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Opinion mining is the process of retrieving the opinion evidences from the reviews and finding their polarity such as positive, negative. Topical term mining enhances the opinion mining in which topical terms have to be extracted along with opinion evidences. Topical terms are specific attribute or quality of a topic. This paper proposes an approach for topical term mining. It aims to assist the users...
In recent years, there has been a rapid growth in usage of smart device. The smart device provides a variety of services which include data associated with user. In this paper, we suggest a design of service meta-knowledge based on ontology for collaboration of distributed smart devices. Furthermore, we suggest a design of API based on android for processing the meta-knowledge base. The meta-knowledge...
Ontologies perform the cumbersome task of translating data on the web into a form suitable for manipulation by machines. The discovery of concepts and relationships from unstructured text is a crucial and challenging task in constructing ontology (s). A pragmatic solution is offered by a metaontology as it supplements the ontology with related concept pairs and relationships. An ideal approach to...
Ontology has been increasingly recognised as an instrumental artifact to help make sense of large amounts of data. However, the challenges of Big Data significantly overburden the process of ontology storage and query particularly. In this respect, the paper aims to convey considerations in relation to improving the practice of storing or querying large-scale ontologies. Initially, a systematic literature...
Generation of problem sets and quizzes forms animportant part of education technologies. Although some systemshave been built for quiz generation, they mostly focus on abstractlogic and mathematical constructs. Knowledge in other domainsis relational rather than propositional, and many systems usededicated knowledge databases. We present a method to presentthis knowledge in the form of objective question...
E-learning environment is spreading in education process especially adaptive e-learning systems. This approach of learning means that learning content is created and distributed in a very different manner to comply with differences among individuals. Providing personalized content is the major research topic for a huge no. of researchers. And sequentially is Computer adaptive test (CAT). Almost all...
GIS information is important for e-Health surveillance because such information is required for monitoring, exchanging and shared across health GIS systems. However, interacting with the health GIS system is difficult for the users who may have less knowledge about GIS systems. This paper proposes a semantic query builder that provides a query service and can interact with health GIS systems. The...
Current approaches for learning ontology from relational databases are focused on the implementation of the transformation rules without paying attention to user requirements in terms of the way in which the ontology should be designed. They propose ready-made functions that transform the database model to an ontology TBox. There is no way to customize the learning process. The paper proposed a solution...
We present in this paper, one important feature of our approach that draws on a semantic enrichment process of the RDB model for learning application ontologies from Relational Databases (RDB)s. In fact, our approach supports collaborative work on the enrichment process and considers the partitioning of the input RDB model into a set of business modules. The objectives behind the collaborative support...
Complex networks of direct relevance to biomedicine have not yet been fully mapped largely due to the incompleteness, isolation, and heterogeneity of data. The Semantic Web, by providing a technical framework for the integration and sharing of heterogeneous databases in different domains, can potentially enable more effective complex network mapping and analysis. However, the feasibility of using...
Big Data are collections of data sets so large and complex to process using classical database management tools. NOSQL databases are in the base of storage of Big Data. Learning ontology from these databases will extract their hidden semantics. This paper illustrates an approach to learn ontology from document oriented NOSQL database. We choose to deal with MongoDB. We present main steps of our approach...
We address the problem of the access to the results of scientific publications in the agri-food domain. We focus on the description of main contributions of the papers treating them as accepted or rejected beliefs of their authors expressed in the form of scientific laws. We define the structure of different kinds of scientific laws present in the domain in the form of an ontology. The main concern...
Today's web is a human-readable where information cannot be easily processed by the machine. At the same time, the enormous amount of data has made it increasingly difficult to find, access, present, and maintain relevant information. The present data retrieval techniques are based on the full content coordinating of keywords, which lack the semantic data. In this paper the semantic based information...
Nowadays, the communication between human and machines have occurred, at most of the cases, throught text due to the fact the oral communication to involve two main aspects: the understanding of the spoken language and the recognition voice, that are unsolved research topics yet. However, it is notable the progress that the areas of voice recognition and synthetizer have gotten in the last decade...
This paper solves the problem of prevention of asthma attacks, and also it can predict a future asthma attack based on prevalent asthma triggers. The system forms an opinion of the everyday condition of the user, for example, if the user is doing fine today but might not be fine tomorrow. With the help of neural networks, it is possible to predict precisely and correct the user habits. A user who...
In the context of large engineering projects the effective and efficient exchange and versioning of information from different engineering disciplines is essential. Semantic data integration approaches provide the necessary means to overcome the gap between heterogeneous local engineering tool concepts and common project-level concepts which enable the mapping of engineering data coming from different...
In this paper, we concentrate on how to apply semantic technology (ST) to intelligence decision-making support system (IDSS). Due to the limitations of traditional IDSS with relational database and the superiority of ST in knowledge presentation, we establish a ST-based IDSS aimed at combining IDSS with ST which integrates expert knowledge with RDF triples. Pig disease diagnose is chosen as an example...
Big Data Era brings two main dimensions, which are heterogeneity and contextual data. The heterogeneity may occur at syntax and semantic levels. Ontologies are largely used to reduce these heterogeneities. Data has limited value if not paired with its context. Usually the internal data of companies are not connected to the rest of data universe including news, weather, user profiles, etc. In Engineering...
The continuous growth in quantity and diversity of life sciences data is triggering several bioinformatics challenges to be able to integrate and select desired information for later study. The majority of these data are scattered through independent systems disregarding interoperability features, which makes data integration processes not a trivial task. Consequently, several ETL (Extract-Transform-and-Load)...
One of the problems that hinders large scale network management tasks is the number of possible heterogeneous data sources that provide network information and how to focus on a desired network segment without requiring a deep knowledge of the network structure. This work investigates how to intelligently and efficiently refine and manage a vast amount of network monitoring data sources, by using...
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