern on keyword extraction is basically which keywords should be rejected from many keywords. In the extraction approach, RAKE is used, even though, in VF2E [8] the authors presented their approach as an improved RAKE method with a set of pruning rules named value feature entity extraction (VF2E) even then the number of keywords extracted is considerable comparatively by our proposed method. As they have already mentioned, the keywords extracted by RAKE are large in number. However, after extracting the keywords by RAKE the authors in VF2E apply pruning rules, and the three rules about which we have concern are as follows:Rule 1: Exclude VFs, which includes one word and is an adjective, adverb, or interjection. Rule 2: Exclude VFs, which have the uppercase first letters and whose named entity is identified as GPE (geopolitical entity) or PERSON. Rule 3: Exclude VFs without a noun. In the critical analysis considering all the rules mentioned above, we found that they accept only those keywords which contain at least one noun. Thus, it is evident that nouns are the most crucial entity in the graph for which user's post reviews or talk about a restaurant for that specific entity in their reviews. However, in this rule, it is also evident that we have all the noun words as keywords in the results, and the minimum length of the keyword is two letters; so in this case, the simple rule causes a tremendous amount of noun words as keywords.