VOEditor: a Visual Environment for Ontology Construction and Collaborative Querying of Semantic Web Resources

Like other components of Semantic Web-based applications, ontologies are evolving over time to reflect changes in the real world. Several of these applications require keeping a full-fledged history of ontology changes so that both ontology instance versions and their corresponding ontology schema versions are maintained. Updates to an ontology instance could be non-conservative that is leading to a new ontology instance version no longer conforming to the current ontology schema version. If, for some reasons, a non-conservative update has to be executed, in spite of its consequence, it requires the production of a new ontology schema version to which the new ontology instance version is conformant so that the new ontology version produced by the update is globally consistent. In this paper, we first propose an approach that supports ontology schema changes which are triggered by non-conservative updates to ontology instances and, thus, gives rise to an ontology schema versioning driven by instance updates. Note that in an engineering perspective, such an approach can be used as an incremental ontology construction method driven by the modification of instance data, whose exact structure may not be completely known at the initial design time. After that, we apply our proposal to the already established [Formula: see text]OWL (Temporal OWL 2) framework, which allows defining and evolving temporal OWL 2 ontologies in an environment that supports temporal versioning of both ontology instances and ontology schemas, by extending it to also support the management of non-conservative updates to ontology instance versions. Last, we show the feasibility of our approach by dealing with its implementation within a new release of the [Formula: see text] OWL-Manager tool.

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Automatic Ontology Construction

Advances in Library and Information Science - Critical Approaches to Information Retrieval Research ◽

10.4018/978-1-7998-1021-6.ch007 ◽

2020 ◽

pp. 109-149

Author(s):

Amita Arora

Keyword(s):

Semantic Web ◽

World Wide ◽

Quality Information ◽

Ontology Construction ◽

Internet Connection ◽

Generation Technique ◽

Unstructured Text ◽

Ontology Generation ◽

High Quality Information ◽

Work Done

World wide web has information resources even on unthinkable subjects. This information may be available instantly to anyone having Internet connection. This web is growing exponentially, and it is becoming difficult to locate useful information in such a sheer volume of information. Semantic web extends the current web by emphasizing on interoperable ontologies which are capable of processing high quality information so that the agents placed on top of semantic web can automate the work or curate the content for the user. In this chapter, an extensive research in the area of ontology construction is presented, and after having a critical look over the work done in this field and considering the limitation of each, it has been observed that constructing ontology automatically is a challenging task as this task faces difficulties due to unstructured text and ambiguities in English text. In this work an ontology generation technique is devised covering all important aspects missing in the existing works giving better performance as compared to another system.

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Towards a Semantic Web of Evidence-Based Medical Information

Knowledge Media in Healthcare ◽

10.4018/978-1-930708-13-6.ch014 ◽

2011 ◽

pp. 254-273

Author(s):

Rolf Grutter ◽

Claus Eikemeier ◽

Johann Steurer

Keyword(s):

Semantic Web ◽

Medical Information ◽

Uniform Resource Identifier ◽

Simple Method ◽

Web Resources ◽

Web Of Data ◽

Description Framework ◽

Object Models ◽

Application Specific ◽

Resource Description

It is the vision of the protagonists of the Semantic Web to achieve “a set of connected applications for data on the Web in such a way as to form a consistent logical Web of data” (Berners-Lee, 1998, p. 1). Therefore, the Semantic Web approach develops languages for expressing information in a machine-processable form (“machine-understandable” in terms of the Semantic Web community). Particularly, the Resource Description Framework, RDF (Lassila & Swick, 1999), and RDF Schema, RDFS (Brickley & Guha, 2000), are considered as the foundations for the implementation of the Semantic Web. RDF provides a data model and a serialization language; RDFS a distinguished vocabulary to model class and property hierarchies and other basic schema primitives that can be referred to from RDF models, thereby allowing for the modeling of object models with cleanly defined semantics. The idea behind this approach is to provide a common minimal framework for the description of Web resources while allowing for application-specific extensions (Berners-Lee, 1998). Such extensions in terms of additional classes and/or properties must be documented in an application-specific schema. Application-specific schemata can be integrated into RDFS by the namespace mechanism (Bray, Hollander & Layman, 1999). Namespaces provide a simple method for qualifying element and attribute names used in RDF documents by associating them with namespaces identified by URI (Uniform Resource Identifier) references (Berners-Lee, Fielding, Irvine & Masinter, 1998).

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A WIKIPEDIA-BASED FRAMEWORK FOR COLLABORATIVE SEMANTIC ANNOTATION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213011000413 ◽

2011 ◽

Vol 20 (05) ◽

pp. 847-886 ◽

Cited By ~ 1

Author(s):

N. FERNÁNDEZ ◽

J. A. FISTEUS ◽

D. FUENTES ◽

L. SÁNCHEZ ◽

V. LUQUE

Keyword(s):

Semantic Web ◽

Data Processing ◽

Web Search ◽

Semantic Annotation ◽

Formal Ontology ◽

Manual Annotation ◽

Annotation Task ◽

Web Resources ◽

User Actions ◽

Common User

The semantic web aims at automating web data processing tasks that nowadays only humans are able to do. To make this vision a reality, the information on web resources should be described in a computer-meaningful way, in a process known as semantic annotation. In this paper, a manual, collaborative semantic annotation framework is described. It is designed to take advantage of the benefits of manual annotation systems (like the possibility of annotating formats difficult to annotate in an automatic manner) addressing at the same time some of their limitations (reduce the burden for non-expert annotators). The framework is inspired by two principles: use Wikipedia as a facade for a formal ontology and integrate the semantic annotation task with common user actions like web search. The tools in the framework have been implemented, and empirical results obtained in experiences carried out with these tools are reported.

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Automatic Topic Ontology Construction Using Semantic Relations from WordNet and Wikipedia

International Journal of Intelligent Information Technologies ◽

10.4018/jiit.2013070104 ◽

2013 ◽

Vol 9 (3) ◽

pp. 61-89 ◽

Cited By ~ 4

Author(s):

V. Subramaniyaswamy

Keyword(s):

Web Mining ◽

Relation Extraction ◽

Past Research ◽

Semantic Relations ◽

Svm Classifier ◽

Ontology Construction ◽

Knowledge Resources ◽

Web Resources ◽

The Past ◽

The Web

Due to the explosive growth of web technology, a huge amount of information is available as web resources over the Internet. Therefore, in order to access the relevant content from the web resources effectively, considerable attention is paid on the semantic web for efficient knowledge sharing and interoperability. Topic ontology is a hierarchy of a set of topics that are interconnected using semantic relations, which is being increasingly used in the web mining techniques. Reviews of the past research reveal that semiautomatic ontology is not capable of handling high usage. This shortcoming prompted the authors to develop an automatic topic ontology construction process. However, in the past many attempts have been made by other researchers to utilize the automatic construction of ontology, which turned out to be challenging due to time, cost and maintenance. In this paper, the authors have proposed a corpus based novel approach to enrich the set of categories in the ODP by automatically identifying the concepts and their associated semantic relationship with corpus based external knowledge resources, such as Wikipedia and WordNet. This topic ontology construction approach relies on concept acquisition and semantic relation extraction. A Jena API framework has been developed to organize the set of extracted semantic concepts, while Protégé provides the platform to visualize the automatically constructed topic ontology. To evaluate the performance, web documents were classified using SVM classifier based on ODP and topic ontology. The topic ontology based classification produced better accuracy than ODP.

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Learning pattern of hurricane damage levels using semantic web resources

International Journal of Computational Science and Engineering ◽

10.1504/ijcse.2019.10026143 ◽

2019 ◽

Vol 20 (4) ◽

pp. 492

Author(s):

Quang Khai Tran ◽

Sa kwang Song

Keyword(s):

Semantic Web ◽

Web Resources ◽

Learning Pattern ◽

Hurricane Damage

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The Study of Ontology Reasoning to Semantic Web

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.181-182.230 ◽

2011 ◽

Vol 181-182 ◽

pp. 230-235

Author(s):

Xian Yi Cheng ◽

Ai Qin Yang ◽

Xue Yun Cheng

Keyword(s):

Semantic Web ◽

Description Logic ◽

Practical Reasoning ◽

Reasoning Task ◽

Ontology Construction ◽

Semantic Reasoning ◽

Ontology Reasoning ◽

Knowledge Reasoning ◽

Ontology Language ◽

Knowledge Expression

The next generation Web, Semantic Web, has recently been drawn considerable attention from both academia and industry. The ontology is regarded as the cornerstone of the Semantic Web, is playing an important role with the knowledge expression and knowledge reasoning. Ontology language, Description Logic and the relationships of them were presented. This paper analyzes the principle of semantic reasoning about DL and reasoning machine. Finally, performers testify the practical reasoning task about a concrete Ontology construction.

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