Concepts, Ontologies, and Knowledge Representation

Jakus, Grega; Milutinović, Veljko; Omerović, Sanida; Tomažič, Sašo

doi:10.1007/978-1-4614-7822-5

Cited by 48 publications

(29 citation statements)

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“…As natural languages are often ambiguous, inconsistent, underspecified and difficult to model [17], they are not appropriate to represent knowledge.…”

Section: B Methods Of Sensor Knowledge Representationmentioning

confidence: 99%

Geospatial Sensor Network eLearning Collaboratory - A Portal for Sensor Knowledge Acquisition and Representation

Meng

Wang

2014

2014 IEEE 17th International Conference on Computational Science and Engineering

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Nowadays, sensors and sensor networks are being widely used both in the theoretical research and in the engineering application. In order to meet the standardization requirement, the OGC (Open Geospatial Consortium) SWE (Sensor Web Enablement) framework is extended to enable all types of sensors, instruments, and imaging devices to be accessible. However, the raw sensor data cannot represent a wealth of information especially semantic information, and cannot be easily recognized by computers either. With semantic web proposed, formal definitions are captured in ontologies, making it possible for computers to interpret and relate data content more effectively. In the paper, we review the methods of knowledge acquisition and representation, propose to adopt semantic web to acquire and represent sensor knowledge. We are designing and developing a web portal named GSNC (Geospatial Sensor Network eLearning Collaboratory) under the SWE framework. The GSNC application combines sensor data with semantics identified by human and machines, and makes the sensor knowledge acquisition and representation available. In addition, the GSNC project provides the web-based SensorML (Sensor Model Language) editor toolkit which can be migrated to other applications.

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“…As natural languages are often ambiguous, inconsistent, underspecified and difficult to model [17], they are not appropriate to represent knowledge.…”

Section: B Methods Of Sensor Knowledge Representationmentioning

confidence: 99%

Geospatial Sensor Network eLearning Collaboratory - A Portal for Sensor Knowledge Acquisition and Representation

Meng

Wang

2014

2014 IEEE 17th International Conference on Computational Science and Engineering

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“…The scientific meaning derives from Information Science, where 'ontology' refers to a shared conceptualisation of a domain, presented as an organised technical vocabulary for that domain [15]. The term 'ontology' is here meant to evoke the idea that the terms and their associated concepts are the building blocks of theories in the discipline or domain, and hence reflect the most basic units of thought about the subjects under study [16] (p. 9). This is different from, but not unrelated to, the use of this term in Philosophy.…”

Section: Scientific and Philosophical Uses Of The Term Ontologymentioning

confidence: 99%

“…As mentioned above, a key challenge for ontology development is to ensure that concepts are clear and their definitions are succinct without sacrificing their utility: after all, a "good" concept represents the smallest unit of knowledge carrying as much meaning as possible [16] (p. 9). One way to achieve concise definitions is to leverage a systems principle that originated in dialectics.…”

Section: Dialectical Feedbackmentioning

confidence: 99%

Systemic Semantics: A Systems Approach to Building Ontologies and Concept Maps

Rousseau

Billingham

Calvo‐Amodio

2018

Systems

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Abstract:The field of systemology does not yet have a standardised terminology; there are multiple glossaries and diverse perspectives even about the meanings of fundamental terms. This situation undermines researchers' and practitioners' ability to communicate clearly both within and outside their own specialist communities. Our perspective is that different vocabularies can in principle be reconciled by seeking more generalised definitions that reduce, in specialised contexts, to the nuanced meaning intended in those contexts. To this end, this paper lays the groundwork for a community effort to develop an 'Ontology of Systemology'. In particular we argue that the standard methods for ontology development can be enhanced by drawing on systems thinking principles, and show via four examples how these can be applied for both domain-specific and upper ontologies. We then use this insight to derive a systemic and systematic framework for selecting and organising the terminology of systemology. The outcome of this paper is therefore twofold: We show the value in applying a systems perspective to ontology development in any discipline, and we provide a starting outline for an Ontology of Systemology. We suggest that both outcomes could help to make systems concepts more accessible to other lines of inquiry.

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“…The various technologies followed for representing knowledge are fuzzy Petri nets [16], [17], hybrid representation, production rules, hierarchical representation, neural networks, semantic networks [3], logical representation, ontology [10], and knowledge webs.…”

Section: A Existing Knowledge Representation Modelsmentioning

confidence: 99%

Towards MORK: Model for Representing Knowledge

Kamala¹,

Selwyn²

2016

IJMECS

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Abstract-Smart world needs intelligent system for effective and timely decision making. This is achieved only through a knowledge based system with functional knowledge representation units. In this paper, two models are proposed for representing knowledge. This process involves in getting the data and placing the information in the correct location. Logical notations are used for taking the clauses and graph is used for putting the entities. In Model one, the data is translated into logical statements using predicate logics, later the knowledge is stored in conceptual graph and retrieved. Whereas in Model two, the given information is translated using First Order Logic (FOL), by applying description logic concept rules are defined and as a result reasoning is done. Storage is done by using concept-relation graph. The main aims of our models are to have easy and simple access over the information. These models return the required exact answer, for the higher order query posted by the end user to the intelligent system.

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Concepts, Ontologies, and Knowledge Representation

Cited by 48 publications

References 0 publications

Geospatial Sensor Network eLearning Collaboratory - A Portal for Sensor Knowledge Acquisition and Representation

Geospatial Sensor Network eLearning Collaboratory - A Portal for Sensor Knowledge Acquisition and Representation

Systemic Semantics: A Systems Approach to Building Ontologies and Concept Maps

Towards MORK: Model for Representing Knowledge

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