Semi-automatic Derivation of Specific-Domain Ontologies for the Semantic Web

2006 ◽  
Author(s):  
JRG Pulido ◽  
Ma Arechiga ◽  
R Acosta ◽  
PD Reyes ◽  
S Legrand
Keyword(s):  
Semantic Web ◽  
Specific Domain ◽  
Domain Ontologies

scholarly journals Enabling social WEB for IoT inducing ontologies from social tagging

2019 ◽  
Vol 8 (1) ◽  
pp. 19
Author(s):  
Mohammed Alruqimi ◽  
Noura Aknin
Keyword(s):  
Semantic Web ◽  
Collective Intelligence ◽  
Heterogeneous Systems ◽  
Social Tagging ◽  
Specific Domain ◽  
Current Effort ◽  
Domain Experts ◽  
Social Tagging Systems ◽  
Formal Ontologies ◽  
Domain Ontologies

<span>Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span>

Referencing Resources through Ontology Evolution

2010 ◽  
pp. 224-244
Author(s):  
Déliar Rogozan ◽  
Gilbert Paquette
Keyword(s):  
Semantic Web ◽  
Domain Knowledge ◽  
Software Agents ◽  
Dynamic Environments ◽  
Ontology Evolution ◽  
Domain Ontologies ◽  
Original Domain ◽  
Fundamental Requirement

Evolution is a fundamental requirement for useful ontologies. Knowledge evolves continuously in all fields of knowledge due to the progress in research and applications. Because they are theories of knowledge in a precise domain, Ontologies need to evolve because the domain has changed, the viewpoint of the domain has changed or because problems in the original domain conceptualization have to be resolved or have been resolved (Noy & Klein, 2003). Moreover, in open and dynamic environments such as the Semantic Web, the ontologies need to evolve because domain knowledge evolves continually (Heflin & Hendler, 2000) or because ontology-oriented software-agents must respond to changes in users’ needs (Stojanovic, Maedche, Stojanovic, & Studer, 2003).

Using Semantic Web Concepts to Retrieve Specific Domain Information from the Web

2011 ◽  
pp. 249-270
Author(s):  
Rafael Cunha Cardoso ◽  
Fernando da Fonseca de Souza ◽  
Ana Carolina Salgado
Keyword(s):  
Information Retrieval ◽  
World Wide Web ◽  
Semantic Web ◽  
Web Sites ◽  
World Wide ◽  
Specific Domain ◽  
New Concepts ◽  
The World ◽  
Domain Information ◽  
The Web

Currently, systems dedicated to information retrieval/extraction perform an important role on fetching relevant and qualified information from the World Wide Web (WWW). The Semantic Web can be described as the Web’s future once it introduces a set of new concepts and tools. For instance, ontology is used to insert knowledge into contents of the current WWW to give meaning to such contents. This allows software agents to better understand the Web’s content meaning so that such agents can execute more complex and useful tasks to users. This work introduces an architecture that uses some Semantic Web concepts allied to Regular Expressions (REGEX) in order to develop a system that retrieves/extracts specific domain information from the Web. A prototype, based on such architecture, was developed to find information about offers announced on supermarkets Web sites.

Knowledge-based terminological e‑dictionaries

Terminology ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 259-290 ◽  
Author(s):  
Christophe Roche ◽  
Rute Costa ◽  
Sara Carvalho ◽  
Bruno Almeida
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Theoretical Perspective ◽  
Specific Domain ◽  
Methodological Foundation ◽  
Knowledge Based ◽  
Different Types ◽  
The Creation ◽  
Islamic Archaeology ◽  
Concept Systems

Abstract The advent of the Semantic Web and of the Linked Data initiative have contributed to new perspectives and opportunities regarding terminology work. Among them are the double dimension approach and the theoretical perspective of ontoterminology anchored therein, which explore the synergies resulting from the systematic organisation of both term systems and concept systems. By doing so, they provide a theoretical and methodological foundation underlying the creation of knowledge-based terminological products that can support the conception and development of different types of e‑dictionaries. Within that scope, and based on examples pertaining to two different subject fields, namely endometriosis and Islamic archaeology, this article aims to propose a framework for the creation of a terminological e-dictionary, defined as a reference resource in a specific domain that gathers, structures and describes linguistic data in a systematic way in one, two or more languages, in order to define concepts that are denoted by terms.

Improving Software Agent Communication with Structural Ontology Alignment Methods

2010 ◽  
Vol 5 (3) ◽  
pp. 49-64 ◽  
Author(s):  
Jairo F. de Souza ◽  
Rubens N. Melo ◽  
Jonice Oliveira ◽  
Jano de Souza ◽  
Sean Wolfgand M. Siqueira
Keyword(s):  
Semantic Web ◽  
Software Agents ◽  
Software Agent ◽  
Semantic Interoperability ◽  
Ontology Alignment ◽  
Agent Communication ◽  
System A ◽  
Domain Ontologies

To perform tasks on the semantic web, software agents must be able to communicate with other agents using domain ontologies, even when considering different ontologies. In this regard, it is necessary to address semantic interoperability to enable agents to recognize common concepts and misunderstandings. In this paper, the authors propose the use of negotiation concepts in business scenarios for addressing concept compatibilization problems in communication between software agents and present an algorithm developed in the GNoSIS system. A validation of this approach is presented.

Evidence Sources, Methods and Use Cases for Learning Lightweight Domain Ontologies

2011 ◽  
pp. 1-15 ◽  
Author(s):  
Albert Weichselbraun ◽  
Gerhard Wohlgenannt ◽  
Arno Scharl
Keyword(s):  
Semantic Web ◽  
Real World ◽  
Use Cases ◽  
Ontology Learning ◽  
Shared Meaning ◽  
Domain Ontologies ◽  
Real World Problems

By providing interoperability and shared meaning across actors and domains, lightweight domain ontologies are a cornerstone technology of the Semantic Web. This chapter investigates evidence sources for ontology learning and describes a generic and extensible approach to ontology learning that combines such evidence sources to extract domain concepts, identify relations between the ontology’s concepts, and detect relation labels automatically. An implementation illustrates the presented ontology learning and relation labeling framework and serves as the basis for discussing possible pitfalls in ontology learning. Afterwards, three use cases demonstrate the usefulness of the presented framework and its application to real-world problems.

Semantic Web-Based Framework for Scientific Workflows in E-Science

2017 ◽  
pp. 187-202
Author(s):  
Singanamalla Vijayakumar ◽  
Nagaraju Dasari ◽  
Bharath Bhushan ◽  
Rajasekhar Reddy
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Scientific Community ◽  
Web Applications ◽  
Future Generation ◽  
Scientific Workflows ◽  
Specific Domain ◽  
Web Based ◽  
Scientific Results ◽  
Generation Computer

In the future generation, computer science plays prominent role in the scientific research. The development in the field of computers will leads to the research benefits of scientific community for sharing data, service computing, building the frameworks and many more. E-Science is the active extending field in the world by the increase data and tools. The proposed work discusses the use of semantic web applications for identifying the components in the development of scientific workflows. The main objective of the proposed work is to develop the framework which assists the scientific community to test and deploy the scientific experiments with the help of ontologies, service repositories, web services and scientific workflows. The framework which aims to sustenance the scientific results and management of applications related to the specific domain. The overall goal of this research is to automate the use of semantic web services, generate the workflows, manage the search services, manage the ontologies by considering the web service composition.

XML Mining for Semantic Web

2011 ◽  
pp. 317-342
Author(s):  
Rafael Berlanga ◽  
Victoria Nebot
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Large Scale ◽  
Huge Amount ◽  
Semantic Annotations ◽  
Data Formats ◽  
Wide Acceptance ◽  
Domain Ontologies ◽  
The Web

This chapter describes the convergence of two influential technologies in the last decade, namely data mining (DM) and the Semantic Web (SW). The wide acceptance of new SW formats for describing semantics-aware and semistructured contents have spurred on the massive generation of semantic annotations and large-scale domain ontologies for conceptualizing their concepts. As a result, a huge amount of both knowledge and semantic-annotated data is available in the web. DM methods have been very successful in discovering interesting patterns which are hidden in very large amounts of data. However, DM methods have been largely based on simple and flat data formats which are far from those available in the SW. This chapter reviews and discusses the main DM approaches proposed so far to mine SW data as well as those that have taken into account the SW resources and tools to define semantics-aware methods.

XML Mining for Semantic Web

Data Mining ◽  
2013 ◽  
pp. 625-649
Author(s):  
Rafael Berlanga ◽  
Victoria Nebot
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Large Scale ◽  
Huge Amount ◽  
Semantic Annotations ◽  
Data Formats ◽  
Wide Acceptance ◽  
Domain Ontologies ◽  
The Web

This chapter describes the convergence of two influential technologies in the last decade, namely data mining (DM) and the Semantic Web (SW). The wide acceptance of new SW formats for describing semantics-aware and semistructured contents have spurred on the massive generation of semantic annotations and large-scale domain ontologies for conceptualizing their concepts. As a result, a huge amount of both knowledge and semantic-annotated data is available in the web. DM methods have been very successful in discovering interesting patterns which are hidden in very large amounts of data. However, DM methods have been largely based on simple and flat data formats which are far from those available in the SW. This chapter reviews and discusses the main DM approaches proposed so far to mine SW data as well as those that have taken into account the SW resources and tools to define semantics-aware methods.

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