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2004
: Knowledge Representations issues take on special significance in the light of development of the novel Web's reality that involves the Semantic Web, GRID, P2P and other today's ITs. In contrast to the previous IT evolution's stages, the recent one utilizes ontology as separated resource. An elaborate knowledge representation approach implies an efficiency of knowledge-based systems and their interoperability. This paper deals with Ontology Engineering approach that allows both build and generate the consistent dynamic autonomous knowledge-based systems.
Ijca Proceedings on International Conference on Advances in Computer Engineering and Applications, 2014
As a backbone of the Semantic Web, Ontologies provide a shared understanding of a domain of text. Ontologies, with their appearance, usage, and classification address for concrete ontology language which is important for the Semantic Web. They can be used to support a great variety of tasks in different domains such as knowledge representation, natural language processing, information retrieval, information exchange, collaborative systems, databases, knowledge management, database integration, digital libraries, information retrieval, or multi agent systems. Thus a fast and efficient ontology development is a requirement for the success of many knowledge based systems and for the Semantic Web itself. This paper provides discussion on existing ontology tools and methodologies and the state of the art of the field.
Procedia Computer Science, 2021
The paper considers a purpose and features of ontologies use in describing of subject area, both from a theoretical and an applied point of view. Criteria for identifying ontologies types are indicated. Based on cognition schematism principles, a knowledge representation ontologies system has been developed, that combines language, forms of knowledge representation and process schemes. It is shown that it is exactly such a system of ontologies makes it possible for the practical use of ontologies in computing environments.
This paper presents a knowledge management experiment realized in an industrial company. Our research concerns the development of a knowledge engineering module integrated in a collaborative eGroupware system. This platform is used by engineers to realise their projects in a collaborative way and in following a defined professional process. The first step of our approach is based on the modelling of the professional process used by professional actor. We have developed a formalism called RIOCK (Role Interaction Organisation Competence and Knowledge) to identify the emanating Knowledge resulting from the interaction between the roles played by professional actors. According to the obtained cartography of Knowledge, we have defined a typology of Knowledge and built an ontology to create a representation language in order to share and broadcast Knowledge. In other hand, the RIOCK models allow us to design a knowledge engineering module based on a multi-agent system. This system monitors the action of the professional actors inside the eGroupware and capitalizes, annotates, and broadcasts Knowledge in using the semantic web technologies and the ontology.
… on Information Reuse and Integration (IRI-2001 …, 2001
Creating ontologies for scientific, natural or business domains is a process by itself while the development of knowledge bases that depend on those ontologies is another important process. Nevertheless, the research focus in the field of ontology is on the first process, namely, creating ontologies. The aim of the presented research is to improve the second process, namely, building knowledge bases that depend on well-established ontologies. Practically, the aim of the work is to enhance the process of knowledge-based systems construction by providing a common integrated environment for the ontologies development and deployment for knowledge based systems. The environment covers many aspects related to the content, the editing facilities and exchange of knowledge. The eXtensible Markup Language (XML) was chosen as the medium for persisting and exchanging knowledge.
Ontology plays a vital part in knowledge representation. These-days e-learning packages are prepared on the basis of the ontological structure which represents the knowledge to be designed. If we talk in simple terms, we can claim that taxonomical representation of text materials in text books are nothing but a sort of ontological representations. Taxonomy is a sort of hierarchical representation of texts based on topics, sub-topics, titles, subtitles, etc. Ontology has relations which links the above mentioned items. That way ontology goes a step further form the taxonomical representations. In a way, hierarchies, typologies, taxonomies, and ontologies are related and one depend on the other or one is an extension of the other. Here we are reminded of field semantics or sematic fields propounded by Trier and expanded by Lehrer. A semantic field is a 'group of words closely related in meaning, often subsumed under a general term. The examples are kinship terms, colour terms etc. The object of analysis of semantic field is to collect all the words that belong to a field and show the relationship of each of them to one another and to the general term. The taxonomy and ontology go further to take into their folds concepts beyond words. The purpose of this paper is to elaborate on how field semantics, hierarchies, typologies, taxonomies and ontologies help us to represent knowledge there by find ways to create e-learning packages for sake of learners who wants to access knowledge in a systematic fashion. Ontologies are a vast domain in lexical semantics and in artificial intelligence whose boundaries are somewhat fuzzy. Basically, ontology is a formal system that aims at representing for a given domain by means of basic elements, the different concepts and their related linguistic realizations. A large view of ontological knowledge can also include various forms of encyclopedic knowledge about the domain, and common-sense knowledge as well as rhetorical and metaphorical knowledge and expressions. Ontologies represent an important bridge between knowledge representation and computational lexical semantics. Ontologies are widely used as a formal device to represent the lexical content of words. Declarative representation of knowledge has long been acknowledged as a key building block of Al systems. A knowledge base (KB) with associated reasoning mechanisms can serve as a backbone for tasks such as query answering, learning and diagnosis. One of the key challenges in deploying knowledge representation (KR) systems is the cost and the complexity of building the knowledge base, especially in cases where it needs to cover a broad domain. Building a KB is highly labor intensive for several reasons. First, the acquisition of domain
IAENG Transactions on Electrical Engineering Volume 1, 2013
Handbook of Research on Social Dimensions of Semantic Technologies and Web Services, 2009
In this chapter, several knowledge representation and processing techniques based on a symbolic and semantic approach are briefly described. The majority of present-day techniques, like the relational database model or OWL (Web Ontology Language), is based on the symbolic approach and supports the representation and processing of semantically related knowledge. Although these two techniques have found many successful applications, there are certain limitations in their wider use, stemming from the use of naming in explicit description of the meaning of the represented knowledge. To overcome these limitations, the authors propose a technique based on the semantic approach, Hierarchical Semantic Form (HSF), that uses semantic contexts to implicitly define the meaning. This chapter first provides concise information about the two most popular techniques and their limitations, and then proposes a new technique based on semantic approach, which facilitates a large scale processing of sem...
INCOSE International Symposium, 2005
Recent emerging technologies such as internetworking and the World Wide Web have significantly expanded the types, availability, and the volume of data accessible to information management systems. In this new world, there is less emphasis on isolated devices and greater emphasis on information exchange. This shift is making knowledge management and application integration key issues in computer technology. In order to stay competitive, a company is held hostage by how it understands, maintains and accesses its data. Data comes from many sources, containing a variety of types and formats. One possible way to tackle this complex data problem is to implement an ontology, a unifying framework that facilitates communication through a shared understanding and vocabulary of a system. It enables communication between systems that are independent of system technologies, information architectures and application domain. This paper describes the importance of ontologies in knowledge management and provides the basis on how to architect such an ontological system.
… Review on Computers and Software (I …, 2007
The semantic web is an extension of the current web in which information is given well-defined meaning. It is a concept that enables better machine processing of information on the web, by structuring documents written for the web in such a way that they become understandable by computers. This can be used for creating complex applications such as intelligent browsers, intelligent software agents, global databases with data from the web, reuse of information, etc. Central to the vision of the semantic web are ontologies. Ontologies provide a shared understanding of a domain of interest to support communication among human and software agents, typically being represented in a machine-processable representation language. Web ontology languages like OWL provide a technological basis to enable the semantic web. This paper considers the basic principles of the semantic web, and reviews important tools for creating and maintaining ontologies in various frameworks.
This Ontologies are widely used as a means for solving the information heterogeneity problems on the web because of their capability to provide explicit meaning to the information. They become an efficient tool for knowledge representation in a structured manner. There is always more than one ontology for the same domain. Furthermore, there is no standard method for building ontologies, and there are many ontology building tools using different ontology languages. Because of these reasons, interoperability between the ontologies is very low. Current ontology tools mostly use functions to build, edit and inference the ontology. Methods for merging heterogeneous domain ontologies are not included in most tools. This paper presents ontology merging methodology for building a single global ontology from heterogeneous eXtensible Markup Language (XML) data sources to capture and maintain all the knowledge which XML data sources can contain.
SUMMARY One of the objectives of the h-TechSight project is the development of tools that help to build and dynamically maintain knowledge maps related to a specific knowledge-intensive domain. A first design of a dynamic ontology management system to be used for this purpose is proposed in this document.
2008
Ontology is a terminology which is used in artificial intelligence with different meanings. Ontology researching has an important role in computer science and practical applications, especially distributed knowledge systems. In this paper we present an ontology which is called Computational Object Knowledge Base Ontology. It has been used in designing some knowledge base systems for solving problems such as the system that supports studying knowledge and solving analytic geometry problems, the program for studying and solving problems in Plane Geometry, the knowledge system in linear algebra.
Lecture Notes in Computer Science, 2004
In this paper we present the use of ontology for knowledge representation and handling in Software Agent Systems. Motivation has come from Pellucid IST project where we need to capture and capitalize employee's knowledge in organization. This knowledge is then presented to other employees as they work on particular tasks. The Protg ontology editor and JADE multi-agent system is used for implementation. Ontology is usually used in intra-agent communication for agents to understand each other; we used ontology also as knowledge data model to store knowledge as instances of ontological terms into object database, thus agents can access and manipulate knowledge data directly and still stay lightweight.
International Journal of Human Computer Studies, 1995
The 2nd International Conference on Distributed Frameworks for Multimedia Applications, 2006
The Multi-Agent Systems (MAS) represent an environment for the design, development and deployment of Intelligent and Distributed Processing Systems focused on sophisticated applications, as the collaborative learning [1] and the auctions [2]. This paradigm aims for the encapsulation of specific functionalities in an intelligent and autonomous software component called Agent. So in order to support a cooperative platform among Agents is necessary to share the knowledge domain and to make easy the administration of the Ontologies repositories. This kind of role is carry out by an Ontology Agent, which receives demands of specific services from the Agents of the MAS, deals with them and responds with the appropriate message. Therefore in this work is depicted a methodology for building Ontology Agents that are encoded as Web Services to federate functionalities regarding to the administration of Ontologies through the Internet. The aim of the paper is to encourage the development of specialized Agents that integrate all the tasks for managing Ontologies, and that offer them to the Agents community.
To define what is the Semantic Web is very difficult as well as Web itself. “The Semantic Web is a mesh of information linked up in such a way as to be easily process able by machines, on a global scale.” “The Semantic Web approach develops languages for expressing information in a machine process able form. “Ontologies allow users to organize information into taxonomies of concepts. Each with their attributes, and describe relationships between concepts. More recently, the notion of ontology has also become widespread in fields such as intelligent information integration, cooperative information systems, information retrieval, electronic commerce and knowledge management.
Arxiv preprint arXiv: …, 2011
2006
In the past few years, several studies have emphasized the use of ontologies as an alternative to information organization. The notion of ontology has become popular in fields such as intelligent information integration, information retrieval on the Internet, and knowledge management. Different groups use different approaches to develop and verify de effectiveness of ontologies [1] [2] [3]. This diversity can be a factor that makes it difficult the formularization of formal methodologies of evaluation. This paper seeks to provide a way to identify the effectiveness of the knowledge representation based on ontology that was developed trough Knowledge Based System tools. The reason for that is because all processing and storage of gathered information and knowledge base organization is done using this structure. Our evaluation is based on case studies in the Ontoweb system [4], involving real world ontology for money laundry domain. Our results indicate that modification of ontology structure can effectively reveal faults, as long as they adversely affect the program state.
International Journal of Information Engineering and Electronic Business, 2020
The success of machine represented web known as semantic web largely hinges on ontologies. Ontology is a data modeling technique for structured data repository premised on collection of concepts with their semantic relationships and constraints on domain. There are existing methodologies to aid ontology development process. However, there is no single correct ontology design methodology. Therefore, this paper aims to present a review on existing ontology development approaches for different domains with the goal of identifying individual methodology's weakness and suggests for hybridization in order to strengthen ontology development in terms of its content and constructions correctness. The analysis and comparison of the review were carried out by considering these criteria but not limited to: activities of each method, the initial domain of the methodology, ontology created from scratch or reuse, frequently used ontology management tools based on literature, subject granularity, and usage across different platforms. This review based on the literature showed some approaches that exhibit the required principles of ontology engineering in tandem with software development principles. Nonetheless, the review still noted some gaps among the methodologies that when bridged or hybridized a better correctness of ontology development would be achieved in building intelligent system.
2005
The Web is an immense repository of data and knowledge. Semantic web technologies support semantic interoperability and machine-to-machine interaction. A significant role is played by ontologies which can support reasoning. Generally much emphasis is given to retrieval, while users tend to browse by association. If data are semantically annotated, an appropriate intelligent user agent aware of the mental model and interests of the user can support her/him in finding the desired information. The whole process must be supported by a core ontology.
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