tvnovellas.info Theory OWL REPRESENTING INFORMATION USING THE WEB ONTOLOGY LANGUAGE PDF

OWL REPRESENTING INFORMATION USING THE WEB ONTOLOGY LANGUAGE PDF

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This document is also available in these non-normative formats: PDF version. along with information written in RDF, and OWL 2 ontologies . Each OWL 2 ontology represented as an instance of this conceptual structure can . such statements are captured using the functional-style syntax, which is. ontology language to a known logical formalism, and by using automated reasoners that RDF and RDFS allow the representation of some ontological knowledge. The .. information to indicate earlier versions of the current ontology . OWL is an ontology language designed for the Semantic. Web. – It provides a rich collection of operators for forming The Web was made possible through established standards to represent the extra semantic information needed for.


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However, the Web Ontology Working Group of W3C [10] identified a number of . ontology language to a known logical formalism, and by using automated .. and predicate logic (Antoniou and Van Harmelen ) to represent information in. Reasoning on the Semantic Web: OWL, reasoning tools. Page 6. Representing knowledge. There are a As XML, using the industry-standard structured mark- up language. • As graphs RDF describes the semantics of information in a. OWL 2 ontologies can be used along with information written in RDF, can be used to represent first simple information and then more complex The W3C OWL 2 Web Ontology Language (OWL) is a Semantic Web language . syntaxes are shown throughout the document by using the buttons below.

Suitable procedures for visualization, and different structures, information systems are becoming navigation, knowledge discovery, integration with online increasingly complex. The Semantic Web aims to find, share databases, multilingual context sensitive user interface, and refine the information of current web more easily. The reasoning, querying, decision support and the system Semantic Web is a solution for representing the data in a machine understandable manner. Ontology is a powerful management tools are also incorporated. For the sake of technology for representing the data in semantic web. This paper proposes a unified properties? These and integrated framework, i.

These two fields are finally coming together, and this article is an attempt to articulate that link. In other words, a couple of year-old words are becoming the hot new buzzwords in our industry. Semantics is the Greek philosophic study of the nature of meaning, especially as it is expressed in language. Semantics tells us how to describe it. The meaning of the model varies, depending on its purpose: It can represent a data base design, with the boxes representing tables and the lines representing foreign keys.

Also represented are the columns of the tables.

Data Modeling, RDF, & OWL – Part One: An Introduction To Ontologies

It can be a conceptual model representing the structure of a business, with the boxes representing things of significance to the business and the lines representing semantic relationships between them. Also represented may be the definitions of data describing those things of significance. These are very different things. A database design describes an artifact that can be employed to store and manipulate data.

Owl: Representing Information Using the Web Ontology Language e-book downloads - Mon premier blog

Other than constraints on cardinality, business rules are not generally represented on data models of either kind. Even in the case of business data models, the models are supposed to represent fundamental structures, while business rules represent variable constraints.

In other words, database design, business data modeling, and business rule modeling are three very different things. This article then uses that to describe a completely different mindset. It is about defining categories of data. Its graphic nature provides an excellent basis for discussing and negotiating the meaning of those categories. Accompanied by business rules analysis, the two provide a basis for collecting data according to those categories, and its corresponding database design provides a mechanism for doing so.

In its latest incarnations, however, an ontology language begins with instances of actual data. The data modeling mindset is based upon the closed world assumption: Only that which is asserted is known. Ontology languages are based on the open world assumption.

Computer Science > Logic in Computer Science

All assertions are assumed to be true until proven otherwise. Efforts in the Semantic Web community are already beginning to invest in technology that enables data to be readily integrated Belleau et al. The aim of such a platform is to offer users the ability to ask questions using multiple connected resources that share common identifiers and have a common format RDF and query interface SPARQL.

This platform complements other existing data access modes such as our Web site and RESTful web services, but additionally contains explicit links between the different data resources. This enables a single query to be asked across multiple distributed datasets and across a range of biological domains.

The development of the platform began by collecting requirements from both a scientific and a technical perspective.

Pdf ontology language web using information owl representing the

The scientific requirements were gathered as a series of use cases and competency questions collected from research scientists and users of EBI services. In particular, we were looking for questions that required data to be integrated from multiple resources and that are not trivial to answer with our existing infrastructure due to the disparate nature of the data.

These questions were used to identify points of integration between resources. Therefore, we randomly selected records as the test set in the current study. In the medical record, the most important disease from which the patient suffers is listed first and the complications are listed subsequently. This study only focused on the first disease that is listed in the medical record.

For the first scenario, we use the original ontology without the newly added probabilities and apply the path ranking algorithm PRA [ 40 ] to model the ontology relationships and train the classifier for each relationship. In the ontology, a relationship path can be formed by connected ontology triplets. For example, disease, alias, disease and disease, corresponding symptoms, symptoms can be connected as a path.

Considering the ontology as a directed graph, PRA adopts the relationship path as a feature and represents all the relationship paths in the ontology as feature vectors. Afterwards, the classifiers are trained to identify the relationships between the entity pairs.

Each prediction result produces a 1-specificity, sensitivity pair and represents a point in the ROC space. Then, we plot the ROC point for each possible threshold value result the threshold specifies the minimum a posteriori probability for assigning a sample to the positive class , thereby forming a curve.

In this study, we use the area under the curve AUC , whose value is typically between 0 and 1, to measure and compare the classification performances of classifiers. An AUC value of 0.

A satisfactory classifier should have an AUC value that substantially exceeds 0.

Using web the pdf ontology information owl representing language

The higher the AUC value is, the better is the classification performance. The ROC curves that are presented in Fig. Using various threshold values, we aim at determining whether the experimental result can yield an accurate diagnosis based on various ontologies, where 0 denotes no and 1 denotes yes.

Web using owl information language ontology the pdf representing

The calculation of a classifier with the test data returns a probability pair, namely, [P1, P2], that specifies a probability of 0 or 1. The obtained results, such as 0: [3. The ontology that was developed with probabilities and enriched by more complete knowledge can accurately represent the relationships between diseases and symptoms and can provide superior data support for decision-making during diagnosis.

Comparing the blue curve with the red curve, the accuracy of the diagnosis has been significantly improved. This is expected since the OR value is particularly suitable for comparing the relative odds of the occurrence of disease outcomes given exposure to the health feature variable and attribute. The p-values are calculated using the GraphPad Prism 7 software based on the principle of the Z test by comparing the AUC values with 0.