Knowledge representation with ontologies: the present and future

Brewster, Christopher; O’Hara, Kieron

doi:10.1109/mis.2004.1265889

Cited by 143 publications

(73 citation statements)

References 16 publications

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“…Knowledge has been represented using network, graphs, and finite automata and using concept maps. According to Christopher Brewster (2004) many knowledge based representations involve use of Ontology. Ontology finds its origin from the field of philosophy whereas its implication in the field of computer science is stated as "ontology is formal, explicit specification of a shared conceptualization" (Thomas R. Gruber, 1993).…”

Section: Research Backgroundmentioning

confidence: 99%

Correlating and Cross-linking Knowledge Threads in Informledge System for Creating New Knowledge

Nair¹,

Malhotra²

2012

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

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Abstract:There has been a considerable advance in computing, to mimic the way in which the brain tries to comprehend and structure the information to retrieve meaningful knowledge. It is identified that neuronal entities hold whole of the knowledge that the species makes use of. We intended to develop a modified knowledge based system, termed as Informledge System (ILS) with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. We conceive that every piece of knowledge is a cluster of cross-linked and correlated structure. In this paper, we put forward the theory of the nodes depicting concepts, referred as Entity Concept State which in turn is dealt with Concept State Diagrams (CSD). This theory is based on an abstract framework provided by the concepts. The framework represents the ILS as the weighted graph where the weights attached with the linked nodes help in knowledge retrieval by providing the direction of connectivity of autonomous nodes present in knowledge thread traversal. Here for the first time in the process of developing Informledge, we apply tenor computation for creating intelligent combinatorial knowledge with cross mutation to create fresh knowledge which looks to be the fundamentals of a typical thought process.

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Section: Research Backgroundmentioning

confidence: 99%

Correlating and Cross-linking Knowledge Threads in Informledge System for Creating New Knowledge

Nair¹,

Malhotra²

2012

Proceedings of the International Conference on Knowledge Engineering and Ontology Development

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“…A comprehensive description of KR can be found in [1]; a discussion of relationships between KR and Ontologies, in [2]. The major paradigms in KR are as follows.…”

Section: Overview Of Knowledge Representationmentioning

confidence: 99%

“…Several paradigms have been used for KR, such as semantic networks, frames, production rules, and predicate logic [1,2]. In particular, Conceptual Graphs (CG) [3] have been used for KR because of its logic formalism.…”

Section: Introductionmentioning

confidence: 99%

Pre-conceptual Schema: A Conceptual-Graph-Like Knowledge Representation for Requirements Elicitation

Jaramillo

Gelbukh

Isaza

2006

Lecture Notes in Computer Science

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Abstract. A simple representation framework for ontological knowledge with dynamic and deontic characteristics is presented. It represents structural relationships (is-a, part/whole), dynamic relationships (actions such as register, pay, etc.), and conditional relationships (if-then-else). As a case study, we apply our representation language to the task of requirements elicitation in software engineering. We show how our pre-conceptual schemas can be obtained from controlled natural language discourse and how these diagrams can be then converted into standard UML diagrams. Thus our representation framework is shown to be a useful intermediate step for obtaining UML diagrams from natural language discourse.

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“…Ontologies are a key component of the semantic web. Although rich ontologies support data exchange or reasoning [8], query expansion and complex visualization, we are interested in a pragmatic interpretation of website structure or classification schemes as ontologies [9] to support simple visualization .…”

Section: Related Workmentioning

confidence: 99%

“…4. The proxy cross-references the results with the site ontology (7) and the results with their associated categories (8) are stored (9). 5.…”

Section: Fig 1 a Logical View Of The Naviguidance Architecturementioning

confidence: 99%

Using Existing Website Ontologies to Assist Navigation and Exploration

Saward

Shah

Hall

2005

Advances in Web Intelligence

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Knowledge representation with ontologies: the present and future

Cited by 143 publications

References 16 publications

Correlating and Cross-linking Knowledge Threads in Informledge System for Creating New Knowledge

Correlating and Cross-linking Knowledge Threads in Informledge System for Creating New Knowledge

Pre-conceptual Schema: A Conceptual-Graph-Like Knowledge Representation for Requirements Elicitation

Using Existing Website Ontologies to Assist Navigation and Exploration

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