Semantic Web for Effective Healthcare Systems. Группа авторов

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Название Semantic Web for Effective Healthcare Systems
Автор произведения Группа авторов
Жанр Программы
Серия
Издательство Программы
Год выпуска 0
isbn 9781119764151



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information extraction and text analysis techniques.

      1.1.1 Ontology-Based Information Extraction

       • Concepts are also known as Classes. It is a unit of knowledge, shared among identified group of persons for the concept’s domain. There exists relationship among concepts.

       • Instances are individuals of concepts. They represent specific elements attached to the domain ontology. Instances are the “thing” represented by a concept.

      Information Extraction (IE) and Ontology are related with one another like: Ontology is used in information extraction as part of understanding process of the domain; on the other hand, IE is used to design and enrich Ontology [7]. Common vocabulary and shared understanding among different people are enabled by Ontology. The contextual representation of data semantics is well described by the Ontology [8]. The UML diagrams along with Ontology support the biologists by classifying the entities and interactions between proteins and genes [9]. The terms (vocabularies) and the concepts (classes) in the source Ontology are used in term matching, thereby used in tagging the text documents. Thus the Ontology and their specifications are used in the information extraction process.

      1.1.2 Ontology-Based Knowledge Representation

      Ontology facilitates the shared understanding among the people by formalizing the conceptualization of a specific domain. The contextual representation of data semantics is well described by the Ontology [8]. Ontology defines concepts (domain) by using the common vocabulary and describes attributes, behavior, relationships and constraints. The UML diagrams along with The interactions between proteins and genes are well explained by Ontology representation which would support the biologists for classification [9]. Reviews on hotels and movies are classified using the rule-based systems and Ontology [14–16]. Document annotation and rules were used to create knowledge base of web documents from the extraction of semantic data like named entities [14, 17, 18]. Ontology learning and RDF repositories were used for building the knowledge and information management which in turn enabled the automatic annotation and retrieval of documents [19]. Wordnet Ontology was used in extracting the sentiments based on lexicon dictionaries [20, 21].

      Knowledge base refers the dictionary for the vocabulary used to represent concepts of a specific domain. The Ontology provides the semantic knowledge for class instances like a dictionary. The meaning of the documents may be extracted using the semantic-based approach by establishing the suitable context within the document, instead of using terms present in the document. Related terms were extracted and categorized using the semantic-based approaches like LSI [11] and LDA [13] techniques. Ontology-based sentiment analysis model was developed for mining product features from customer reviews [1]. Ontology along with Genetic Algorithm, a hybrid-model, was used for automatic grouping of Chinese proposals into different clusters resulted in >90% F-measure value [26]. Sentiment lexicons of emotional categories were derived from the twitter posts of mobile products by using Ontology learning and the lexicon-based techniques [27]. Ontology and vector analysis method was used in feature selection and sentiment analysis of movie reviews [22]. Ontology-based sentiment analysis model along with rule-based classification was used in the postal services