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Название: Semantic Web for Effective Healthcare Systems

Автор: Группа авторов

Издательство: John Wiley & Sons Limited

Жанр: Программы

Серия:

isbn: 9781119764151

isbn:

СКАЧАТЬ Φtw C 0.01770.0042 0.00040.1415 0.12470.1429 0.00040.1502 0.00050.2238 Bill Φtw C 0.00040.0468 0.0265-0.0015 0.00010.1614 0.01210.1176 0.00050.2111
Features of DS1 Number of terms selected by CFSLDA Correlated feature terms by CFSLDA model (DS1)
Cost 68 cost, test, money, charge, day, case, department, patient, room, pay, bill, ...
Medicare 110 doctor, discharge, medicine, treatment, appointment, admission, disease, option, pain, reply, duty, test, meeting, …
Staff 112 staff, patient, medicine, problem, report, manner, management, treatment, complaints, …
Infrastructure 101 hospital, room, facility, meals, rate, …
Time 73 time, service, hour, operation, day, bill, ...
Schematic illustration of spring view of domain ontology. Schematic illustration of precision versus recall curve for the Dataset DS1.

      1.7.1 Discussion 1

      Ontology querying involves direct extract of feature from its repository instead of doing similarity measures as other techniques like Naive Bayes algorithm do. The similarity between the terms is incorporated into the model during the CFSLDA modelling technique itself rather than the querying phase. It is very much required as the lexicon-based indexing technique just uses the keywords with one-to-one mapping and it does not look for synonymous terms and contextually related terms. OnSI model retrieves these types of terms from the document collection for the features or topics, which in turn improves the recall value. 100% accuracy may not be attained sometimes, as some of the terms present in query documents may not be present in the Ontology and it may need to be updated. In the next iteration, the value gets improved.

      1.7.2 Discussion 2

Technique Recall Accuracy Time
Naive Bayes Classifier 30% 69% 3.98 s
k-Means Clustering 37% 79% 4.25 s
OnSI (Ontology-based CFSLDA) 57% 88% 2.45 s

      1.7.3 Discussion 3

      Generally, the term-document (TD) matrix is stored in .csv format which takes megabytes of storage whereas the .owl format, the Ontology file, takes only kilo bytes of storage. For example, size of .csv file was 3.5 MB (approx.) when review documents were converted into TD matrix for the dataset DS1. Each review document consumes 1 kB (approx.) storage and also it depends on the number of terms present in the dataset. However, DS1 takes only 360 kB (approx.) when .owl format is used.

      This chapter focused on building of Ontology for the contextual representation of user-generated content, i.e., the review documents. The contextually aligned documents are represented in domain Ontology along with the semantics using the Ontology-based Semantic Indexing (OnSI) model.